Tencon2025 Technical Program

Tuesday, October 28 8:00 - 9:30

Track A1F1 CCI 1.1: Computing and Computational Intelligence (CCI) 1.1

Room: F1. Sipadan I

Chair: Khairil Anuar (Multimedia Universiti, Malaysia)

8:00 Benign Versus Malignant Classification of Lesions on Intraoral Fluorescent Images Obtained Using Blue Light Irradiation

Asahi Takahashi, Keishu Sano, Shigeo Ishikawa and Tadanori Fukami (Yamagata University, Japan)

In recent years, a technique for detecting diseases by irradiating the oral mucosa with blue light has been attracting attention. Under blue light irradiation, diseased tissues can be observed as a dark color because of a decrease in flavin adenine dinucleotide and destruction of collagen cross-linking structure in the diseased tissues, while blue-green fluorescence is observed in healthy tissues. However, the determination of tumor as benign or malignant (cancer) is still dependent on the knowledge and experience of the oncologist. In this study, we attempted to globally detect lesion regions using Mask R-CNN, an instance segmentation method, and to then locally classify the region into three classes (healthy tissue, benign tumor, cancer) using SVM (support vector machine) and Resnet (residual neural network). The results showed an accuracy of 70.7%. There were no cases in which cancer was classified as healthy tissue, but 27.0% of cases were misclassified as benign tumors.

8:15 Nrithya: a Curated Image Dataset of Indian Folk and South Indian Classical Dance Forms

Malavika V, Karthika Subbaraj and Srinivasan R (Sri Sivasubramaniya Nadar College of Engineering, India)

This study presents two carefully curated image datasets capturing the rich visual diversity of Indian folk and classical dance forms. The first dataset focuses on four South Indian classical dance styles - Bharatanatyam, Kathakali, Kuchipudi and Mohiniyattam - initially comprising 410 images, expanded to 1,621 through image augmentation. The second dataset encompasses six Indian folk dances - Bhavai, Garba, Karakattam, Malwai Giddha, Toda and Yakshagana - growing from 600 to 2,368 images after augmentation. All images were sourced from publicly available online platforms and YouTube videos, ensuring diversity in performers, regional styles, costumes and poses. Preprocessing techniques such as background removal and Canny edge detection were applied for costume-based feature extraction, and their effectiveness was systematically evaluated. This work addresses a critical gap in the availability of ethnically representative dance datasets by providing curated collections to support a range of computer vision tasks, including posture recognition, dance form classification, and cultural analytics - thereby contributing to both cultural heritage preservation and AI-driven dance research.

8:30 Comparative Analysis of Transformer-Based Models for Bail Prediction Using HLDC Dataset

Karthika Subbaraj, Aishwarya R and Nirranjana R (Sri Sivasubramaniya Nadar College of Engineering, India); Vishranth Msk (Vellore Institute of Technology, India)

This research presents a transformer-based approach to predict bail outcome using the Hindi Legal Documents Corpus (HLDC), a large-scale dataset consisting of legal texts from Indian district courts. The work employs a TFIDF-based extractive summarization technique to extract key factual information, enhancing relevance of input and shortening sequence length. Four transformer models-MuRIL, DeBERTa, DistilBERT, and mBERT-are fine-tuned and evaluated within a rigorously optimized experimental framework. Each model was trained for a binary classification task, to predict whether bail should be 'granted' or 'denied'. Experimental results show that MuRIL exhibits the highest performance with 84% accuracy, an AUC of 0.89, and an average precision of 0.94, which attributes to MuRIL's strong alignment with Indian language semantics and its synergy with the TF-IDF-based sentence ranking strategy. These findings validate the importance of linguistic alignment and input relevance in high-stakes legal NLP tasks. The proposed approach offers a scalable and interpretable framework for decision support in India's judicial context.

8:45 Scalable Face Recognition with Multiple Deep Learning Model Integration

Pawan Kamalkishor Ajmera, Ramakant Soni, Anmol Mathur, Jayesh Harlalka and Vipin Saraogi (BITS Pilani, India)

This paper presents the design of a scalable face recognition system to address real-world challenges including illumination-variation, occlusions and motion blur. This proposed method integrates multiple state-of-the-art models into a unified pipeline: YOLOv5-Face is used for preparation of the dataset, SCRFD for face detection with high accuracy and face alignment, ArcFace for discriminative feature embedding generation and BYTETracker for continuous multi-face tracking. Experimentations conducted on a locally generated dataset demonstrate strong performance by achieving the training accuracy of 90.13%, validation accuracy of 89.02%, and a ROC AUC score of 0.9592. The system is able to sustain real-time performance with 25-30 FPS and low latency of 33-40 ms per frame. These results make this system suitable for large-scale surveillance applications. The results also highlight the effectiveness of multi-model integration in improving the accuracy and adaptability when compared to traditional approaches. This paper contributes a practical solution for deployment in the domains such as campus surveillance, access control and crowd monitoring. It also provides foundation for future work which include liveness detection, super-resolution for low resolution recognition and lightweight edge-compatible model implementation.

9:00 Comparing Deep Learning Approaches for Low Resolution Face Recognition Challenges in Security and Surveillance Applications

Ramakant Soni and Pawan Kamalkishor Ajmera (BITS Pilani, India)

Facial recognition in the low-resolution environments remains as a critical challenge for security and surveillance systems. This paper comprehensively evaluates two advanced deep learning models: MobileNetV2 and EfficientNet-B0 across four facial datasets: LFW, LFW Funneled, TinyFace and a locally collected low-resolution dataset (Local LR). Using PyTorch with extensive data augmentation, model's performance was assessed on metrics: Accuracy, Loss, F1-score and ROC AUC. The experimental results shows that EfficientNet-B0 achieves higher accuracy on challenging low-resolution datasets like TinyFace (69.64% compared to MobileNetV2's 61.86%). EfficientNet-B0 shows 29.3% reduction in test loss and 6.4% higher F1-score maintaining comparable performance on higher-resolution datasets (97.81% on Local LR). MobileNetV2 shows more computational advantages with fewer parameters and lower FLOPs in comparison to EfficientNet-B0. This makes it more suitable for edge deployments despite slightly lower accuracy (96.43%) on LFW compared to EfficientNet-B0 (96.97%). These findings provide quantitative benchmarks for model selection. Results are highlighting EfficientNet-B0's strength in feature extraction from low-quality images and reflects the efficiency benefits of MobileNetV2 which is offering practical guidance for implementing facial recognition systems in real-world scenarios where image resolution and hardware constraints vary significantly.

9:15 Data-Driven Approach for Dynamic Comparator in Analog-to-Digital Conversion

Buddhi Prakash Sharma, Anu Gupta and Chandra Shekhar (BITS Pilani, India)

The performance of dynamic comparators significantly influences the efficiency, speed, and accuracy of Analog-to-Digital Converters (ADCs), especially in modern low-power and high-speed applications. Traditional design methodologies relying on exhaustive simulations and manual tuning are inefficient and fail to adapt to process, voltage, and temperature (PVT) variations. This paper proposes a comprehensive data-driven optimization framework that leverages machine learning (ML) and artificial intelligence (AI) to enhance dynamic comparator performance across key metrics. The framework introduces a four-stage pipeline comprising feature extraction from simulation and measurement data, supervised learning for offset voltage prediction, reinforcement learning (RL) for adaptive calibration, and Bayesian optimization for automated sizing of circuit components. These methods collectively reduce design complexity, improve accuracy and power efficiency, and enable real-time adaptability. While integration with Electronic Design Automation (EDA) tools remains a future direction, the proposed framework lays the foundation for scalable, intelligent comparator design targeting applications such as IoT, biomedical systems, and high-speed communications.

Tuesday, October 28 8:00 - 9:30

Track A1F2 CSR 1: Control Systems & Robotics (CSR) 1

Room: F2. 501 Kadamaian

Chair: Fatanah Mohamad Suhaimi (Universiti Sains Malaysia, Malaysia)

8:00 Data Driven Approach for Rapid Collision Checking Using Cross-Attention on Robot and Obstacle Sets

Chayan Maiti and Deep Patel (IIITDM Kancheepuram, India); Sreekumar Muthuswamy (IIITD&M Kancheepuram, India)

Collision checking is the process of determining whether a candidate robot configuration or trajectory intersects any obstacles in the workspace, and it underpins all safe motion-planning algorithms. Reliable real-time collision checking is critical for safe, autonomous robot manipulation in dynamic industrial settings. This paper presents a learning-based collision checking framework that leverages Set Transformers with cross-attention mechanisms to rapidly and accurately predict collisions between robotic manipulators and complex, variable obstacle environments. Unlike traditional geometric approaches, the proposed method efficiently processes a robot's configuration and a dynamic obstacle set to estimate collision likelihood in real-time. A synthetic dataset generated via CoppeliaSim for three industrial robot models (UR5, UR10, ABB IRB140) is used for training and evaluation. Experimental results demonstrate an average classification accuracy of 89.6%, with the model significantly outperforming established learning-based methods such as Fastron and CollisionGP, while achieving up to 7.6× faster inference than PyBullet-based checks. The proposed approach offers a scalable, generalizable, and computationally efficient solution for safe robot operation in dynamic and unstructured environments.

8:15 Modified INN Control Law for Efficient Flow Regulation with Pneumatic Control Valve

Manmahendra Singh Daksh and Puneet Mishra (Birla Institute of Technology and Science, Pilani, India)

In the industrial process plant, the control valve is the key component in regulating the flow of fluid that is done by the controller. Hence, the controller is crucial in improving the plant's overall efficiency. This paper proposes a new modified Inverse Neural Network (INN) control law that provides better setpoint tracking and disturbance rejection performance compared to the existing INN control law while being tested on a laboratory scale flow process to regulate fluid flow across the pneumatic control valve. In this proposed work, the data for process variables and controller outputs are taken for different set points, and then the identification of system parameters is done with the help of the Particle Swarm Optimization (PSO) technique. The identified parameters are used as initial parameters, while the updated INN control and existing INN control for the plant are tested for optimized control performance. In this process of hardware in the loop, the laboratory scale flow process is interfaced with the help of a DAQ USB device, NI USB-6001, and the flow across the valve gets controlled in real-time. In the case of the proposed modified INN controller (MOD-INN), the plant's actual output tracks the set point precisely with lower Integral Absolute Error (IAE) compared to existing INN control to regulate fluid flow across the control valve for different setpoints, as well as in the case of an introduced disturbance in the setpoint. Quantitatively, in terms of IAE, a 13.44 times improvement was achieved by MOD-INN over the existing INN controller under servo performance for a 1.2V value of setpoint, while a 10.94 times improvement was observed under regulatory performance.

8:30 Advanced 3D Path Planning for Robotic Calligraphy Based on LLM-Driven Text Prompts

Cheuk Tung Shadow Yiu and Dick Ho Cheung (The Hong Kong University of Science and Technology, Hong Kong); Haolun Huang (HKUST, Hong Kong); Kam-Tim Woo (Hong Kong University of Science and Technology, Hong Kong)

Chinese calligraphy is a complicated art form that requires precise control of movement and brush dynamics, making it a challenging task for robotic systems. This paper presents a novel approach to enabling robotic manipulators to perform Chinese calligraphy using 3D path planning algorithms and large language model (LLM) as input generators. The proposed framework integrates an LLM to generate textual content prompts, which are then transformed into detailed 3D Bézier curve trajectories which represent the strokes of Chinese characters. These trajectories are optimized to ensure smooth and accurate motion of the robotic manipulator, taking into account the brush's pressure, orientation, and speed to emulate traditional calligraphy techniques. Experimental results demonstrate the effectiveness of the approach in producing visually authentic Chinese calligraphy with high precision and artistic quality. This work highlights the potential of combining LLM with advanced robotic path planning to bridge the gap between traditional art and robot world.

8:45 LiDAR-Based Cooperative Tracking Using IMM Extended Object Tracking and Information Filter

Yoshihiro Nakatani, Shuzo Uchiyama, Masafumi Hashimoto and Kazuhiko Takahashi (Doshisha University, Japan)

This paper presents a cooperative method for tracking objects, such as cars, two-wheelers, and pedestrians, using multiple light detection and ranging sensors (LiDARs) deployed in a road environment. Each LiDAR system independently estimates the motion and shape of objects, e.g., in terms of their positions and velocities, and sizes, within its field of view. A central server collects and fuses these estimates from LiDAR systems. The proposed approach uses an extended object-tracking method based on the multiplicative error model-extended Kalman filter (MEM-EKF) framework combined with an interacting multiple model estimator to accurately track the motions and shapes of objects exhibiting various movement patterns, such as stopping, constant velocity motion, and suddenly moving and stopping. In addition, information filter-based fusion is performed at the central server. Simulation results using two LiDAR systems in an intersection environment demonstrate the effectiveness of the proposed method compared to the conventional single-model MEM-EKF method and standalone tracking method with a single LiDAR system.

9:00 An Improved Auction Algorithm for Multi-Robot Task Allocation

Kuang Jui Lim, Shalini Darmaraju and Choon-Hian Goh (Universiti Tunku Abdul Rahman, Malaysia)

The problem of task allocation in multi-robot systems (MRTA) is critical for optimizing performance in various applications, such as warehouse automation and search and rescue operations. Current algorithms often fail to consider real-life complexities like robot dynamics and energy consumption, leading to inefficiencies. This study aims to address these gaps by proposing improved versions of the auction algorithm (A) and the enhanced auction algorithm (EA), focusing on optimizing task allocation by considering task complexity and robot capabilities. The methodology involves using auction and extended auction algorithms as the backbone, with two improved algorithms proposed to enhance fairness and efficiency. An energy handling function is also incorporated. Six scenarios were simulated, involving 10, 15, and 25 robots with 100 and 200 tasks, respectively. The mean and standard deviation of completion times were computed to evaluate performance. Results indicate that the improved auction (IA) algorithm consistently outperforms the auction (A) algorithm, while the improved enhanced auction (IEA) algorithm generally performs better than the enhanced auction (EA) algorithm. For instance, in scenarios with 25 robots and 200 tasks, the standard deviation of A, IA, EA, and IEA is 62.7s, 51.7s, 21.3s and 20.5s, respectively. A lower standard deviation indicates a better distribution. Additionally, the energy handling function significantly improved task allocation efficiency. In conclusion, our improved algorithms demonstrate better performance with the added energy handling function enhancing practical applicability. Future work should address real-life robot dynamics and communication delays to further improve the system's efficiency and effectiveness.

Tuesday, October 28 8:00 - 9:30

Track A1F3 PES 1: Power, Energy & Electrical Systems (PES) 1

Room: F3. 502 Mesilau

Chair: Madihah Md Rasid (Universiti Teknologi Malaysia, Malaysia)

8:00 Optimal Integration of Solar-Powered EV Charging Stations in Radial Distribution System

Raj Chakraborty and Punam Das (National Institute of Technology Agartala, India); Sadhan Gope (NIT Agartala, India); Diptanu Das and Priyanath Das (National Institute of Technology Agartala, India)

Electric Vehicles (EVs) are critical to sustainable transportation systems owing to their energy efficiency and low environmental impact. However, their unpredictable charging patterns introduce stochastic loads into Radial Distribution System (RDS), leading to power quality degradation and elevated grid losses. This study presents a strategic methodology for deploying EV Charging Stations (EVCS) integrated with Photovoltaic (PV) units in RDS, aiming to minimize Active Power Loss (APL) and maintain a stable voltage profile. To determine the role of PV systems in enhancing network efficiency, the network parameters were analyzed under both solar-augmented and conventional charging configurations. Additionally, the PV system capacity was optimized to align with grid requirements. The proposed framework was tested on the IEEE 69-bus RDS, partitioned into four zones, each hosting one EVCS and one PV unit to balance charging accessibility with network efficiency. A Backward/Forward Sweep Power Flow (BFSPF) analysis was conducted to account for dynamic electrical parameters. The placement problem was formulated as an optimization problem and solved using Symbiotic Organisms Search (SOS) algorithm which demonstrated a reduction in APL of approximately 6.20% upon integrating PV units with EVCS.

8:15 Comparison of Deep Learning and Statistical Methods for EV Charging Demand Forecasting

Maizatul Shafiqah Sharul Anuar (Universiti Teknologi PETRONAS, Malaysia); Illani Mohd Nawi and Saeed S. Ba Hashwan (Universiti Teknologi Petronas, Malaysia)

The growing adoption of electric vehicles (EVs) presents challenges for maintaining power grid stability and managing energy resources efficiently. Accurate forecasting of EV charging demand is crucial for optimizing charging infrastructure and ensuring grid reliability. This study evaluates two time series forecasting models: the Long Short-Term Memory (LSTM) network and the Autoregressive Integrated Moving Average (ARIMA) model. The LSTM model, based on deep learning, captures complex, time-dependent, and nonlinear patterns in EV charging data, whereas ARIMA, a statistical approach, models linear trends and seasonality. Both models were trained on 90% of the dataset and tested on the remaining 10%, with performance evaluated over 30-, 60-, and 90-day forecasting horizons using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The results show that the LSTM model achieved significantly higher forecasting accuracy than ARIMA. These findings demonstrate the potential of deep learning approaches to capture complex, nonlinear patterns in EV charging behavior, providing a more reliable framework for demand forecasting in smart grid applications.

8:30 Minimizing Operation Cost of Fast Charging Station with Post-FIT PV by EV Charging Preference and Electric Market Price's Scenarios

Kento Imai and Shigeru Tamura (Meiji University, Japan)

Electric vehicles are rapidly penetrating and the number of their fast charging stations is also rising. Regarding the revenue of fast charging station, photovoltaic generation and battery energy storage supplying electricity, and utilizing power market effectively are necessary. There has been growing number of post-feed-in-tariff photovoltaic generation and it can be bought at a cheaper price in Japan. However, the revenue of fast charging station includes uncertainties such as electric vehicle's charging demand, photovoltaic generation, and power market price. The uncertain charging demand is acquired based on surveys of EV charging preference and traffic census data in this study. It also deals with the other uncertainties by using a clustering method, X-means, to generate the scenarios used for stochastic programming. The study aims to find the optimal capacity of battery and amount of post-feed-in-tariff photovoltaic generation to be bought under such uncertainties. The various prices of post-feed-in-tariff photovoltaic generation affecting the revenue are also studied.

8:45 Enhancing Thermal Imaging Accuracy for Power System Maintenance

Hasanka Gayan Somaweera, Gethwan Suriyawansa, Rusira Thispura Thilakarathna, Charithri Yapa and Nimantha Madhushan (University of Sri Jayewardenepura, Sri Lanka); Hasith Kapilapadma Liyanage (UUniversity of Peradeniya, Sri Lanka)

Accurate temperature measurement of electrical components is crucial for the health monitoring of electrical components for periodic maintenance. Traditional thermal cameras can apply a single emissivity value across the entire image. This gives an inaccurate reading due to different kinds of materials and factors like elevation from sea level, environmental temperature, and relative humidity. This makes analyzing a large number of thermal images ineffective. This research presents a novel method for machine learning-based object identification by practically applying formulas to increase accuracy considering all the factors. The proposed method can dynamically adjust the emissivity values based on the characteristics on the materials and the environmental conditions. The results show a significant improvement in the temperature of each component. This can improve better monitoring of components health as well as optimize the maintenance schedules. This leads to increase the reliability and efficiency of the electrical systems for periodic maintenance in the electrical industry.

9:00 Deep Learning-Based Resident Change Detection Using Electricity Metering Data

Jongin Kim, Dayeon Lee and Dong Sik Kim (Hankuk University of Foreign Studies, Korea (South)); Beom Jin Chung (Seoul National University of Science & Technology, Korea (South)); Young Mo Chung (Hansung University, Korea (South))

As smart meter technology advances, the advanced metering infrastructure (AMI) enables real-time monitoring of electricity usage at 15-minute intervals. Since prior studies mainly focused on industrial infrastructures, anomaly detection in residential electricity usage remains underexplored. In this paper, we propose a gated recurrent unit (GRU)-based deep learning model to detect resident changes in residential households using the electricity metering data through AMI. Monthly consumption patterns and energy consumption are predicted using actual electric energy metering data collected from 846 apartment households in Seoul, Korea. To detect the resident changes, combined losses are computed by integrating the consumption pattern and consumption energy prediction errors. The experimental results show an accuracy of 82.55%, specificity of 100%, and recall of 65.11% when allowing a +-1-month error margin. The proposed deep leaning model demonstrates the practical feasibility of detecting resident changes in residential households and is expected to contribute to the development of smart grid services.

9:15 Recent Evolution of Intelligent Approach in Electric Motor Drive System for EV Applications: a Concise Review

Syazmie Sepeeh (MAHSA University, Malaysia); Shamsul Aizam Zulkifli (UTHM, Malaysia & Universiti Tun Hussein Onn Malaysia, Malaysia); Huang-Jen Chiu (National Taiwan University of Science and Technology, Taiwan); Hanis Farhah Jamahori (Universiti Teknologi PETRONAS, Malaysia)

The fast development of electric vehicles (EVs) has motivated major studies on smart management techniques for electric motors, which are essential for guaranteeing optimal performance, efficiency, and dependability. This brief paper offers a summary of recent advances in artificial intelligence (AI)-based techniques used in electric motor management inside EV applications. The conversation covers several artificial intelligence models, including fuzzy logic (FL) systems, evolutionary algorithms (EA), machine learning (ML), and deep learning (DL). Every approach is quickly examined in terms of its working principles, benefits, drawbacks, and appropriateness for particular motor control duties, including speed control, torque optimisation, defect identification, and adaptive control. Particular focus is on how AI methods combine with motor types often used in EVs, such as permanent magnet synchronous motor (PMSM) and induction motor (IM). The paper highlights current trends and research opportunities to provide researchers and practitioners a modest but instructive reference for enhancing intelligent motor control in modern EV systems.

Tuesday, October 28 8:00 - 9:30

Track A1F4 ECD 1: Electronics, Circuits & Devices (ECD) 1

Room: F4. 503 Dinawan

Chair: Maizatul Zolkapli (Universiti Teknologi MARA, Malaysia)

8:00 TCAD Study of SEE-Induced Reliability Analysis of PDSOI FinFET Based Capacitorless 1T DRAM

Mitali Rathi (NIT RAIPUR, India); Guru Prasad Mishra (NIT Raipur, India)

A comprehensive TCAD study of Single event effect on Capacitorless 1T-DRAM based on Partially depleted silicon on insulator (PDSOI) FinFET is demonstrated in this work. The device is well calibrated through 3D simulations using SILVACO ATLAS TCAD tool. First the memory operation and device characteristics is studied. The program operation is done by using Impact Ionization (II) method. Further the impact of high energy particle (HEP) strike on the Capacitorless 1T- DRAM operations such as Write-Hold-Read is carried out. The effect of various single event upset parameter such as HEP strike time, linear energy transfer (LET) value, HEP strike position are also evaluated. It is observed that as the device is scaled, it is highly susceptible to Single Event Effects (SEEs), even the drain and source regions are highly sensitive to SEEs. Hence, there is bit flip occurring in the device, and the read operation is corrupted, leading to malfunction of the DRAM memory.

8:15 Analysis of Dielectric Modulated Gate-All-Around (GAA) Junction-Less Transistor Based Biosensor

Achinta Baidya (Mizoram University, India); Vishal Kumar Pandey, Aditya Abhiraj and Kartik Swamy (Electronics and Communication Engineering, Mizoram University, India); Lalthanpuii Khiangte (National Institute of Technology Mizoram, India)

This work investigates a junction-less transistor (JLT) with gate all around (GAA) structure and dual cavity for the biomolecule sensing application. The GAA - JLT structure design with cavity exhibits good subthreshold swing and drain induced barrier lowering. The nanocavity is so placed below the gate that dielectric change due to the presence of biomolecule modulates the device's electrostatic characteristics. GAA-JLT have shown a significant variation in respect of threshold voltage, on-current, off current, ION/IOFF ratio due to the change in biomolecule in cavity. Due to immobilized higher dielectric biomolecules in cavity increase in gate capacitance occurs which results in increase in drain current, positive shift of threshold voltage, decrease of IOFF current. The device's sensitivity can be understood from the fluctuations of these parameters. The proposed structure with two 10 nm × 3 nm nanocavity has shown significant bio-sensing capability for detecting the biomolecules. All sensitivities of the biosensor are evaluated for biomolecules with dielectric constants (k) ranging from 1.6 to 12. Maximum ION/IOFF ratio achieved is 9.42× 10 9, Sensitivity (S(I on /I off )) ranges from 1.08 to 1357.6.

8:30 Design and Simulation of Multibeam Cylindrical EIK Cavities for a Ka-Band Klystron

Soumaya Mandal (NIT Mizoram, India); Santigopal Maity (NATIONAL INSTITUTE OF TECHNOLOGY MIZORAM, India); Chaitali Koley (National Institute of Technology, Mizoram, India)

This study reports on the computer-aided design and simulation of intermediate and input/output cavities configured in 2-pi Mode for a 4-beam Ka band klystron. Coaxial type extended interaction cavities have been investigated to achieve higher R/Q value. A 3-gap input/output cavity, in which, the central conductor was optimized for symmetric field distribution and uniform transmission coefficients, has been proposed for this multiple beam klystron (MBK). The coaxial cavity configuration with higher order mode allows usage of larger size cavities for the ease in fabrication complexity along with the stability compared to square shaped cavities. Here an R/Q has been achieved around 166 for intermediate cavity and 116 for I/O cavities. The Qext also has been analyzed with the variation of coupling slots. The proposed MBK has potential applications in high data rate millimeter-wave communication systems, satellite links, and 5G wireless backhaul networks, for which Ka-band frequencies are gaining increasing prominence.

8:45 An Advanced Electrothermal Small-Signal Compact Modeling of 250 nm GaN HEMTs Using ASM Methodology

Vijay Maitra, Ellapu Bhanu Prakash and Anil Prasad Dadi (National Institute of Technology Mizoram, India); Ashok Ray (North Eastern Regional Institute of Science and Technology, India); Sushanta Bordoloi (National Institute of Technology Mizoram, India)

Small-signal modeling of Gallium Nitride (GaN)-based High Electron Mobility Transistors (HEMTs) requires accurate self-heating and charge trapping representation. A small-signal model is proposed which simplifies yet accurately represents drain-lag behavior, improves the extraction flow and simulation precision of the physics-based compact model for the 250 nm device. To incorporate the thermal effects in the proposed small-signal model, the conventional ASM-HEMT model is referred and modified suitable. The model includes RSUB = 20 Ω and CSUB = 100 fF, along with a thermal network defined by RTH and CTH to capture transient thermal behavior. Pulsed I-V measurements and temperature-dependent characterization are used to validate the proposed model. Pulsed I-V measurements show peak IDS values of 230.45 mA at VDSQ = 8 V, 228.15 mA at 15 V, and 213.44 mA at 28 V as compared to the conventional ASM-HEMT reference model. The investigation is carried out in a calibrated Cadence Virtuoso simulation environment.

9:00 Simulation and Optimization of Top-Gated Graphene-Based Field Effect Transistor for Enhanced Biosensing Applications

Paramasudhen Ravendran (Asia Pacific University of Technology & Innovation (APU), Malaysia); Hemah Selvarajan (Universiti Putra Malaysia (UPM), Malaysia); P. Susthitha Menon (Universiti Kebangsaan Malaysia, Malaysia & Institute of Microengineering and Nanoelectronics (IMEN), Malaysia); Reena Sri Selvarajan (Asia Pacific University of Technology & Innovation (APU), Malaysia)

As nanotechnology and miniaturization lead the bioengineering domain, the quest to optimize the devices for biosensing applications remained as a gap to be fulfilled. Particularly, graphene-based field-effect transistors offer various advantages, and this has enticed researchers from all over the world to produce impactful research works for the past two decades. This work reports on the top-gated graphene-based field effect transistor (TG-GFET) device simulation and optimization that primarily focus on the key criteria such as electrode thickness, channel lengths, and electrode material to further enhance its practical implementation in biosensing applications. Optimized parameters obtained from the simulation work were verified with the theoretical concept prior to reporting in this work.

9:15 PWM and Differential PWM Generation Circuits Based on Conventional Schmitt Trigger Cooperating with Integrator: Analysis and Implementation

Sukkharak Saechia (Phranakhon Si Ayutthaya Rajabhat University, Thailand); Chanapat Kaew-in (School of Engineering, King Mongkut's Institute of Technology Ladkrabang, Thailand); Paramote Wardkein (King Mongkut's Institute of Technology Ladkrabang, Thailand); Nisukan Chatchaiphat (School of Engineering, King Mongkut's Institute of Technology Ladkrabang, Thailand)

This paper presents systematic approaches to generate the Pulse Width Modulation (PWM) signal by leveraging a simple configuration based on an operational amplifier (op-amp) comprising the Schmitt Trigger circuit and the integrator circuit. Two PWM modulation techniques are proposed according to the input position of the information signal. First, utilizing inverting terminal of the Schmitt Trigger circuit as an input terminal can function as the PWM modulator. The resulting PWM output exhibits its duty cycle proportional to the derivative of the input signal. Second, when input signal is applied to the non-inverting terminal of the integrator circuit, the generated PWM signal whose duty cycle is proportional to both input and its integral. To ensure that the generated PWM signal conveys the message correctly, PWM demodulator is used to validated and confirm the results. Therefore, both proposed configurations effectively function as PWM modulator for both information signal and its integral. Finally, the results of in-depth mathematical analysis are validated and align well with all experimental results.

Tuesday, October 28 8:00 - 9:30

Track A1F5 CS 1: Communication Systems (CS) 1

Room: F5. 504 Madai

Chair: Chee Yen Leow (Universiti Teknologi Malaysia, Malaysia)

8:00 Adaptive Deep Reinforcement Learning for Coordinated Autonomous Vehicle Platooning on Edge Networks

Nischal Maheshwari, Vaishvi Shah, Nitya Balar, Dhruv Luniya, Vasukumar Parsaniya, Bhabani Prasad Mishra and Nitin Singh Rajput (Pandit Deendayal Energy University, Gandhinagar, India); Santosh Kumar Satapathy (Pandit Deendayal Energy University, India & Assistant Professor, India)

Vehicle platooning is a promising application for achieving greener, faster, and more economical transportation. In a platoon, vehicles operate in a coordinated convoy, where the lead vehicle is responsible for navigation and driving instructions, which are followed by the trailing vehicles. However, the operation of such a system requires not only intra-platoon communication but also reliable connectivity with infrastructure and cloud services, which are often located at distant locations. Ensuring seamless and uninterrupted communication in this setting poses a significant challenge. To address this, we propose an adaptive multi-agent deep reinforcement learning framework for enhancing communication reliability in vehicle platoons, particularly within edge-assisted networks. In the proposed model, each vehicle functions as an intelligent agent that learns an optimal policy for selecting communication channels that ensure reliable data transmission, both within the platoon and between the platoon and infrastructure nodes. The framework is built on deep Q-learning with experience replay, enabling agents to learn robust strategies over time. The simulation results demonstrate that the proposed model effectively learns optimal policies and maintains high performance even under dynamic and challenging network conditions.

8:15 Exploration of Spatial Diversity in Wi-Fi CSI Crowd Counting for LOS and NLOS Environments

Juliane G Cudal, Charles S Dela Cruz, Nicolas John P Ellovido, Justin Andrei J Fajarda, Kyle Dominic R Reantoquio and Josyl Mariela Rocamora Reyes (University of Santo Tomas, Philippines)

Conventional crowd counting (CC) systems employ cameras, LIDAR, or environmental sensors to detect the number of people in a target environment. However, these systems require line-of-sight (LOS) to recognize and estimate the crowd effectively. An alternative sensing method uses Wi-Fi channel state information (CSI) derived from the received signals. Despite Wi-Fi's capability to work in non-line-of-sight (NLOS) conditions, recent Wi-Fi CSI CC systems focus mostly on LOS scenarios, with some systems utilizing multiple pairs of TX-RX devices to improve the classification performance. Therefore, this study investigates LOS and NLOS scenarios for Wi-Fi CSI CC systems utilizing single- or multi-pair devices. We gathered diverse datasets and applied two preprocessing techniques: discrete wavelet transform (DWT) for noise filtering and principal component analysis (PCA) for dimension reduction. These datasets were utilized in training machine learning models to recognize three classes: 0, 1, and 3 persons. Results show that adding another pair of TX-RX devices boosts the CC classification performance to reach 100% accuracy even in NLOS or obstructed conditions.

8:30 Sectorized Cooperative Relay Transmission Utilizing Surplus Energy for WPSNs

Shoma Taniguchi, Kosuke Sanada, Hiroyuki Hatano and Kazuo Mori (Mie University, Japan)

Wireless powered sensor networks (WPSNs) suffer from the double near-far problem, in which supplied and consumed energy depends on the location of sensor nodes (SNs). To mitigate the double near-far problem, cooperative relay transmission has been proposed, in which the packets from the SNs far from a power transmitter are relayed by the close SNs utilizing their surplus energy. In this scheme, however, all the SNs closer to the power transmitter are candidates for relaying, causing energy waste problem at candidate SNs. Moreover, the relay SNs selection problem in WPSNs is commonly solved by iterative algorithms with high computational complexity, which is impractical for practical networks. To address this problem and improve the data collection performance, this paper proposes surplus energy aided cooperative relay transmission employing sectoring technique for WPSNs. The proposed scheme divides the network area into sectors based on the number of SNs accommodated in each sector, and information transmissions are carried out sector by sector. This reduces energy waste in the relay candidate SN and does not require high computational complexity. Thus, this can also improve packet collection rates. The computer simulation results show that the proposed scheme provides better performance over the conventional one.

8:45 LTE Bands for Human Presence Detection Using Software Defined Radio

Allen Gabriel T. Estorque, Miguel Leo S. Malibiran, Kurt Louis A. Mariano, Krizelle Anne Lou M Recinto, Alyza Joyce D. Sones, Josyl Mariela Rocamora Reyes and Jehiel D. Santos (University of Santo Tomas, Philippines)

Human presence detection has various applications, with most existing methods relying on fixed-frequency RF signals like Wi-Fi, limiting transmitter-receiver distance. This study investigates the feasibility of using LTE bands such as bands 28, 3, 1, and 41 for human presence detection in indoor environments. LTE signal data were collected during video call sessions using a Software-Defined Radio (SDR). Support Vector Machine (SVM) models were trained and evaluated on 0 vs. 1 person, 0 vs. 3 person, multiple classifications per band. Accuracy results on 0 vs. 1 person, 0 vs. 3 person comparisons were: Band 28 - 70% & 85%, Band 3 - 50% & 55%, Band 1 - 75% & 80%, and Band 41 - 60% & 85%. Multi-class SVM training yielded accuracies of 60% (Bands 28 ), 50% (Band 3 & 1), and 62% (Band 41). This highlights the impact of LTE frequency variation on human presence and crowd density detection.

9:00 Blind ISI Channel Estimation, Symbol Detection, and Message Recovery Using Clustering

Wakana Yagyu, Tomotaka Kimura and Jun Cheng (Doshisha University, Japan)

In wireless communication systems, the presence of inter-symbol interference (ISI), resulting from multipath propagation and channel memory, poses a significant challenge to reliable symbol detection and message recovery. A blind estimation of the ISI channel with a memory length-(L) is proposed. The iterative (K)-means clustering algorithm is used in the receiver for this blind channel estimation process. The algorithm produces (K^L) potential sets of channel taps as a consequence of channel phase uncertainties caused by phase rotation. Subsequently, these are reduced to (K) sets using the squared error comparison between the received and the reconstructed symbols. The transmitted message is subsequently obtained using these (K) candidate channel tap sets through Viterbi demodulation, channel decoding, and verification of cyclic redundancy check (CRC). The block error rate of the proposed approach is close to that of a perfectly known channel, yet it nearly aligns with the performance of the conventional MMSE estimator utilizing a few pilot symbols.

Tuesday, October 28 8:00 - 9:30

Track A1F6 CCI 1.2: Computing & Computational Intelligence (CCI) 1.2

Room: F6. 505 Sepilok

Chair: Farashazillah Yahya (Universiti Malaysia Sabah, Malaysia)

8:00 Weigh&Pay: Semi-Automated Self-Checkout and Pricing for Non-Barcoded Items Using AI Vision

Maxine Van Caparas, Fernan Frans Pelobello, Jan Kevin Albior Galicia and Maria Leonora Guico (Ateneo de Manila University, Philippines)

Long queues in supermarkets are one of major problems for consumers and retail establishments around the globe. Thus, self-checkouts using barcode scanning have been developed in pursuit of shorter checkout times. However, some customers find these systems to be tedious to use. Furthermore, there is a gap in implementation when it comes to non-barcoded items which require weighing and manual code entry. Thus, deviating from the barcode system and eliminating the need for manual barcode scans may simplify the checkout process by eliminating the need of packaging, weighing, and labeling in advance. This research aims to develop a semi-automated self-checkout system for shopping item classification and price calculation of non-barcode items such as fresh proteins and produce. This was done by developing a computer vision module using YOLOv8 trained on a custom dataset, a time-based digital scale module for real-time weight reading, a price calculation module, and a hardware configuration.

8:15 Improved Genetic Algorithm Based on Fractals for Packet Scheduling in MANETs

Mercy Sharon Devadas (Chennai Institute of Technology)

Mobile Ad-hoc Networks (MANETs) have become an integral component of modern wireless communication systems, especially in scenarios where infrastructure-based networks are either unavailable or impractical. These self-configuring, decentralized networks are used in various applications, such as disaster recovery, military operations, and mobile conferencing, where efficient data transmission is critical. Ensuring the effective operation of MANETs is therefore essential, particularly in terms of Quality of Service (QoS), which encompasses factors like delay, packet loss, and throughput. This study presents an innovative technique aimed at enhancing QoS in MANETs by combining the mathematical concept of fractals with the optimization power of genetic algorithms. Specifically, the research focuses on developing an improved packet scheduling algorithm-a core element in achieving better QoS. The proposed system classifies packets into service classes based on their priority levels and acceptable delay thresholds. Following classification, a modified genetic algorithm is applied to schedule packets efficiently. To assess the effectiveness of this approach, simulations were performed using the NS3 network simulator, and the results were thoroughly analyzed.

8:30 An Efficient Knowledge Graph Construction Using LLM-Based Retrieval Augmented Generation for Agricultural Data

Rohini Naik (COEP Technological University, India & VPKBIET Baramati, India); Sunil Mane (COEP Technological University, India)

The agricultural industry produces a large volume of unstructured data from various sources, including research papers, field reports, weather data, and satellite images. Extracting valuable insights from unstructured data is crucial for improving decision-making, boosting productivity, and driving innovation. Knowledge graphs (KGs) provide a structured method to represent and link information, yet constructing them from unstructured agricultural data poses a significant challenge due to diversity of data and complexity of the domain. In this study, we introduce a methodology for creating agricultural knowledge graph using the Large Language Model (LLM)-based Retrieval-Augmented Generation (RAG) technique. This approach combines 1) the generative capabilities of LLM with the automated extraction and organization of entities-relationships, and 2) the RAG framework, which enhances the precision of information retrieval pertinent to the agricultural field. We describe the design, implementation, and evaluation of our method, showcasing its effectiveness in generating accurate and comprehensive knowledge graph. Our experimental evaluation, demonstrated that proposed framework achieves average ROUGE performance of 29% higher with respect to RAG retrieval and 53% more compared to vector retrieval. Also, average BLEU metric achieves 20% and 68% higher respectively. This work establishes a foundation for scalable, intelligent knowledge system that can support various agricultural applications, such as advisory services, research, and policy development.

8:45 Unlocking Secure Clouds: a Modern Perspective on Access Control and Privacy

Prateek Sharma, Junaid Khan, Mohammed Rehan Deshnoor, Dhruti Dobariya, Darshit Verma and Abhishek Upadhyay (BITS Pilani, Hyderabad Campus, India); Jay Dave (BITS Pilani Hyderabad Campus, India)

As cloud computing continues to support diverse and critical applications, ensuring secure, efficient, and privacy-preserving data sharing across dynamic, multi-cloud environments remains a significant challenge. Traditional solutions often suffer from inflexible access policies, high computational overhead, and complex key management, making them unsuitable for scalable and real-time scenarios. In this paper, we propose a lightweight and practical Data Protection as a Service (DPaaS) framework that enables fine-grained access control, efficient key management, and secure deduplication. Our scheme integrates ciphertext-policy attribute-based encryption (CP-ABE) with policy attribute masking to protect user privacy and utilizes distributed key management to support dynamic role updates without re-encrypting the entire dataset. Additionally, the system incorporates client-side deduplication techniques to reduce storage overhead while maintaining data confidentiality. We implement a prototype and evaluate its performance and security in a real cloud environment. Experimental results demonstrate that our scheme achieves low computation time, supports secure access control, and minimizes resource consumption, making it suitable for deployment in resource-constrained and large-scale cloud-based systems.

9:00 TASNet: a Temporal Attention Network for Front-Wheel Axis Angle Prediction in Real-World Driving

Pritam Chakraborty and Anjan Bandyopadhyay (Kalinga Institute of Industrial Technology, India)

Autonomous navigation in unstructured environments such as off-road terrain remains a significant challenge due to dynamic obstacles and unpredictable surfaces. Most vision-based models depend on structured road features, limiting their applicability in such settings. We propose TASNet, a vision-only deep learning architecture that integrates a lightweight CNN backbone, bidirectional LSTM for temporal context, and spatial attention to prioritize obstacle-relevant features. The model is trained using CARLA-generated synthetic data with domain randomization (e.g., lighting, weather), improving generalization to real-world variability. TASNet reduces steering angle error by 27.4% compared to NVIDIA PilotNet and achieves a 92% collision-free success rate in simulation. Real-world tests on an all-terrain vehicle (ATV) dataset confirm its robustness across varied outdoor scenarios. Moreover, TASNet is optimized for deployment on resource-constrained hardware, delivering real-time inference at 45 FPS with 8.2W power on NVIDIA Jetson AGX. Ablation studies underscore the importance of temporal modeling and attention. TASNet offers a cost-effective alternative to LiDAR-based systems for field robotics and disaster response.

9:15 Real-Time Fabric Defect Detection System Using Yolov8

P Kaythry (Anna University, India & Sri Sivasubramaniya Nadar College of Engineering, India); Swetha A and Shree Varshini N (Sri Sivasubramaniya Nadar College of Engineering, India)

Fabric defect detection represents an essential and pivotal process within the textile industry, which is fundamental for guaranteeing both product quality and consistency across various manufacturing outputs. Nevertheless, the traditional methods of manual inspection that have been employed historically are not only labor-intensive and time-consuming but also inherently subjective, thus making them highly susceptible to human error and misjudgment. In response to these significant challenges, this study presents a machine learning approach that utilizes the You Look Only Once model (YOLO) to effectively tackle and ameliorate these pressing issues. The intricate nature and vast diversity of fabric defects, which can arise from a multitude of factors including machine malfunctions, inferior quality yarns, spoilage of materials, and excessive stretching during the manufacturing process, greatly exacerbate the challenges associated with accurate detection. To overcome these formidable obstacles, the proposed YOLO model serves as a more efficient and precise alternative to the traditional inspection.

Tuesday, October 28 8:00 - 9:30

Track A1F7 ETS 1: Engineering Technologies & Society (ETS) 1

Room: F7. 506 Selingan

Chair: Dalila Mat Said (Centre of Electrical Energy Systems (CEES), Universiti Teknologi Malaysia, Malaysia)

8:00 Indoor Comfort Analysis of Integrated Radiant Ceiling Panels with Fan Coil Units in a Tropical Climate

Mohammad Hadi Ghasemi (Universiti Kuala Lumpur Malaysian France Institute, Malaysia); Kushsairy Kadir (Universiti Kuala Lumpur British Malaysian Institute, Malaysia); Mohammad Mehdi Salehi Dezfouli (Norwegian University of Science and Technology, Norway); Muhamad Fahezal Ismail (UniKL MFI, Malaysia); Alireza Dehghani-Sanij (University of Waterloo, Canada); Mohammad Miqdad Abdul Aziz (Universiti Kuala Lumpur British Malaysian Institute, Malaysia)

This study investigates the integration of Radiant Cooling Panels (RCPs) with Fan Coil Units (FCUs) in the Sustainable Energy Living Lab (SELL) at the University of Kuala Lumpur British Malaysian Institute (UniKL BMI) in a tropical climate. The thermal performance of the building is evaluated using real-world data and detailed simulations in TRNSYS, following ASHRAE standard 55 for thermal comfort. The results show that the combination of RCPs and FCUs significantly improves Thermal Comfort (TC), with more than a 50% reduction in the Predicted Mean Vote (PMV) and Predicted Percentage of Dissatisfied (PPD) indices compared to scenarios using only FCUs. Annual and monthly analyses reveal stabilized PMV values closer to neutral levels and substantial PPD reductions, indicating enhanced Indoor Environmental Quality (IEQ). These findings highlight the potential of Radiant Cooling Technology (RCT) to achieve both energy efficiency and occupant satisfaction, thereby supporting the advancement of sustainable building practices in tropical climates.

8:15 Evaluation of a Neural Network for Personal Identification from the Frequency Spectrum of Ballistocardiographic Signals

Hitoshi Ueno (Tokyo Information Dsign Professional University, Japan); Karin Takahashi (Tokyo Information Design Professional University, Japan)

Ballistocardiogram signals are vibration signals on the surface of the body caused by changes in blood pressure, and can be obtained by pressure sensors. Since all that is required is to detect pressure fluctuations, there is no need to place the sensor in close contact with the skin of the human body, and biosignals can be obtained even through clothing, making them widely used. Conventionally, the vital signs obtained by pressure sensors and used as biosignals are heart rate and respiratory rate, but ballistocardiogram signals also contain other information about the living body. We have been focusing on the fact that the waveform of ballistocardiogram signals contains personal characteristics, and have been researching a personal identification algorithm that can identify individuals from ballistocardiogram signals. In this study, we analyzed ballistocardiogram data from 10 different individuals by inputting the frequency spectrum data of the ballistocardiogram signals into a three-layer fully connected neural network. As a result, we were able to achieve an F1 score of 0.7 or higher, which is an evaluation method of the classification system. In addition, in the analysis of nine individuals excluding subjects with a lot of body movement, we achieved a high classification accuracy of 0.87.

8:30 The Portable Fingerprint Attendance Using NB-IoT

Thanakrit Tawatthanadech, Thitirath Chaewsuwan and Thana Udomsripaiboon (University of Phayao, Thailand)

This paper presents a portable fingerprint scanner for recording the attendance of students using Internet of Things (IoT) technology through the Narrowband Internet of Things (NB-IoT) communication system, which is a low-power long-distance communication. This device is compact and energy-saving. It can be carried to various locations through the NB-IoT communication system to connect to the Internet. Moreover, the proposed fingerprint scanner system is able to monitor the attendance data of students by using the R307 fingerprint sensor module to receive information from each user's fingerprints and then send it to store and process on the Google service platform, which is the Google Sheet, via the Internet system based on cloud technology. The data on the cloud technology can be processed, report and summarize the attendance results. It also has the ability to store and forward all information to those involved in developing the quality of students for further proceedings.

8:45 Building Students' Industry Workplace Skills Using Artificial Intelligence

Chien Ching Lee (SIT, Singapore); Ryan Fraser Kirwan (Singapore Institute of Technology, Singapore)

Effective written communication is a crucial skill for students transitioning to industry, where accurate documentation of project activities and decisions are paramount. This paper evaluates the impact of a structured series of embedded workshop designed to enhance logbook writing skills among Applied Computing students to prepare them for their eight-month internships under an Integrated Work Study Programme (IWSP). The workshop was held over a three-week period, two hours per week during scheduled online classes. Generative AI (GenAI) performed different roles for the workshop: as a chatbot taking on the role of a group of clients in a Requirements Engineering (RE) exercise (lesson 1), as a coach to identify weaknesses in their writing (lesson 2) and as an editor to help them revise their RE documentation drafts (lesson 3). Students performed RE documentation through iterative logbook entries. Data was collected via a comparison of students' logbook entries in lesson three compared to lesson one and an end-of-workshop survey. The primary improvements observed between the students' logbook submissions were in critical analysis, content relevance and depth, and the integration of supporting ideas. The survey findings revealed that GenAI, when deployed as a user, helped generate ideas during brainstorming sessions and provided a basic and structured skeleton to kickstart the writing process. GenAI, as a coach, helped students be more objective by considering multiple perspectives. As an editor, it offered clarity and formatting suggestions, which helped students write more clearly and coherently. The findings underscore the effectiveness of AI-assisted workshops in preparing students for industry by developing essential written communication skills, thereby fostering better project documentation and reporting practices.

9:00 Deep Learning-Based Detection and Auditory Summation of the Philippine New Generation Series Peso Coins for Visually Impaired Individuals

Robert G. de Luna (Polytechnic University of the Philippines, Philippines & University of Sto. Tomas, Philippines); Jana Elyssa O. Bandong (Polytechnic University of the Philippines - Sto. Tomas Campus, Philippines); Selwin Adam R. Garcia (Polytechnic University of the Philippines - Santo Tomas Campus, Philippines); Trishia L Mirabel, Maria Carmela L. Panaligan and Jullianne Christille Narito Sunga (Polytechnic University of the Philippines - Sto. Tomas Campus, Philippines)

This research is a deep learning-driven assistive device specifically engineered to support visually impaired individuals in identifying Philippine New Generation Series (NGS) peso coins. The primary goal of this study is to facilitate independent and accurate coin recognition through an accessible and user-friendly auditory feedback system. The system leverages computer vision and artificial intelligence to distinguish between various denominations based on their visual features. To train deep learning models, a custom dataset consisting of 1,419-coin images was manually collected, ensuring diversity in coin orientation, lighting conditions, and background environments to reflect real-world usage. Three deep learning architectures-Convolutional Neural Network (CNN), ResNet50, and VGG16-were selected for evaluation, given their proven efficacy in image classification tasks. These models were trained and validated on the prepared dataset, and their performance was compared in terms of accuracy. Among the three, the CNN model outperformed the others, attaining an accuracy of 99.89%, demonstrating its superior capability in recognizing coin types with high precision and minimal error. The finalized CNN model was then integrated into a functional hardware prototype which combines the trained model with a camera module and a microcontroller-based processing unit. Upon capturing an image of the coin, the system classifies its denomination and provides real-time auditory feedback, thereby enabling blind or visually impaired users to accurately identify coins without external assistance.

9:15 Integrating Generative AI into Creative Workflows: a TAM-Based Investigation of AI Adoption in Video Production and Editing

John Heland Jasper Ortega (FEU Institute of Technology, Philippines)

This study investigates the integration of generative artificial intelligence (AI) into video production and editing workflows using the Technology Acceptance Model (TAM) as the theoretical framework. Given the creative industry's substantial economic contribution globally and in the Philippines, the research focuses on how video professionals perceive the usefulness and ease of use of AI tools such as Adobe Premiere Pro, After Effects and OpenAI's Sora. A purposive sample of 60 professionals, including editors, motion graphics artists and content creators, was surveyed to assess behavioral intention, user attitude and the influence of external factors on AI adoption. Findings indicate a generally positive attitude toward AI, with many respondents highlighting increased efficiency and automation of repetitive tasks. However, the study also identifies key barriers such as skill gaps, tool complexity and limited training opportunities, emphasizing the need for competency-based training and institutional support. Correlation and word cloud analysis reinforce the importance of enhancing education quality and multimedia learning to support effective adoption. The results confirm TAM's applicability in this context and suggest that with improved accessibility, policy alignment and targeted capacity building, generative AI can significantly enhance creative workflows while supporting the development of employability skills in the digital content sector.

Tuesday, October 28 8:00 - 9:30

Track A1F8 CCI 1.3: Computing & Computational Intelligence (CCI) 1.3

Room: F8. 507 Monsopiad

Chair: Anup Nandy (National Institute of Technology Rourkela, India)

8:00 Disentangle Class Imbalance in Micro-Expression Recognition with Causal Structure Learning

Pei Sze Tan (Monash University, Malaysia Campus, Malaysia); Sailaja Rajanala (Monash University, Malaysia); Raphael C.W. Phan (Monash University, Malaysia Campus, Malaysia)

Micro-expression recognition is a critical task in affective computing, yet it is often hindered by biases inherent in datasets, leading to skewed and unreliable outcomes. This work introduces a novel approach to disentangling bias in micro-expression recognition using causal structure learning. By modeling causal relationships within the data, we identify and mitigate sources of bias that traditional machine learning models often overlook. Our framework integrates causal discovery techniques to uncover biased patterns in widely used micro-expression datasets. We employ debiasing strategies to enhance the fairness and accuracy of recognition models and generate counterfactual examples to address sensitive attributes such as gender and age, allowing us to observe the effects of imbalanced classes on classification results. Experiments conducted on baseline micro-expression recognition models demonstrate comparable results after undersampling to create emotion class balance, revealing label bias in current training datasets including CASME2, SAMM, and SMIC. Further evaluation on balanced gender and age classes using generated counterfactual data as additional training instances showed performance improvements for the 4DME dataset.

8:15 Unsupervised Machine Learning of Historical Flood Data for Flood Events Clustering

Jackel Vui Lung Chew and Sean Woon (Universiti Malaysia Sabah, Malaysia)

Floods remain one of the most frequent and destructive natural disasters in Malaysia. To understand flood characteristics, which aid the development of early warning systems, we investigate the effectiveness of unsupervised machine learning techniques to identify clusters of flood events based on historical flood data provided by the Department of Irrigation and Drainage Sabah. The dataset used in this study consists of hydrological features such as average recurrence interval, flood duration, flood depth, and affected areas. Due to the nature of raw dataset such as missing data, varying scales, and outliers, we generated six different preprocessed datasets using an extensive data preprocessing which includes handling of missing values, feature normalization, and outlier removal. The main reason for doing this task was to ensure the clustering model robustness. We then applied five clustering algorithms, which are K-means, K-medoids, hierarchical clustering, density-based spatial clustering of applications with noise (DBSCAN) and its extended version, HDBSCAN, to derive natural clustering in the flood events. The clustering quality was carefully assessed using internal validation metrics such as silhouette score, Davies-Bouldin index, and two-dimensional visualization by principal component analysis. In our study, K-means, K-medoids, and hierarchical clustering outperformed DBSCAN and HDBSCAN when a predefined number of clusters was required. The best clustering quality was achieved using datasets with imputation and normalization. The major contribution of our study is to provide meaningful insights into the characteristics of flood events, offering potential applications in early warning systems.

8:30 DR-VQA: Large Language Model Based Vision Question Answering System on Diabetic Retinopathy

Saleh Musleh (Hamad Bin Khalifa University & Aspire Academy for Sports, Qatar); Hamada Al Absi and Md Rizwan Parvez (Hamad Bin Khalifa University, Qatar); Anant Pai and Ghassan Salih (Hamad Medical Corporation, Qatar); Tanvir Alam (Hamad Bin Khalifa University, Qatar)

Medical Visual Question Answering (VQA) presents challenges due to the complexity of imaging data and the need for precise, context-aware responses. Traditional VQA models often struggle in clinical settings, limiting their utility in decision-making. This study fine-tunes the BLIP (Bootstrapped Language-Image Pretraining) model for medical VQA, leveraging both visual and textual data to generate accurate diagnostic answers. To enhance semantic relevance, BERT-based similarity evaluation is integrated. Using a dia- betic retinopathy dataset, the model achieves a validation BERT similarity (BERTsim) score of 0.94 and a test score of 0.95 on 450 samples, demonstrating strong alignment with expert an- notations. These results highlight the model's potential to assist clinicians by improving diagnostic accuracy and efficiency. The proposed approach can streamline medical workflows, reduce clinician workload, and enhance patient outcomes. Future work will focus on expanding datasets and refining the model for broader medical applications. We believe our approach will support to enhance the patient care as well democratization on AI technology for community.

8:45 Machine Learning-Based Prediction of Fuel Consumption for Diesel and Gasoline Vehicles Considering Terrain Variations

Shaun Patrick B Calumba, Curt Johann M Maracha, John Darren E Casidlac and Cedrick Johnlery Sayson Judilla (National University, Philippines)

This study presents a machine learning-based approach to real-time fuel consumption monitoring for diesel and gasoline vehicles considering terrain variations. The study leverages machine learning algorithms to develop a predictive model that accounts for the impact of terrain variations on fuel consumption. The proposed model integrates terrain data acquisition and various vehicular parameters, including speed, RPM, throttle position, elevation, road grade, tire circumference, engine torque, piston area, and cylinder capacity. To enhance accuracy, the study utilizes Support Vector Machine, Random Forest Regression, and Gradient Boosting Regressor models. The results are validated using K-Fold cross validation and the Hold Out method, ensuring the robustness of the predictive model. Through this model, Comparative analyses including correlation matrix evaluation, scatter plots, Feature Importance, boxplots, and 3D visualizations demonstrate the relationships between key variables and fuel consumption patterns. The results highlight the impact of terrain and vehicle dynamics on fuel efficiency, offering valuable insights for optimizing fuel consumption strategies.

9:00 ChatGPT in the Classroom: A Qualitative Study of Educators' Pedagogical and Ethical Perspectives

Sareen Kaur Bhar, Nurul Sakinah Aziz, Munirah Munawar Ali and Aimi Hazwani Abdullah (Multimedia University, Malaysia)

This qualitative study investigates educators' perceptions of ChatGPT integration in higher education through 12 semi-structured interviews. The objective of this study is to examine Malaysian higher-education educators' perceptions of integrating ChatGPT into teaching, focusing on pedagogical benefits, ethical concerns, and institutional readiness. Findings indicate that educators view ChatGPT as a useful tool for enhancing teaching strategies, improving engagement, and supporting professional tasks. Reported benefits include idea generation, efficiency, and personalized learning. ChatGPT was also noted for helping overcome language barriers and resource limitations. However, challenges such as limited institutional support, ethical concerns, reduced critical thinking, and unequal access remain significant barriers. By centering educators' experiences often overlooked in AI-in-education research, this study offers valuable, practical, and context-specific insights for responsible and effective ChatGPT adoption in diverse contemporary higher education settings. The findings are based on 12 participants from a single Malaysian institution and may not be generalizable across all contexts.

9:15 Personalising AI-Generated Jokes: an Empirical Study

Ally Tze-Rou Teh (Australia); Toh Xiao Ying (Monash University, Australia); Chai Xuen Seow (Malaysia); Tazeek Bin Abdur Rakib and Lay-Ki Soon (Monash University Malaysia, Australia)

This paper presents our investigation of the potential of generative AI in crafting personalised humour, focusing on how user-specified keywords and demographic information can guide joke generation. We introduce a two-part system: (1) a personalised joke generator built using GPT-3 that tailors humour based on keywords and user demographics (age, gender, continent), and (2) an automated joke ranking pipeline powered by fine-tuned BERT models to filter and prioritise high-quality, humorous content, trained on Reddit-sourced joke data. To evaluate the effectiveness and reception of the generated jokes, we conducted a human-centered evaluation involving 32 participants with diverse personality profiles. Evaluators rated the jokes across four humour dimensions: Funniness, Offensiveness, Surprise, and Reality Representation. Our findings reveal notable variations in humour perception across personality types based on MBTI. For instance, Sentinels responded more positively to the generated jokes, while Analysts tended to rate them lower across most categories. Additionally, a strong correlation between perceived funniness and surprise highlights the role of unexpectedness in humour appreciation. These results underscore the importance of personality-aware humour generation and suggest that current AI models require further refinement to effectively cater to diverse audiences.

Tuesday, October 28 1:30 - 3:30

Interactive Session A.1

Room: Interactive Area 1, Foyer of Sipadan

Chairs: Md Pauzi Abdullah (UTM, Malaysia), P. Susthitha Menon (Universiti Kebangsaan Malaysia, Malaysia & Institute of Microengineering and Nanoelectronics (IMEN), Malaysia)

#1 Development of BaRISTA: a Computer Vision-Based Real-Time Integrated Sorting Tool for the Assessment of Green Coffee Beans

Cristina Ysabel Almario, Kyle Aaron Coloma, John Carlo Velonza and Carlos Oppus (Ateneo de Manila University, Philippines)

Small-scale coffee bean producers remain heavily reliant on manual sorting, which can be a tedious, labor-intensive, subjective, and error-prone process. In line with this, the study aims to develop BaRISTA, an automated system that combines a custom YOLOv8 classification model with hardware components to assess and sort Robusta green coffee beans (GCBs) based on the presence of visible defects, providing small coffee producers with a more affordable yet precise and reliable alternative to current manual methods or more expensive equipment. The study entailed developing a custom dataset of Robusta GCBs, training a YOLOv8 classification model to classify the beans based on the presence of visible defects, and testing the model with batches of unseen beans. Testing showed the classification system's effectiveness as the model achieves a precision of 97.794%, an accuracy of 99.375%, a recall of 99.750%, and an F1-score of 98.762%-all of which demonstrate its strong capability in classifying GCBs.

#2 Practical 2D FSM for Stochastic Computing with Improved Hardware Efficiency and Accuracy

Jiho Kim, Gayoung Kang and Youngmin Kim (Hongik University, Korea (South))

As artificial intelligence continues to advance rapidly, traditional binary computing methods face limitations regarding power consumption and hardware complexity. Stochastic Computing, which processes data using probabilistic bitstreams, offers a computational paradigm that enables highly parallelized operations, enhances energy efficiency, and reduces hardware complexity. Despite its potential, the practical application of SC has been constrained by the trade-off between efficiency and accuracy. To address this gap, this study proposes the 4×4 two-dimensional finite state machine architecture and method for Stochastic Computing, demonstrating an optimized structure that improves both efficiency and accuracy. The architecture utilizes independent probabilistic inputs and weight-adjusted state transitions to reduce bitstream correlation and enable more flexible function implementation. This configuration achieves over 1.37× better performance in terms of accuracy, area, and power consumption compared to prior designs, while maintaining a practical and scalable design that balances hardware complexity and computational precision, making it well-suited for neuromorphic computing and other low-power, high-efficiency applications.

#3 Changes in DC Characteristics Due to Changes in Crystal Structure of AlGaN Layer Due to Voltage Stress Application in AlGaN/GaN HEMTs

Sho Nagai, Soichi Sano, Junya Takeda, Hideki Hoji and Hirohisa Taguchi (Chukyo University, Japan)

Voltage stress of 1-12 V was applied to an AlGaN/GaN high-electron-mobility transistor (HEMT) from the drain electrode, and the I-V characteristics were measured precisely. The experimental results revealed that the change in I-V characteristics caused by voltage stress resulted from changes in the crystal structure of the AlGaN layer. There was no significant change when the voltage stress value was approximately 1 or 2 V. When 3-5 V was applied, the drain current value increased, and the initial strain resulting from crystal defects remaining in the AlGaN layer was canceled by the electric field. However, under excessive stress of 6 V or higher, distortions caused by crystal defects occurred again in the AlGaN crystal structure, electron scattering increased, and the current value decreased. The current value changed greatly in the region where the drain voltage was between 1.0 and 3.2 V, but the I-V characteristics matched in the region where the drain voltage was 3.2 V or more. Therefore, it is believed that two-dimensional electron gas (2DEG) formed stably at approximately 3.2 V, and the crystal defects were not affected by the voltage stress change. Furthermore, when the measurement temperature was changed to 80°C and 100°C, an overshoot effect was observed near a drain voltage of 3.2 V. This is considered to be caused by the improvement in carrier mobility resulting from stable 2DEG formation.

#4 Development of High-Performance Multiply-Accumulate (MAC) Unit Design on FPGA for DSP Applications

Nazirul Hafiz Mohd Nazeri (Sophic Automation Sdn Bhd, Malaysia); Siti Zarina Md Naziri and Rizalafande Che Ismail (Universiti Malaysia Perlis, Malaysia)

The multiply-accumulate (MAC) unit performs multiplication and accumulation, which makes it an essential component for digital signal processing (DSP) applications. Designing high-performance MAC units is crucial for enhancing the computational efficiency of modern computer systems. However, achieving a balance between delay, power consumption, and area utilization poses a significant challenge. This study thoroughly compares and analyzes various multiplier and adder architectures, including Vedic multipliers, ripple carry adders, Brent-Kung adders and pipelined Brent-Kung adders. The research aims to design an efficient MAC unit by investigating optimal combinations of these components. The proposed design utilizes a Vedic Multiplier using Brent-Kung adder and Pipeline Brent-Kung adder which are compared with other configurations. The MAC unit is implemented in Verilog HDL and analyzed using Intel Quartus Prime and functionally verified using Intel ModelSim. The design was implemented on the Altera Cyclone V 5CSXFC6D6F31I7ES FPGA. The synthesis process reveals that the proposed design achieves a low delay of 28.506 ns, an area utilization of 360 LUT units, and a balanced power consumption of 427.600 mW, resulting in the speed of 310.850 MHz. The study concludes that combining the Vedic multiplier with Brent-Kung adder and Pipeline Brent-Kung adder significantly improves MAC unit performance.

#5 LoRa-Enabled Wireless Sensor Network for Water Consumption Reading and Billing Using Optical Character Recognition

Christian P Javier, Veronniecka T Tolentino, Jasmine Joy L Atienza, Joshua Kyle G Bravo and Francis A Malabanan (FAITH Colleges, Philippines)

The growing need for precise and efficient water consumption monitoring underscores the limitations of traditional meter reading methods, which depend on manual labor and are susceptible to human error, limited accessibility, and delayed billing processes. This research presents a LoRa-based wireless sensor network that automates water meter reading and billing using Optical Character Recognition (OCR) technology. The system employs an ESP32-CAM module to capture images of water meter displays, which are then processed using OCR algorithms to extract numerical data. The extracted readings are transmitted via LoRa communication to a central gateway and stored on a cloud-based platform, which supports a web interface for real-time monitoring. The proposed solution significantly enhances data transmission range, reading accuracy, energy efficiency, and system responsiveness. By enabling continuous and remote monitoring of water usage, the system helps detect leaks early and encourages responsible consumption. This approach benefits both consumers and utility providers by improving operational efficiency, reducing costs, and promoting sustainable water management practices.

#6 Effect of Gender and Walking Condition on Pelvic Orientation and Gait Parameters in Young Adults

Yin Qing Tan (Universiti Tunku Abdul Rahman, Malaysia); Joe Yan Tan (ViTrox, Malaysia)

Pelvic orientation plays a crucial role in gait. While gender-based anatomical differences in pelvic structure are well-documented, their influence on dynamic walking patterns remains underexplored particularly in different walking conditions such as overground and treadmill walking. This study aims to investigate the effects of gender and walking condition on pelvic orientation and spatiotemporal gait parameters among healthy undergraduate students. Fifteen healthy participants walked barefoot at their self-selected pace. Data collected using a three-axis accelerometer. The Mann-Whitney U test was used to examine gender differences while Wilcoxon test assessed differences between walking conditions. No significant gender differences were found in most parameters, except pelvic tilt range which was greater in females, likely due to anatomical differences. In contrast, significant differences were observed between walking conditions. Treadmill walking was associated with lower speed, lower cadence and reduced pelvic orientation, which may be attributed to cautious gait adaptations, limit optic flow, and reduced propulsion forces inherent to treadmill environments. Walking condition significantly influences pelvic orientation and gait parameters, whereas gender appears to have minimal effect in a homogenous, healthy young population. These findings highlight the need to consider walking environment in gait assessments, particularly in clinical and rehabilitative settings. Overground walking may better represent natural gait mechanics, while treadmill use may necessitate adaptation strategies for stability and balance.

#7 Impact Study of Faulty Sensors on Flocking-Based Cooperative Control of Nonholonomic Robots

Lamia Iftekhar (North South University, Bangladesh)

In this paper, we investigate the effects of faulty internal sensors on a group of non-holonomic robots implementing a well-established flocking algorithm. The flocking algorithm has long been proven to have robust and scalable performance for agents with nonholonomic constraints, but it traditionally assumed perfect state information for all agents. To evaluate its resilience in more realistic scenarios, we systematically introduce robots with faulty sensors that incorrectly measure their own states. These agents broadcast their erroneous states to their neighboring agents, who in turn use the compromised information to develop their control input. We conduct a comprehensive analysis across a spectrum of scenarios, varying both the density of robots with faulty sensors and the intensity of sensor errors. Qualitative insights obtained through individual simulation runs as well as aggregate quantitative results are discussed. Four carefully chosen evaluation metrics are employed to quickly assess performance degradation caused by the faulty sensors. We sought to identify critical failure points for common scenarios through two distinct sets of experiments. Our goal is to provide empirical results and discussions that can inspire the design of more robust and cost-effective controllers, specifically by targeting the primary failure mechanisms identified.

#8 Performance of Unmanned Aerial Vehicle Access Points in Cell-Free Massive Multiple-Input Multiple-Output System

Noor S Othman, Afsana Afrin and Abdulrahman Mohamed Shalaby (Universiti Tenaga Nasional, Malaysia)

Cell-free massive MIMO (CF-mMIMO) eliminates fixed cell boundaries by using distributed access points (APs) coordinated by a central unit, enabling spatial diversity and improved throughput. However, fixed ground APs lack flexibility, making them unsuitable for highly mobile users or emergency scenarios. This paper investigates the deployment of unmanned aerial vehicles (UAVs) as access points in CF-mMIMO systems, focusing on optimizing their three-dimensional (3D) placement to maximize the downlink sum rate. A comparative analysis of Particle Swarm Optimization (PSO) and Gradient Descent (GD) algorithms is conducted under varying user densities and numbers of UAVs. An existing air-to-ground path loss model, incorporating UAV altitude constraints, is employed to evaluate UAV placement in CF-mMIMO systems. Simulation results demonstrate that PSO consistently outperforms GD in both convergence speed and achievable throughput. At higher user densities, PSO achieves approximately 3 bps/Hz higher throughput than GD and up to 7.3 bps/Hz gain at the highest density considered. The results confirm the effectiveness of PSO for UAV deployment in CF-mMIMO systems, where its adaptability to dense and dynamic user distributions provides significant performance gains.

#9 Design and Implementation of Posit Arithmetic-Based FIR Filter on FPGA

Keerthana Menon (IIIT Kottayam, India); Jayakrushna Sahoo (Indian Institute of Information Technology Kottayam, Kerala, India); Kala S (Indian Institute of Information Technology Kottayam, India); Nalesh S (Cochin University of Science & Technology, Kochi, Kerala, India)

With increasing advancement in the field of signal processing, there is a need to develop efficient hardware that can support the digital signal processing algorithms for various applications, such as audio and video processing, image segmentation, image enhancement and other medical applications, communication systems, etc. Current systems employ the IEEE-754 format to represent numbers. However, the posit number system has emerged as a promising alternative, offering improved accuracy and dynamic range. In this work, we present a posit arithmetic-based finite impulse response (FIR) filter for signal processing applications. Here, we implement 16-bit and 32-bit posit adders and multipliers, excluding guard, round, and sticky (GRS) bits. These arithmetic modules are used to implement two 5-point FIR filters to smooth out a noisy signal. The proposed posit arithmetic-based architecture is implemented on the Zynq UltraScale+ ZCU104 FPGA device and the hardware resource utilization is reported. It is observed that the 32-bit posit FIR filter achieves a performance comparable to the 64-bit floating-point-based filter at an operating frequency of 28.57 MHz.

#10 Enhanced Local Disparity Map Estimation Using Segment-Side Window-Based Cost Aggregation

Ahmad Fauzan Kadmin (Universiti Teknikal Malaysia Melaka, Malaysia)

Accurate disparity map estimation is critical for computer vision applications such as 3D reconstruction, autonomous navigation, medical imaging, and immersive entertainment systems. Traditional local window-based methods often suffer from edge fattening and texture inconsistency, leading to inaccurate depth estimation. This paper proposes a Segment-Side Window-Based (SSB) algorithm that integrates segment-based cost aggregation to address these challenges. The SSB method combines Truncated Absolute Difference (TAD), Gradient Magnitude (GM), and Census Transform (CT) for robust matching cost computation, followed by Superpixel Linear Iterative Clustering (SLIC) segmentation and Side Window Filtering (SWF) for edge-preserving aggregation. The final stage involves postprocessing and disparity refinement to enhance the accuracy of the disparity map with left-right consistency checking (L-R Check) and median filter. Experimental results on the Middlebury dataset demonstrate that SSB achieves superior performance, reducing the average bad pixel error to 24.7% (All) and 14.6% (Nonocc), outperforming state-of-the-art methods like Bilateral Filter (BF), Guided Filter (GF), and Median Filter (MF).

#11 Distributed IMM-Based Cooperative Object Tracking Using Multiple Roadside LiDARs

Ryota Imai, Masafumi Hashimoto and Kazuhiko Takahashi (Doshisha University, Japan)

This paper proposes a tracking method of objects, such as cars, two-wheelers, and pedestrians, using multiple light detection and ranging sensors (LiDARs) set in an intersection environment. Each LiDAR unit detects objects using deep learning CenterPoint from its own LiDAR point cloud data and exchanges object information (i.e., the position, size, heading angle, and confidence score of the objects) by communicating with its neighboring LiDAR units. Then, the object information is fused. Here, each LiDAR unit estimates the object poses, and the estimates are fused by exchanging the information among the neighboring LiDAR units. A distributed interacting multimodel estimator is employed to accurately estimate the poses of objects under various motion modes, such as stopping and suddenly moving and stopping, in a distributed manner without requiring a central server. Simulation experiments using four LiDAR units with a mesh type of network topology set at signal poles in an intersection environment validate the performance of the proposed method.

#12 Pi-Droponics: an Automated Hydroponics System with Notification System Using Convolutional Neural Network

Curt Johann M Maracha, Shaun Patrick B Calumba, Joshua Shem M Barachina and Analyn Balog (National University, Philippines)

Hydroponics is a soilless farming method that utilizes technology to regulate the growth environment. A two-layer vertical hydroponic system was constructed in a greenhouse utilizing the Nutrient Film Technique, including sensors that keep track of the light intensity, air temperature, TDS, and pH levels. The objective is to develop a fully automated hydroponic system with alerts for monitoring plant growth and detecting diseases. The sensor data is sent to the NodeMCU esp8266 and saved in Firebase, while a CNN algorithm is used to create a plant disease detection model, which is stored in a Raspberry Pi 4B. The app retrieves data from Firebase and sends real-time updates on system conditions, plants' health status, and notifications when sensor values are outside the threshold range or diseases are detected. Accurate pH and TDS readings effectively monitor the greenhouse environment for the hydroponics system. Air humidity, temperature, and light intensity sensors indicate no significant difference between the greenhouse and the outside environment. A disease detection model using CNNs was developed and successfully tested on external plant leaves. The healthy state of the system's plants indirectly validates the model's effectiveness, although direct testing with the system's plants was not conducted.

#13 Design of a Mobile Navigation System for a University Pedestrian

Jan Kevin Albior Galicia (Ateneo de Manila University, Philippines)

This paper presents ADMUNAV, a mobile navigation system developed to assist pedestrian wayfinding within Ateneo de Manila University. Addressing the challenges of navigating a large, congested campus, ADMUNAV utilizes geospatial datasets and Dijkstra's algorithm to compute optimal walking routes in real time. The application features an offline-capable Realm database, intuitive Android user interface, multi-entrance building handling, and dynamic path recalculations with alternative route suggestions. Performance testing demonstrated 100% routing accuracy, an average computation time of 98.9 ms, and minimal memory usage across multiple Android devices. By offering a lightweight, infrastructure-independent navigation solution tailored for university environments, ADMUNAV contributes toward enhancing campus mobility and supports Sustainable Development Goal 9 on innovation and infrastructure.

#14 From Triple Helix to Quadruple Helix: the Evolution of University-Government-Industry Collaboration

Yaeko Mitsumori (Osaka University, Japan)

Until the early 2000s, the prevailing assumption in most national innovation systems was that scientific discoveries and inventions would naturally drive economic development and, in turn, societal advancement. The R&D community guided research trajectories in basic, applied, and industrial research, while the public remained passive recipients of innovation. However, over the past two decades, a new approach has gained prominence. Today, research trajectories are expected to be legitimized among relevant publics, designed to create positive public impact, and shaped with public participation. A science park is considered a structure that fosters innovation. Traditionally, the key players in a science park were academia, government, and industry. However, with the advent of the SDGs era, the science park model has been shifting to the Quadruple Helix Model. In this framework, not only academia, government, and industry but also the community and citizens play a crucial role in developing a science park. This paper focuses on Tsuruoka Science Park in Yamagata and examines the transition from the Triple Helix collaboration model to the Quadruple Helix Model.

#15 Democratizing Gait Analysis: Development and Validation of a Customizable Wearable IMU System

Siow Cheng Chan (University Tunku Abdul Rahman, Malaysia); Boon Meng Gan (Universiti Tunku Abdul Rahman, Malaysia)

Gait analysis plays a crucial role in clinical settings for assessing abnormalities associated with musculoskeletal and neurological conditions. Traditional laboratory-based equipment used for gait assessment is costly and impractical for continuous monitoring in daily life. Wearable inertial sensors offer a promising alternative due to their affordability, portability, and user-friendly nature. However, their reliability and validity compared to established techniques remain underexplored. This study proposes a wearable inertial sensor system designed to address these challenges by providing a low-cost, flexible solution that enhances accessibility to gait analysis. The system's reliability was validated against the BTS G-Walk wearable inertial sensor. By employing kinematic equations and advanced filtering algorithms, including a fine-tuned Kalman filter and algorithm, the inherent uncertainties were effectively mitigated. The prototype integrates ESP32 and MPU6050 components to enhance functionality. Comparative analysis with the BTS G-Walk demonstrated high accuracy, achieving over 85% agreement and less than 16% Relative Root Mean Square Error (RRMSE) for gait speed, step count, step length, stride length, and cadence measurements. Overall, this study establishes the proposed system as a reliable and valid tool for spatiotemporal gait assessment in healthy adults, offering a practical solution for clinical applications and research.

#16 EcoFruit14K: a Large-Scale Collection for Spotting Green Produce in Natural Backdrops

Nirban Roy (Institute of Engineering & Management, Kolkata, India); Swapnanil Adhikary (Institute of Engineering and Management Kolkata, India); Shreyan Kundu (Institute of Engineering and Management, India); Susovan Jana (Institute of Engineering & Management, Kolkata, India & Jadavpur University, India); Shuvam Chakraborty (Indian Institute of Technology Delhi, India)

Detecting camouflaged objects in agricultural environments-specifically green lime fruits blending into dense foliage-poses a significant challenge due to minimal color and texture contrast. To address this, we present DLP-14k, a benchmark dataset comprising 14,000 high-resolution images of lime fruits captured under realistic field conditions. The dataset encompasses varied lighting scenarios (morning shadows, midday glare, late-afternoon diffuse light), multiple occlusion levels (overlapping leaves, branches), and includes 1,000 foliage-only negative samples to reduce false positives. We augment the dataset through systematic transformations-random rotations, horizontal and vertical flips, and brightness and contrast adjustments-to enhance model generalization and mitigate overfitting. We evaluate two state-of-the-art deep learning frameworks: YOLOv8, a one-stage detector optimized for real-time applications, and Faster R-CNN, a two-stage detector known for high precision. Experimental results demonstrate that YOLOv8 achieves inference speeds exceeding forty-five frames per second with competitive mean Average Precision (mAP), but suffers from elevated false positive rates in low-contrast settings. In contrast, Faster R-CNN attains approximately a 1.5% higher mAP and lower misclassification rates under heavy occlusion, albeit with reduced inference throughput. We conduct ablation studies on augmentation strategies, anchor box configurations, and backbone network depths, quantifying their impacts on precision, recall, and mAP. We release DLP-14k alongside standardized training and evaluation protocols and baseline code to foster reproducibility. Models are trained using an 80:10:10 split for training, validation, and testing. Baseline code includes example training and inference scripts. These resources underpin reproducible research and facilitate rapid prototyping of precision agriculture solutions such as autonomous harvesting robots and drone-based monitoring systems. Future work includes extending the dataset to additional fruit species and environmental conditions, exploring hybrid attention-based architectures for improved discrimination, and integrating segmentation masks. Our contributions provide the computer vision and precision agriculture communities with a robust resource and benchmark results for advancing camouflaged object detection in natural environments.

#17 Development of Organizational Community Extension Tracking and Approval System Using Microsoft as a Low-Code Development Platform

Jan Guiller U. Vergara, Christian Aldwin D. Canlapan, Gian Mark T. Pulgar, Jenelyn J. Salimbagat, Ron Andrei E. Soriano, Steffi Gabrielle O. Dillague, John Jev C. Sablaya and Hameil A. Renabor (National University, Philippines)

Community extension focuses on faculty members' transformative role in societal needs and stakeholder collaborations; however, it relies heavily on face-to-face interaction. Ensuring continuous and comprehensive training for faculty members in higher education institutions (HEIs) as extension workers or community development facilitators is crucial. However, the absence of a precise monitoring and evaluation system challenges the faculty member's contribution to institutional attainment in community engagements. One such solution is to streamline the processing of community engagements and their approvals through automation, which uses the Low-Code Development Platforms (LCDPs). The tracking system was designed according to the developed swimlane process using Microsoft 365 and Power Automate, divided into a Recording and Approval System and a Monitoring System. The system was divided into two flows: NUArCE and NUArCE Go, which were used for both recording with approvals and monitoring. Results showed the system offers a short processing time for evaluating and crediting employee community engagement, with an average of 72.9 seconds for 10 runs for the NUArCE and 22 seconds for 3 runs for the NUArCE Go. Issues and challenges include storage, printable areas, file extension conversion, and bottlenecking due to long user action times. Nevertheless, the tracking system provided the desired output within seconds, ensuring real-time monitoring of faculty members' community engagement summary.

#18 CrOptimize: Integrated Agri-Tech Solution for Optimized Farm Lot Utilization and Resource Management

Hannah Aizel C Garcia, Gilbert P Mendoza, Jake Harold B Roxas and Vianca Dhenise D Vergara (FAITH Colleges, Philippines); Adonis Santos (First Asia Institute of Technology and Humanities, Philippines)

CrOptimize is an integrated agri-tech solution designed to optimize farm lot utilization and resource management by combining geo-tagging, drone surveillance, and soil nutrient monitoring. The system aims to improve agricultural efficiency and sustainability, especially in regions like the Philippines, where food security is a growing concern. Geo-tagging enables accurate measurement of farm areas, ensuring precise seed allocation and minimizing surplus or shortages. Soil nutrient sensors analyze soil composition and guide farmers in selecting suitable crops and planting strategies, enhancing yield potential. Drone surveillance validates seed distribution and continuously monitors crop growth, promoting transparency and accountability in farming practices. This data-driven approach reduces inefficiencies and resource wastage while supporting informed decision-making among farmers and agricultural suppliers. Suppliers also benefit from enhanced supply chain accuracy, enabling better distribution planning and inventory management. Although challenges such as GPS signal limitations, sensor calibration, and drone maintenance may impact deployment, CrOptimize offers a scalable and adaptable framework for modernizing agriculture. By integrating advanced technologies, the system empowers stakeholders to improve productivity and align with global efforts toward sustainable and resilient food systems.

#19 WELLNEST: Fostering Student Well-Being in Academic Settings

Francine Alanysse Amigo, Anjelie Carpio and Vernon Jhon Quilang (National University, Philippines); Alyssa C Vicente (National University Philippines, Philippines); Charlyn A. Malimata (National University, Philippines)

The "WellNest" system is a web and mobile-based platform designed to enhance mental health services in academic settings. It streamlines the core operations of the Guidance Services Office (GSO), automating tasks such as appointment scheduling, user management, and counseling form processing. Developed using an agile approach and an Input-Process-Output (IPO) framework, "WellNest" offers students and employees flexible, confidential access to mental health support. The platform provides tailored features for different user roles, ensuring that administrators, counselors, and users can efficiently access the services and resources relevant to their needs. By integrating digital tools, the system reduces administrative burdens, improves service accessibility, and fosters a more supportive environment for addressing mental health challenges within the university community. Additionally, "WellNest" takes a holistic approach to mental health management, empowering academic institutions to proactively support the well-being of their members and cultivate a healthier, more responsive mental health ecosystem in the academic setting.

#20 Anti-Poaching System Using Wireless Communications and Image Processing for Wildlife Sanctuaries

Irvin Johnson Zari, John Christopher Delos Santos, John Philip Alcala, Alain Bernard Rañola, Bernadeth B Zari, Melannie B. Mendoza and Edwin L. Astorga (Adamson University, Philippines)

Poaching in the Philippines constitutes a critical problem that endangers species survival and inflicts detrimental environmental consequences. Despite the government's efforts to tackle the issue through enhanced legislation and enforcement, the challenge remains. Biak-na-Bato National Park is a protected area in the country impacted by poaching. The objective of the study is to develop a device that combines PIR sensors and cameras to detect human intrusions and alert local authorities. The collected data will be transmitted wirelessly through internet connectivity for distant observation and analysis. A web server from the ESP32 CAM Module was employed to surveil the remote area, and its incorporation into the PyCharm programming environment facilitated image analysis for the identification of unauthorized individuals. Consequently, real-time alerts and alarms were generated simultaneously, allowing users to make informed decisions regarding the need for on-site involvement. These applications served as the primary interface for assessing the security of the remote area safeguarded by the prototypes and the data gathered from the system, utilizing Arduino and Python programming languages. The system's accuracy was assessed utilizing three cameras and ten trials across six variables, yielding results ranging from 83.33 percent to 100 percent. The data transfer rate attained around 9.6 Mbps, with a minor fluctuation of 3.03 percent in device uptime. The technology exhibited absolute reliability in identifying individuals. Moreover, all real-time testing data was systematically archived within the system, meeting the design specifications and providing a feasible solution for combating poaching activities. The results validate the system's dependability in incursion detection, data transfer, and remote monitoring, offering a cost-efficient and scalable alternative to bolster conservation initiatives in protected regions. Their testing confirmed the system's effectiveness in detecting human intrusion, providing data rapidly, notifying authorities through audible alarms, and employing many integrated devices for reliable data transmission.

#21 Design of Dual Band Antenna Using CMA for IEEE 802.11a and Wi-Fi Applications

Sai Debasisa Patra and Sambhudutta Nanda (VIT AP University, India)

This paper presents a dual-narrow-band antenna design at 5.06/5.48GHz is proposed. It has a circular patch at the top layer that is loaded with an SRR-shaped slot. Dual modes are excited to achieve the dual-band, and the antenna's modal behavior is examined using characteristics mode analysis. Based on the modal behavior of the proposed antenna using a characteristic mode analysis (CMA), an antenna operating at dual resonant modes at 4.98GHz and 5.95GHz, respectively is designed for the dual-band operation. The proposed antenna is printed on a low cost FR4 substrate with a size of 25×33×1.6mm3. The full-wave simulation method is used to excite dual modes using a 50Ω coaxial feedline. The simulated reflection coefficient S11≤ 10dB and the maximum gain achieved with peak gain 6.17dBi and 4.13dBi by improving the antenna settings. The bandwidth covers IEEE 802.11a 5G sub-6GHz communication, which corresponds to 5.04GHz and 5.48GHz bands, as well as Wi-Fi (indoor Wi-Fi).

#22 Integrating DWVD Dimensionality Reduction and Bio-Inspired Feature Selection for Accurate Lung Cancer Prediction

Karthika M S (Vellore Institute of Technology, India); Harikumar Rajaguru (Bannari Amman Institute of Technology & ECE, India); Ajin R Nair (Manipal Institute of Technology Bengaluru, MAHE, India)

Early and accurate detection of lung cancer is crucial to designing effective treatments and contributes strongly to increasing survival rates among patients. The study uses a mixed-methods approach for the classification of lung cancer using microarray gene expression data. The issue of high-dimensional data is handled by Discrete Wigner-Ville Distribution (DWVD)-based dimensionality reduction without losing important spectral and temporal gene features. To further optimise the dataset, Optimal Feature Selection is carried out using the Harmonic Search (HS) and Cuckoo Search (CS) algorithms. The reduced and cleaned dataset obtained is then classified using a variety of machine learning algorithms such as Non-Linear Regression (NLR), Softmax Discriminant Classifier (SDC), Gaussian Mixture Model (GMM), Random Forest (RF), and Support Vector Machine with Radial Basis Function kernel (SVM-RBF). Performance is strictly analyzed with different metrics including accuracy, F1 Score, Jaccard Index, Matthews Correlation Coefficient (MCC), and Cohen's Kappa. Out of all the classifiers, the SVM-RBF model produced the highest potential results with accuracy of 98.34%, F1 Score of 98.99%, Jaccard Index of 98.01%, MCC of 0.9425, and Kappa of 0.9423 after HS-based feature selection. These results demonstrate the power of integrating novel signal processing, bio-inspired optimization, and strong classifiers for accurate lung cancer prediction from gene expression data

#23 TerraMori: IoT-Based Autonomous Terrarium for Optimized Growth of Moringa Oleifera

Gregory Hans B. Abundabar, Gen-Ichie Balatbat, Ma. Sopia Alyanna D. Garcia, Mark Angelo C Purio, Joan D. Sta Ana and Melannie B. Mendoza (Adamson University, Philippines)

This paper presents TerraMori, an Internet of Things (IoT)-based sealed terrarium designed for automated cultivation of Moringa oleifera. The system integrates an ESP32 microcontroller with sensors for temperature, humidity, light, carbon dioxide, oxygen, and soil moisture, together with actuators for ventilation, irrigation, misting, and lighting. Sensor readings are processed in real time and mapped to the Blynk IoT platform, enabling both automated regulation and remote monitoring. A four-week evaluation compared two plants grown inside TerraMori with outdoor controls. The system maintained stable internal conditions within target ranges (temperature 26-38°C, humidity 55-75%, soil moisture 30-60 %, CO₂ 450-550 ppm, and O₂ 18-21%). TerraMori plants showed improved growth and more stable leaf color values under controlled conditions. Effect size analysis indicated substantial practical differences, although statistical significance was limited by sample size. Overall, TerraMori demonstrates potential as a sustainable, autonomous cultivation solution for urban and resource constrained environments, with future work focusing on larger trials, longer deployment, and additional objective plant health metrics.

Tuesday, October 28 2:30 - 4:00

Track A2F1 CCI 2.1: Computing & Computational Intelligence (CCI) 2.1

Room: F1. Sipadan I

Chair: Helen Sin Ee Chuo (Universiti Malaysia Sabah, Malaysia)

2:30 DGE2I-Net: Exploring Depth Gait Energy and Entropy-Based Image Features for Human Gait Classification Using Deep Neural Networks

Mainak Ghosh, Sourav Biswas and Anup Nandy (National Institute of Technology Rourkela, India)

Image-based gait analysis has become an important research area for extracting comprehensive features for classification of gait abnormality. However, traditional Image-based models mostly focus on the pattern analysis from the image sequences, which affects the efficiency of the classification model. To overcome this problem, Gait Energy Image and Gait Entropy Image are used as silhouette-based feature extraction methods. But these silhouette-based methods frequently face challenges due to variations in lighting, background, and clothing conditions. To mitigate these challenges, depth information is captured to represent of gait features using Depth Gait Energy Image (DGEI) and Depth Gait Entropy Image (DGEnI). Due to 3D nature of the depth images, these are not affected by the environmental obstacles. We create a depth gait dataset of 10 subjects to evaluate these features with two Convolutional Neural Network based models. We achieve 96.30% and 98.62% accuracy for classification of human gait with DGEI and DGEnI-features respectively, which significantly outperforms many traditional methods.

2:45 Steel Classifications Based on Sparks Patterns Using Convolutional Neural Network ResNet Pre-Trained Models

Ali M. A. Daban (Universiti Technologi Malaysia, Malaysia & Business Media International, Malaysia); Siti Armiza Mohd Aris (Universiti Teknologi Malaysia, Malaysia); Syahid Anuar (Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia)

Steel plays a vital role in numerous industries due to its strength, versatility, and wide-ranging applications. However, its production is highly energy-intensive and a significant contributor to global CO₂ emissions, raising environmental concerns. Recycling steel offers a more sustainable alternative, yet accurately classifying different steel types remains a persistent challenge, especially in resource-limited settings. This study introduces an automated method for steel classification based on the traditional spark test, utilizing pre-trained Convolutional Neural Networks (CNNs). Specifically, ResNet34 and ResNet101 models were trained on spark test images to evaluate their classification performance. ResNet34 achieved a notable accuracy of 95.0%, outperforming the previously used ResNet50 model, which recorded 92.6%. In contrast, ResNet101 significantly underperformed with an accuracy of just 20.6%. These results demonstrate the practical potential of deep learning models, particularly ResNet34, to enhance the efficiency of steel recycling processes, ensure higher material quality, and contribute to environmental sustainability through automated, precise classification.

3:00 Deep Representation Learning with Deep Belief Network for Petrophysical Properties Prediction

Pallabi Saikia (Rajiv Gandhi Institute of Petroleum Technology, India)

Deep neural networks are becoming predominant for the task of discrimination due to its capability to learn complex features. But its advantages get constrained in the scarcity of labelled data, as accomplishing proper representation by the network can be difficult in such scenarios. In this paper, we investigated deep belief network to learn deep representations of regression data, which ultimately behave as the feature detectors in deep neural network model. The model have been explored on a real-world regression problem of petro-physical properties prediction, in the domain of reservoir characterisation. The model leverages abundantly available unlabelled seismic data to learn better representation and such representations are applied in the deep neural network model for guiding the training of regression model in limited labelled seismic. We analyse the performance of the prediction capability of neural network using the feature detectors obtained from deep belief network. The results demonstrate that the model outperforms conventional neural network model in terms of generalised error and Computation units required.

3:15 Domain Knowledge Leveraged Inductive Transfer Learning on Solving a Regression Task

Pallabi Saikia (Rajiv Gandhi Institute of Petroleum Technology, India)

Deep neural networks (DNNs) are widely used for classification tasks due to their ability to learn complex features. However, their effectiveness is constrained in data-scarce scenarios. Transfer learning (TL) has proven useful in handling such limitations by leveraging knowledge from related tasks. While TL is extensively applied in classification, its use in regression remains relatively unexplored. This paper presents a TL-based approach for regression when labeled data is limited. The study focuses on predicting petrophysical properties in reservoir characterization, a real-world regression problem. A ResNet model, pre-trained on a related classification task within the domain, is adapted to regression by transferring learned parameters. This imposes an inductive bias that enhances model generalization. The experimental results demonstrate that the proposed method significantly improves regression performance compared to conventional approaches, particularly in low-data conditions. The findings highlight the potential of TL in improving regression models when data availability is a major constraint.

3:30 Object Detection for Intelligent Vehicles

Gonugunta Sai Prakash (SRM University-AP, India); Rituparna Choudhury (International Institute of Information Technology Bangalore, India)

Intelligent Vehicle System (IVS) is a very lucrative application of artificial intelligence or AI. These intelligent vehicles need to identify or classify objects present in a captured image. The detection latency is a very important factor for these high-speed applications. In real-time scenarios, this object classification or labeling latency should be minimal. In these applications, the accuracy of detection also plays a vital role. So, this paper proposes a hybrid algorithm to reduce detection latency while improving the accuracy of detection. The proposed approach uses a hybrid of feature selection, clustering, and classifiers to achieve the highest accuracy and lowest latency. The results show that the proposed method achieves better performance as compared to other algorithms. It can complete the classification in 2 msec while the detection latency of other deep learning or machine learning techniques is in the range of seconds. This algorithm also has the highest accuracy among all the existing classification models.

3:45 Multidomain HRV Features for the Smart Diagnosis of ADHD Using ML Models

Shafna V and S D Madhu Kumar (National Institute of Technology Calicut, India)

Attention-Deficit/Hyperactivity Disorder (ADHD) is a common neurodevelopmental condition that persists into adulthood and is often challenging to diagnose due to the subjectivity of traditional assessment methods. While neuroimaging and EEG-based approaches have gained popularity in ADHD research, they are often resource-intensive and less accessible. In contrast, physiological signals such as Heart Rate Variability (HRV) offer a non-invasive and cost-effective alternative that remains underexplored, particularly in adult populations. This study investigates the potential of HRV-derived features for distinguishing adults with ADHD from healthy controls using Machine Learning (ML) techniques. Time-domain, frequency-domain, and wavelet-domain features were extracted from RR interval data. To identify the most informative features, we employed Recursive Feature Elimination (RFE) using a Random Forest Classifier, selecting the top five features contributing most to classification performance. Among various models evaluated, the XGBoost classifier achieved the best performance with 91% recall 83% accuracy and f1-score. The results highlight that HRV-based metrics can provide meaningful insights into autonomic nervous system irregularities associated with ADHD in adults. The proposed approach demonstrates a viable path for scalable and accessible ADHD screening using physiological biomarkers. Future work will focus on expanding the dataset and incorporating advanced feature engineering and deep learning for improved accuracy.

Tuesday, October 28 2:30 - 4:00

Track A2F2 CSR 2: Control Systems & Robotics (CSR) 2

Room: F2. 501 Kadamaian

Chair: Fatanah Mohamad Suhaimi (Universiti Sains Malaysia, Malaysia)

2:30 Data-Driven Controllers for Nonlinear Discrete-Time Systems

Koshy George (GITAM University, India)

The emphasis on data-driven control has increased in recent years, even though the concept is over 100 years old. In one class of such controllers, the objective is to identify a model of the controlled plant and then control using any model-based control technique. Alternatively, methods have been introduced to tune the controller's parameters using the available data directly. Apart from these two, methods have evolved recently based on using a simplified mathematical representation of the plant, typically called model-free control (MFC). MFC is attractive as the objective is to control a plant without explicitly using a mathematical model. It is advantageous if the complex process can be controlled using a simpler model that may not wholly represent the entire process. Many successful applications have been reported. In contrast, model-based control based on the mathematical description of the behaviour has been studied and extensively used. This paper addresses the question of which data-driven controller to use in the context of a class of nonlinear discrete-time systems. It compares a method based on partial linearisation with a technique based on feedforward neural networks. The former is a model-free control and requires no prior information about the plant's dynamics. The latter, a model-based control technique with minimal prior information, can provide significantly better tracking performance.

2:45 Evaluating Sensor Fusion Methods for Precise Differential Drive Robot Positioning

Luqmanul Haqeem Bin Saiful Rizal and Saiful Rizal Abdullah (Malaysia); Khairulmizam Samsudin (Universiti Putra Malaysia, Malaysia); Nur Alifah Ilyana Binti Mohd Sharihan (IEEE Region 10 Conference, Malaysia)

This paper presents a comparative study on the odometry and positioning accuracy of a differential drive mobile robot equipped with motor encoders and an onboard Inertial Measurement Unit (IMU). The primary objective is to evaluate and improve localization performance by comparing three methods: basic dead reckoning, a Complementary Filter (CF), and an Extended Kalman Filter (EKF) for sensor fusion. Dead reckoning, while simple and commonly used, is prone to cumulative errors over time, particularly in dynamic or uneven environments. To address this limitation, the CF and EKF are employed to fuse data from the encoders and the IMU, aiming to enhance position and orientation estimation. Experimental evaluations demonstrate that both sensor fusion techniques significantly outperform basic dead reckoning, with accuracy improvements of up to 80%. Among the methods tested, the EKF-based approach delivers the most consistent and precise localization results, making it the most suitable option for applications requiring high-accuracy autonomous navigation.

3:00 Optimal Variable Gain Sliding Mode Controller Using Grey Wolf Optimization for Pitch Stabilization of Twin-Rotor MIMO System

Koteswara Rao Palepogu and Subhasish Mahapatra (VIT-AP University, India); Atanu Panda (Sister Nivedita University, India)

Helicopters play a pivotal role in military and rescue operations, necessitating precise control during critical maneuvers such as hovering, take-off, and landing. Stabilizing multi-rotor systems becomes increasingly challenging in the presence of external disturbances. This work has been implemented in a twin rotor MIMO (TRMS) system that mimics a helicopter model. This study introduces an innovative method for regulating the pitch angle of a twin-rotor MIMO system through a variable-gain sliding-mode controller. The proposed controller integrates a twisting algorithm with error-dependent variable gains, offering improved control efficiency compared to fixed-gain methods. In this work, the proposed controller parameters are optimized using the Grey Wolf optimization algorithm, ensuring accurate tracking and robust performance. Simulation result outcomes validate the capability of the controller to achieve precise pitch tracking under system uncertainties and actuator faults, highlighting its potential for real-world applications. Besides, the robust behaviour is analyzed under various uncertain scenarios to highlight the effectiveness of the proposed control.

3:15 Design and Development of an Indigenous Modular 5-DOF Robotic Arm for Multidisciplinary Tasks

Ashab Farhan Anon (Aviation And Aerospace University Bangladesh, Bangladesh); Md Samiullah Prodhan (Aviation and Aerospace University Bangladesh, Bangladesh); Holyjith Paul Himel (Aviation and Aerospace University Bangladesh & N/a, Bangladesh); Md Tariqul Islam (Aviation And Aerospace University Bangladesh, Bangladesh); Md. Ridoan Hasan (Aviation and Aerospace University Bangladesh, Bangladesh & N/a, Bangladesh); Khandokar Ahosanul Islam Jisan (Aviation And Aerospace University Bangladesh, Bangladesh); Tyseer Ninad, Md. Ehsanur Rahman and Afzal Hossain (Aviation and Aerospace University Bangladesh, Bangladesh)

This paper presents the design and development of an indigenous modular 5-degree-of-freedom (5-DOF) robotic manipulator aimed at diverse applications. The arm is built from locally sourced materials and cost-effective components to ensure affordability without compromising functionality. This research work describes materials selection, mechanical CAD modeling (SolidWorks), and the joint/actuator configuration. The control system uses open-source microcontrollers (e.g., ESP32, Arduino) and is integrated with sensors. Comprehensive simulations (static stress, dynamic motion, thermal and vibration analyses) were performed to validate the design. A prototype was fabricated and tested, demonstrating the expected reach, payload capacity, and positioning precision. Potential applications in industry, aerospace/space missions, healthcare, defense, and education are discussed. The results indicate that the proposed design meets the objectives of affordability and versatility for multidisciplinary use. Its modular architecture and low-cost parts make it suitable for a range of multidisciplinary applications. Overall, the research and innovative work demonstrates the feasibility and benefits of developing locally engineered robotic manipulators for diverse industries.

3:30 Digital Twin-Based Industry-Informed Environmental Quality Management (DTI2EQM)

Brijith Jacob and Santhosh Kumar G (Cochin University of Science and Technology, India)

The global struggle for clean air and water is a fight to secure access to these essential resources for everyone. This battle against pollution, environmental degradation, and the impacts of climate change engages individuals, communities, organizations, and governments worldwide. Urban centers, in particular, face the challenge of pollution, exacerbated by rapid urbanization. To combat this, cities are implementing various strategies, including air quality monitoring, stringent emissions targets, clean air zones, a transition to renewable energy sources, and expanded public transportation. These and other innovative solutions are crucial in the ongoing pursuit of clean air, clean water, and a sustainable environment. DTI2EQM represents a cutting-edge approach to addressing environmental challenges by merging the power of digital twin technology with industry- specific knowledge and data. We propose the design and implementation strategies to develop a smart city digital twin component leveraging existing (for example, LoRaWAN) infrastructure to interface with pollution sensors. The Digital Twin architecture will integrate multiple technologies, including inputs from the sensors, computational components such as machine learning models, ontology-based knowledge graphs, persistence and data repositories, and a comprehensive dashboard as the user interface.

3:45 Wiper Motor Control for Oscillation Suppression of Windshield Wiper Arms Using H-Infinity Synthesis

Tsutomu Tashiro and Sodai Kato (Osaka Sangyo University, Japan)

In this paper, a windshield wiper control and its design method to achieve both basic function of wiper control and oscillation suppression of wiper arms. A wiper model is described as a three-inertia resonance system to realize second mode oscillation characteristics. The control is designed based on H-infinity control theory to track the target motor angle and suppress the oscillation of the angular speeds of the driver's side arm and passenger's side arm. Verification is carried out with a wiper test rig using five types of rubber blades with different deterioration levels under a wide range of rainfall conditions. The effectiveness of the proposed control is demonstrated by comparing with the results of the conventional control designed based on optimal regulator theory. The experimental results are investigated from the viewpoints of suppressing the second mode oscillation and the accuracy of the reversal motor angle which determines the wiping range of the arms.

Tuesday, October 28 2:30 - 4:00

Track A2F3 PES 2: Power, Energy & Electrical Systems (PES) 2

Room: F3. 502 Mesilau

Chair: Shamsul Aizam Zulkifli (UTHM, Malaysia & Universiti Tun Hussein Onn Malaysia, Malaysia)

2:30 Enhancing WLS State Estimation Using Future Load Profile Nomination (FLPN)

Earl Humprey M Bantug and Abdul Aziz G. Mabaning (Mindanao State University - Iligan Institute of Technology, Philippines)

Traditional pseudo-measurements often rely on operator forecasts, which lack consumer intent and adaptability. This paper proposes an enhanced Weighted Least Squares (WLS) State Estimation framework that integrates Future Load Profile Nominations (FLPNs), forward-declared load expectations with consumer-declared confidence levels, to improve estimation accuracy in low-SCADA or distribution-level networks. Unlike static forecast-based methods, FLPNs are modeled as inverse-variance-weighted pseudo-measurements, enabling direct consumer participation in grid monitoring.

The framework is evaluated on the IEEE 14-bus test system in two configurations: (1) FLPN-only, where SCADA data at participating buses is fully replaced, and (2) FLPN-augmented, where both SCADA and FLPN data are fused. A 4 by 4 sensitivity analysis across FLPN participation (25-100%) and accuracy levels (25-100%) shows that the FLPN-augmented mode consistently achieves the lowest voltage RMSE across most conditions, achieving up to 19.3% lower voltage RMSE than FLPN-only.

These findings demonstrate that integrating participatory declarations significantly enhances estimation accuracy and supports scalable, consumer-integrated grid operations. The study establishes a foundation for future extensions to dynamic and distributed estimation frameworks, advancing intelligent grid monitoring aligned with SDGs 7, 9, and 11.

2:45 Enhanced Bad Data Detection in Power System State Estimation Using Modified CUSUM with Future Load Profile Nomination (FLPN)

Franclein L. Francisco and Abdul Aziz G. Mabaning (Mindanao State University - Iligan Institute of Technology, Philippines)

Power system state estimation is highly sensitive to the quality of input data, particularly in distribution grids where consumer-driven load variability can introduce significant discrepancies between expected and actual power usage. Traditional residual-based detection methods, including classical cumulative sum (CUSUM), often misclassify these natural fluctuations as anomalies due to their reliance on static baselines and lack of normalization. This study proposes an enhanced anomaly detection framework that integrates Future Load Profile Nominations (FLPNs) into a modified two-sided CUSUM structure. The method evaluates normalized residuals relative to time-aware, user-declared load expectations, and incorporates drift compensation to suppress the accumulation of small benign deviations. Simulation results on the IEEE 14-bus system across 100 trials revealed that the Modified CUSUM with FLPN achieved up to 82% reduction in false alarm rate and up to 69% reduction in missed detection rate compared to traditional CUSUM (without FLPN) approach. This underscores the efficacy of integrating FLPNs and normalization in enhancing anomaly detection performance under dynamic consumer load behavior.

3:00 Future Load Profile Nomination (FLPN) in PV-BESS-EV Microgrids: A Philippine Use Case

Jeric Cesar Aquino Enriquez and Abdul Aziz G. Mabaning (Mindanao State University - Iligan Institute of Technology, Philippines)

Modern power systems face challenges amidst the increased integration of distributed energy resources such as photovoltaic (PV) systems, battery energy storage systems (BESS), and electric vehicles (EVs), which introduce complexity and uncertainty of demand profiles. Hence, this study proposes an optimization framework incorporating Future Load Profile Nominations (FLPN) as proactive, consumer-declared forecasts for energy dispatch planning, enabling a Proactive Energy Management (PEM) approach to grid operations. The IEEE 14-bus system is used as a testbed, with PV, BESS, and EV assets scaled proportionally to static loads at each bus. Dispatch optimization is performed under Philippine time-of-use pricing using a Monte Carlo approach to model FLPN uncertainty. Three scenarios are evaluated: (i) baseline dispatch without FLPN, (ii) FLPN-assisted dispatch with flexible EV charging, and (iii) FLPN-assisted dispatch with bidirectional Vehicle-to-Grid participation. Results show that while FLPN visibility alone yields limited operational benefits, its integration with flexible and controllable EVs achieves substantial improvements, reducing dispatch costs by up to 6.8% and lowering grid dependency up to 3%. This approach aims to contribute to achieving United Nation Sustainable Development Goals (SDGs) 7 (Affordable and Clean Energy) and 13 (Climate Action) by promoting proactive, more resilient, and consumer-integrated energy management strategies.

3:15 Privacy-Preserving Data Transformation Using Hybrid Signal Component Analysis for Forecasting Incorporating Future Load Profile Nomination

Perly Rica U. Flores and Abdul Aziz G. Mabaning (Mindanao State University - Iligan Institute of Technology, Philippines)

This study introduces a novel privacy-preserving data transformation framework for load forecasting that uses hybrid signal component analysis (SCA) techniques. Addressing the growing need for accurate energy predictions while protecting consumer privacy, the framework incorporates Future Load Profile Nomination (FLPN) with Load Profile Data (LPD) prior to transformation. A variety of basic and SCA-based methods including Principal Component Analysis (PCA), Independent Component Analysis (ICA), and Wavelet Transform (WT) were tested, both individually and in hybrid combinations, to address the challenge of balancing privacy and forecasting accuracy. Evaluation showed that WT-based combinations significantly boost privacy, achieving a privacy Mean Squared Error (MSE) as high as 4.680, but this severely decreased forecasting accuracy to an MSE of 4.786. These findings show that hybrid transformation methods can be systematically evaluated to optimize the trade-off between utility and privacy. This transformation-based approach offers an alternative to methods like Federated Learning and Differential Privacy, contributing to reliable, accurate, and privacy-respecting power systems aligned with SDG 7 and 11.

3:30 Proactive Energy Management Through Demand-Side Forecasting: A Future Load Profile Nomination Approach

Abdul Aziz G. Mabaning (Mindanao State University - Iligan Institute of Technology, Philippines)

The Future Load Profile Nomination (FLPN) framework is introduced as a novel approach to enhance power system load forecasting by enabling proactive participation from the demand side. As a specific application of the broader concept of Future Behavior Nomination (FBN) within power systems, FLPN addresses a key limitation of traditional forecasting methods, which depend primarily on historical and real-time data and often fail to anticipate deviations driven by future-oriented consumer behavior. FLPN empowers consumers to voluntarily nominate their anticipated electricity consumption in advance, effectively shifting the demand side from reactive to proactive. Mathematical models are developed at both the individual and system-aggregated levels, incorporating dynamic parameters such as participation rates, forecast blending, and time-varying accuracy. A simulation of a non-historical load anomaly validates the framework, demonstrating a significant reduction in forecast error even with imperfect user input. FLPN contributes to the broader objective of Proactive Energy Management (PEM) and directly supports the United Nations (UN) Sustainable Development Goals (SDG) by promoting efficient grid operations that reduce reliance on carbon-intensive spinning reserves (SDG 13), increase the hosting capacity for renewables (SDG 7), and empower consumers for responsible consumption (SDG 12).

3:45 Application of Combined GWO-HHO Techniques for Minimization of Energy Consumption Cost in Home Energy Management System

Avni Bagga and Shreya Ranjan (Vellore Institute of Technology, India); Prabhakar Karthikeyan Shanmugam (VIT University, India)

An increase in global energy requirements, coupled with growing environmental concerns, has led to a heightened focus on efficient energy management systems, specifically in residential settings. Home Energy Management Systems (HEMS) have emerged as a promising solution to optimize energy consumption, reduce costs, and minimize environmental impact. These systems utilize advanced technologies and algorithms to monitor, control, and manage energy usage within households. Recent advancements in optimization techniques have introduced novel approaches to enhance the performance of HEMS. This paper explores the application of two powerful metaheuristic algorithms, the Grey Wolf Optimizer (GWO) and the Harris Hawks Optimization (HHO) algorithm. The objective of this paper is to minimize the energy consumption cost and simultaneously satisfy the operational constraints of residential appliances, which include demand limits, minimum usage frequency, and energy requirements. To meet the constraints, penalty functions are inculcated. The GWO, inspired by the social hierarchy and hunting behavior of grey wolves, and the HHO, which mimics the cooperative hunting strategy of Harris hawks, have shown promising results in optimizing the energy consumption cost by scheduling the loads effectively.

Tuesday, October 28 2:30 - 4:00

Track A2F4 ECD 2: Electronics, Circuits & Devices (ECD) 2

Room: F4. 503 Dinawan

Chair: Mazlina Mamat (Universiti Malaysia Sabah, Malaysia)

2:30 Frequency Dependence of Electron Emission Current from Crystal Defects in AlGaN/GaN HEMTs

Hideki Hoji, Soichi Sano, Junya Takeda and Hirohisa Taguchi (Chukyo University, Japan)

In this study, we investigated the drop in the drain current value of an AlGaN/GaN HEMT immediately after the collapse phenomenon occurred. Analysis of the IV characteristics clarified that this occurrence was owing to electrons being trapped by the current collapse phenomenon, causing a drop in the electron concentration. By applying a frequency that suppresses the current collapse phenomenon from the gate electrode side to the AlGaN-GaN crystal layer, we measured the process in which electrons were released from crystal defects in the AlGaN-GaN crystal layer. The frequency dependence of the carrier emission process was measured at 1 to 4 GHz, and we confirmed that the higher the frequency, the slower the carrier emission and the higher the saturated drain current value. This is because the time taken for electrons to be trapped in crystal defects and the process time for electrons to be emitted differ depending on the crystal defect. In crystal defects with long emission times, electrons are repeatedly emitted and recaptured even when the current collapse phenomenon is suppressed. Consequently, electrons cannot be recaptured at high frequencies, and the saturated drain current value increases. By comparing the carrier emission process at different frequencies, the components of crystal defects that cannot emit or capture electrons can be extracted. Crystal defects can be evaluated by comparing the carrier emission process at different frequencies.

2:45 Relationship Between Kink Phenomenon and Crystal Strain in AlGaN/GaN HEMT Under High Voltage

Soichi Sano, Junya Takeda, Hideki Hoji, Sho Nagai and Hirohisa Taguchi (Chukyo University, Japan)

In this study, we confirmed the dependence of voltage stress and temperature on the I-V characteristics of AlGaN/GaN high-electron mobility transistors (HEMTs) immediately after applying voltage stress. The high voltage stress and high-temperature environment intensified the kink occurrence, likely due to the expansion of the crystal strain. Results showed that, as the voltage stress increases, a crystal strain occurs in the GaN or AlGaN layer, which affects carrier transport. It has been reported that when the drain voltage is lower than 6 V, two dimensional electron gas (2DEG) is not stably formed; thus, carrier scattering occurs due to crystal distortion. Meanwhile, 2DEG stably forms above 6 V; hence, the influence of distortion is assumed to be minimal. The inflection point of the drain conductance is further calculated based on the I-V characteristics obtained from the kink phenomenon. This inflection point suggests a transition from 3D drift motion to 2DEG carrier transport. The impact of lattice scattering becomes more noticeable at drain voltages under 6 V in the I-V characteristics, where three-dimensional transport is dominant. The lattice scattering is confirmed to be relatively mitigated at drain voltages above 6 V in the I-V characteristics, where two-dimensional transport by the 2DEG is prevalent. It is reported that the lattice vibration of crystal strain due to temperature rise saturates above a certain temperature, and electron scattering also gets limited. When a certain temperature is exceeded under high electric field stress, the drain voltage, which is the inflection point, no longer changes. This is because the amount of crystal distortion due to temperature saturates and no longer affects the 2DEG.

3:00 Influence of Thermal Fluctuations on the Inverse Piezoelectric Effect in AlGaN/GaN HEMTs

Junya Takeda, Soichi Sano and Hirohisa Taguchi (Chukyo University, Japan)

With the growing demand for high-performance, high-frequency electronic devices, the development of devices based on gallium nitride (GaN) has garnered increasing interest. In this study, we examined the current-voltage characteristics of AlGaN/GaN high-electron-mobility transistors subjected to electrical stress. Stress application induced a kink effect, which became more pronounced with increasing gate reverse-bias stress and stress application duration. This observation indicated that local lattice strain, caused by the inverse piezoelectric effect, degraded the device performance. When combined with lattice vibrations owing to heat, this strain led to further degradation. However, the combined effect eventually reached saturation. Moreover, lattice vibrations likely suppressed electron trapping and strain during stress application, resulting in a nonlinear performance degradation trend. The inverse piezoelectric effect was sensitive to thermal fluctuations, resulting in instability. Furthermore, transient response measurements of the drain current revealed a long-term current recovery process, indicating a variation in the interaction between polarization and thermal fluctuations.

3:15 Design of Highly Stackable Charge Trap-Based 3D DRAM

Hyeongyu Kim (Jeonbuk National University, Korea (South)); Dabok Lee (Gyeonsang National University, Korea (South)); Hyun-Sik Choi (Kwangwoon University, Korea (South)); Yoojin Seol (Jeonbuk National University, Korea (South)); Jonghyeon Ha (Gyeongsang National University, Korea (South)); Kihyun Kim (Jeonbuk National University, Korea (South)); Jungsik Kim (Gyeongsang National University, Korea (South)); Won-Ju Cho (Kwangwoon University, Korea (South)); Zvi Or-Bach (MonolithIC 3D, USA); Sungil Chang (MonolithIC3D, Korea (South))

In this work, we propose a highly stackable Charge Trap-based 3D DRAM (CT 3D DRAM) structure that addresses key challenges in future memory scaling, including 3D integration, power consumption, and thermal management. Unlike conventional DRAM architectures that rely on complex capacitor structures, the proposed CT 3D DRAM utilizes a simple 1T memory cell with a poly-Si channel and Schottky barrier source/drain (S/D) contacts formed by metal silicide. Hot carrier injection (HCI) from the source side enables fast program operations through an ultrathin tunnel oxide. Key device parameters were optimized using 3D TCAD simulations, and planar CT DRAM devices were fabricated to validate the concept. The fabricated devices exhibited a program/erase window larger than 1 V under a 20 ns pulse, excellent retention characteristics exceeding 10 seconds at 85 °C, and endurance up to 10¹⁵ cycles with a remaining threshold voltage window of approximately 0.32 V. Moreover, the use of metal S/Ds significantly enhances heat dissipation and enables superior thermal management, critical for highly stacked 3D memories. The vertical integration of metal bit lines (BLs) and horizontal poly-Si channels results in lower RC delays, making the CT 3D DRAM scalable even beyond a thousand layers while maintaining effective cell area comparable to conventional 4F² DRAMs. Through the optimized design of the word line (WL) and bit line (BL) structures, as well as control of key materials such as the tunnel oxide and charge trap nitride, we demonstrate that CT 3D DRAM can achieve both high speed and reliability. This architecture offers a promising solution for next-generation 3D DRAM applications requiring high density, low power, and efficient thermal management, particularly in emerging memory platforms like Compute Express Link™ (CXL™) memory.

3:30 Bitstream-Level IO Tampering: Exploiting FPGA Bitstreams to Compromise ADC Integrity in Cyber-Physical Systems

Nur Alifah Ilyana Binti Mohd Sharihan (IEEE Region 10 Conference, Malaysia); Khairulmizam Samsudin (Universiti Putra Malaysia, Malaysia); Shaiful Hashim (UPM, Malaysia); Faisul Arif Ahmad (Universiti Putra Malaysia & Faculty of Engineering, Malaysia)

As Field-Programmable Gate Arrays (FPGAs) are increasingly deployed in mission-critical systems, they have become attractive targets for low-level hardware attacks. This paper presents a novel bitstream-level tampering methodology aimed at disrupting the functionality of FPGA-configured Analog-to-Digital Converter (ADC) applications by manipulating peripheral behavior. The proposed attack operates at post-bitstream generation, assuming adversarial access to the unencrypted bitstream file and the ability to reprogram the target device. Two distinct attack models are demonstrated: (i) Hardware-Level Denial-of-Service (HLDoS) attacks, which disable the ADC's conversion trigger by reverting its control port to a weak pull-up state, thereby halting successive data acquisition; and (ii) Digital Port Tampering (DPT) attacks, which corrupt data integrity by altering specific I/O bits associated with the ADC output interface. Experimental validation on an Intel Cyclone V FPGA confirms the viability and severity of these attacks, revealing both total functional loss and systematic bit-level corruption. The findings underscore a critical security gap in FPGA-based systems and motivate the need for robust bitstream authentication and runtime validation mechanisms.

3:45 Hardware Software Co-Design of 2D Modulation Schemes OTFS and OTSM on System-on-Chip

Sai Kumar Dora and Rakesh Kumar Yadav (IIT ISM Dhanbad, India); Himanshu Bhusan Mishra (IIT (ISM) Dhanbad, India); Amitav Panda (Nokia Solutions and Networks India Pvt Ltd, India)

In this paper, we design low-complexity hardware architectures for the basic modules of the two-dimensional (2D) modulation scheme, orthogonal time sequency multiplexing (OTSM). OTSM scheme works in the delay-sequence domain by using the basic modules inverse Walsh-Hadamard transform (IWHT) and Walsh-Hadamard transform (WHT) at the transmitter and receiver, respectively. We next compare the performance of the proposed architectures of the above-mentioned basic modules with that of its counterpart modules of the another 2D modulation technique Zak based orthogonal time frequency space (OTFS). Note that Zak-OTFS operates in the delay-Doppler domain, requiring the primary modules as 2D inverse Zak (IZak) and Zak transforms at the transmitter and receiver, respectively. This comprehensive comparative analysis is conducted on the computational complexity, timing performance, and power consumption of both schemes, evaluated on the ZCU706 Zynq SoC board. The results indicate that OTSM outperforms the Zak-OTFS in terms of area, power consumption, and latency. Zak-OTFS requires more programmable logic (PL) resources, utilizing 28,879 LUTs and 26,124 FFs, while OTSM uses significantly fewer resources, with 5,440 LUTs and 6,216 FFs.

Tuesday, October 28 2:30 - 4:00

Track A2F5 CS 2: Communication Systems (CS) 2

Room: F5. 504 Madai

Chair: Nur Idora Abdul Razak (Universiti Teknologi MARA, Malaysia)

2:30 Deep Reinforcement Learning-Based Dynamic Sharding for Blockchain IoT

Pooja Khobragade and Ashok Kumar Turuk (National Institute of Technology Rourkela, India)

Internet of Things (IoT) and Blockchain integration are having a huge impact on the future of technological progress. IoT has progressed from an emerging notion to a widely used technology, shaping the future of digital connectivity. With billions of networked IoT devices generating large amounts of data, efficient data management becomes critical. Blockchain has been explored extensively to enhance security in IoT networks however, its scalability limitations become evident when handling large-scale deployments. Sharding is recognized as a promising approach to improve blockchain scalability by partitioning the network into multiple independent groups. These groups, called shards, process transactions in parallel, increasing throughput while reducing communication, computation, and storage overhead. Despite its advantages, many existing blockchain sharding models rely on static algorithm, which fail to accommodate the dynamic nature of blockchain networks. Factors such as variable node involvement and possible security concerns present problems that static sharding cannot solve. To address these restrictions, deep learning provides a strong solution for dynamic and multidimensional sharding in blockchain-based IoT systems. Deep learning, with its capacity to understand complex patterns and adapt to changing network circumstances, can improve the efficiency, security, and scalability of blockchain-powered IoT networks. This article proposes a deep reinforcement learning-based dynamic shards in blockchain IoT applications to overcome scalability difficulties.

2:45 Fisheye Camera-Aided Standalone IRS Control for Transmitter Beamforming

Yoshihiko Tsuchiya (Tokyo University of Science, Japan); Norisato Suga (Shibaura Institute of Technology & ATR, Japan); Kazunori Uruma (Kogakuin University, Japan); Masaya Fujisawa (Tokyo University of Science, Japan)

Intelligent reflecting surface (IRS)-aided wireless communication is attracting interest as a technology that can improve communication quality and coverage in high-frequency bands such as millimeter waves. The proper control of the IRS requires channel estimation for each element, which reduces communication efficiency owing to a channel estimation overhead. Furthermore, when an IRS is used in multiple-input multiple-output (MIMO) systems, the overhead becomes larger than that of single-input single-output (SISO) systems. To control the IRS according to the beamforming of the transmitter, the IRS must cooperate with the transmitter. This requires a channel estimation of the number of elements for each transmitter antenna. Therefore, a standalone IRS that does not require a connection or channel estimation for cooperation between the IRS and transmitter is required. Recently, standalone IRS control in SISO systems using camera images has been proposed. In this paper, we propose a method to adapt this concept to transmitter beamforming. Our method achieves a standalone IRS by predicting the channel based on the 3D position estimated by detecting the user in the camera image and then applying the reflection coefficients corresponding to the estimated beamforming vectors. Numerical experiments confirm that the proposed method can control the IRS with only a slight degradation in communication quality.

3:00 Enhancing VLC Performance: an Experimental Study on System Parameter Tuning

Dilanka de Silva (University of Moratuwa, Sri Lanka & Sri Lanka Technology Campus (Pvt) Ltd, Sri Lanka); Ruwan Weerasuriya (Chalmers University of Technology, Sweden); Sumudu G. Edirisinghe (Sri Lanka Technological Campus, Sri Lanka); Madushanka Nishan Dharmaweera (University of Sri Jayewardenepura, Sri Lanka); Samiru Gayan (University of Moratuwa, Sri Lanka)

This research presents a comprehensive experimental and simulation-based investigation aimed at enhancing the link distance and performance of indoor Visible Light Communication (VLC) systems. A basic VLC setup was incrementally improved by optimizing key system parameters, including LED transmit power, transimpedance amplifier (TIA) feedback resistor, lens configuration, photodiode selection, and wall color. Each enhancement was experimentally validated, and corresponding simulations were conducted to analyze system behavior and verify measurement results. The study also examines the applicability of well-established VLC channel models, such as the Masao Nakagawa model and second-reflection models, in real-world scenarios. The findings confirm that incorporating optical and environmental optimizations can significantly extend link distance, up to 3 m with high accuracy, while aligning closely with simulation predictions. This work not only validates theoretical models but also provides practical design insights for reliable, long-range VLC deployment in indoor environments. The results demonstrate the importance of iterative experimental tuning in bridging the gap between theoretical assumptions and actual VLC system performance.

3:15 A Web-Based Testbed with Spatial Visualization Capabilities for IoT-Based Smart Systems

Josef Isaac Babaran, Ivan Blaise Gonzales, Julius Brian Ipac and Lord Peter Robin Dustin Suyat (University of the Philippines - Diliman Campus, Philippines); Paul John C Tiope, Adrian Cahlil Eiz Togonon and Jaybie A. de Guzman (University of the Philippines Diliman, Philippines)

In the world of the Internet of Things, testbeds offer the necessary infrastructure in which ideas, technologies, and practices are tested and validated. A smart classroom called Smart-iLAB has been previously developed to showcase advances in electronics laboratory instruction, and is equipped with IoT-based sensing and actuation features. However, the Smart-iLAB did not offer the infrastructure required for users to conduct experiments and tests using the available sensors and actuators. In this work, we established a REST-based API testbed for the Smart-iLAB's IoT platform leveraging on MQTT connections, python scripts, a SQL-based database, and a RESTful API. In addition, a digital twin has been developed to provide immediate visual feedback of the Smart I-LAB environment. The REST-based API testbed, with its accompanying digital twin, is hosted on a web-based platform that enables users to interact with the sensors and actuators of the Smart-iLAB externally. The REST-based API testbed can also be used to facilitate the development of scripts, from simple manipulation to complex automation. In addition, the visualization of the layout of the available sensors and actuators serves as a convenient way for users to maximize the capabilities of the smart system remotely. To illustrate its effectiveness, the REST-based API testbed was tested to assess its latency, scalability, and reliability.

3:30 Performance of Slotted ALOHA Systems with Successive Interference Cancellation and Feedback over Nakagami-m Fading Channels

Daiki Fukui, Yuhei Takahashi, Ryo Ozaki, Tomotaka Kimura and Jun Cheng (Doshisha University, Japan)

This paper explores the optimization of transmission probability and code rate in multi-device slotted ALOHA systems employing successive interference cancellation (SIC) and feedback over Nakagami-m fading channels. Although previous research has derived the optimal probability and the code rate to maximize the sum rate in a two-device scenario analytically using Markov process, its extension to configurations with more than two devices remains challenging. The complexity of Markov modeling arises from two main challenges: 1) an increase in the number of devices greatly expands the state space, precluding the analytical determination of system throughput; 2) deriving transition probabilities in the Nakagami-m channel model is difficult. In this study, we use computer simulations to evaluate the sum rate and to search for the optimal transmission probability and code rate to maximize the sum rate of the systems. Our simulation findings reveal that at an average SNR the optimal code rate is independent of the number of devices and transmission probability. In a 30-device slotted ALOHA system with an SNR of 15 dB, the maximum sum rate of 2.9235 is almost achieved at a transmission probability of 0.0665 and an optimal code rate of approximately 4.02, regardless of the number of devices.

3:45 Enhancing Virtualization Security Through System Call-Based Anomaly Detection in Containers

Jie Zhang, Kuan-Chieh Wang, Po-Kai Hsu, Jhen-Jie Hsieh, Po-Shen Chen, Tze-Rong Jian, Kun-Hsiang Huang and Min-Te Sun (National Central University, Taiwan); Chun-Ying Huang (National Yang Ming Chiao Tung University, Taiwan)

In the current era of micro-services, containerized applications face unprecedented security challenges due to shared kernels and limited isolation. This research proposes a container security framework based on monitoring system call sequences to detect anomalies in containers providing various micro-services. We introduce a custom dataset named XXXX, which captures system call sequences behavior in containers running the micro-services and simulating attacks. The framework includes real-time system call monitors, parsers, dashboards, and an unsupervised anomaly detection model using unsupervised learning with autoencoders to enhance the detection capability of unknown vulnerabilities. It leverages containerization benefits - simplicity, scalability, and automation. Our evaluation emphasizes false alarm rate and average detection time. Results show that the attack detection performance of most containers meets expectations, though the detection time of one subset had slightly longer detection time due to the intrinsic complexity of vulnerabilities. This work offers valuable insights for improving container security in micro-service systems.

Tuesday, October 28 2:30 - 4:00

Track A2F6 CCI 2.2: Computing & Computational Intelligence (CCI) 2.2

Room: F6. 505 Sepilok

Chair: Siti Noraini Sulaiman (Universiti Teknologi MARA, Malaysia)

2:30 AI-Based Modeling of a Microwave Elliptical Sensor for Dielectric Measurement of Agricultural Samples

Kim Yee Lee, Yong-Hong Lee, Gobi Vetharatnam and Eng Hock Lim (Universiti Tunku Abdul Rahman, Malaysia); Cheng Ee Meng (Universiti Malaysia Perlis, Malaysia); Kok Yeow You (Universiti Teknologi Malaysia, Malaysia)

This study presents an AI-driven modeling approach for a microwave elliptical sensor used in dielectric measurements. Traditional analytical and numerical methods often face challenges such as high computational complexity, calibration difficulties, and reduced accuracy at higher frequencies, particularly with irregular sensor geometries like elliptical probes. To address these limitations, models were trained using reflection coefficient (S11) data from simulations across 0.5-8 GHz. Three techniques were evaluated: Multiple Linear Regression (MLR), Random Forest (RF), and artificial neural networks using Levenberg-Marquardt (ANN-LM). Performance analysis involved varying dataset sizes and input feature configurations (2-input and 3-input). The trained models were then tested on real agricultural samples and compared with results from a standard open-ended coaxial measurement. The findings show that the proposed AI models accurately predict the complex permittivity of samples under test, enhancing modeling efficiency and adaptability, especially for sensors with non-standard geometries. This capability supports real-time dielectric characterization in agricultural applications.

2:45 A Transfer Learning Based Decision Level Multimodal Framework for Continuous Sign Language Recognition

Navneet Nayan (Department of Computational Intelligence, SRM Institute of Science and Technology, KTR Campus, Chennai, Tamil Nadu, Indi); Debashis Ghosh (Indian Institute of Technology (IIT) Roorkee, India); Pyari Mohan Pradhan (IIT Roorkee, India)

Multimodal frameworks have appeared as a potential solution to achieve breakthrough results in the field of sign language and hand gesture recognition. In this paper, we propose a classifier combination based multimodal framework for continuous sign language recognition. In this work, we propose to use transfer learning to perform classification task on individual modalities. Further, we apply majority voting scheme to combine all the individual classification performances to obtain the final classification accuracy. For transfer learning, pre-trained deep neural networks like GoogleNet, MobileNet-v2 and EfficientNet-b0 are used independently from one another. We applied these networks independently on individual modalities of IPN Hand dataset and combined the classification results obtained on these modalities to obtain the final classification result. IPN Hand dataset is one of the most challenging and dynamic continuous hand gesture datasets. Among the used pre-trained networks, EfficientNet-b0 appeared as the best network in terms of accuracy whereas GoogleNet took the least computational time during classification. On this dataset, our proposed approach performs exceptionally well with individual modalities. After combining the classification results of individual modalities according to our employed algorithm, it is observed that our proposed approach performs better than the earlier reported results. Our method surpasses the reported benchmark performance as well as performs superior to many state-of-the-art results.

3:00 Multimodal Approach Based Sentence Level Sign Language Synthesis

Navneet Nayan (Department of Computational Intelligence, SRM Institute of Science and Technology, KTR Campus, Chennai, Tamil Nadu, Indi); Debashis Ghosh (Indian Institute of Technology (IIT) Roorkee, India); Pyari Mohan Pradhan (IIT Roorkee, India)

In this paper, we present a multimodal sentence level sign language synthesis system. Sentence level sign language synthesis requires synthesis of signs as well as synthesis of transition segments between the signs. In this paper, our focus is to develop. an efficient transition segment between two signs. For this, we propose to use edge features and trajectory features obtained from the transition segment of the query sign sentences. Edge features are extracted using the morphological operations on the hand images, whereas trajectory features are obtained from centroid detection of consecutive hand images. From trajectory, we obtain the direction of motion and shape and co-ordinates of the trajectory. Further, with the help of interpolation techniques we synthesize the hand gestures between the two signs. The correctness of the synthesis is analyzed and modified with the help of edge features and trajectory features. Our proposed approach is tested on some Indian Sign Language phrases and sentences. The proposed method is evaluated based on the mean opinion scores obtained from several users. Evaluation was based on three criteria, namely clarity in understanding, smoothness of the generated videos and similarity to the original sign videos. We obtained decent mean scores and encouraging feedbacks from the users.

3:15 Res-SH: Unbiased Residual Learning for Self-Healing Interface Toughness Prediction with Limited Data

Pei Sze Tan (Monash University, Malaysia Campus, Malaysia); Karen Koh (Monash University, Australia); Sailaja Rajanala and Arghya Pal (Monash University, Malaysia); Raphael C.W. Phan (Monash University, Malaysia Campus, Malaysia); Ze Nan (Xidian University, China); Chee-Ming Ting (Monash University, Malaysia); Fuad Noman (Monash University Malaysia, Australia); Norfadilah Dolmat, Nik Nur Wahidah Nik Hashim and Afidalina Tumian (Petronas Research Sdn Bhd, Malaysia); Nik Nur Wahidah Nik Hashim (International Islamic University Malaysia, Malaysia)

The development of self-healing materials is often hindered by the high costs and material waste associated with traditional characterization methods. Current approaches to toughness prediction, primarily based on convolutional neural networks (CNNs), are limited by their tendency to capture only surface-level features, which can lead to biased predictions. Moreover, working with small datasets, which is common in materials science, further increases the risk of biased training due to overfitting, posing a critical challenge to the reliability and generalizability of predictive models. This study introduces an unbiased residual learning framework designed explicitly for predicting self-healing interface toughness under limited-data conditions. Our approach, ResNet-inspired approach for predicting self-healing material toughness, named Res-SH, used the power of residual networks to capture deeper, more complex patterns in the data, thereby addressing critical challenges in materials research. Res-SH minimises resource consumption and experimental overhead by focusing on unbiased learning, achieving accurate predictions with fewer training epochs and lower R^2 score and root mean square prediction errors compared to conventional CNN and lightweight model MobileNetv2. This novel framework provides a cost-effective and resource-efficient alternative to traditional material characterization methods, reducing material waste and accelerating the discovery and optimization of self-healing material systems.

3:30 Enhanced CNN Models for Accurate Classification of Calcification Patches in Breast Cancer Detection Using DBT Images

Syafiqah Aqilah Saifudin (Universiti Teknologi MARA Cawangan Pulau Pinang, Malaysia); Siti Noraini Sulaiman (Universiti Teknologi MARA, Malaysia); Muhammad Khusairi Osman (Universiti Teknologi Mara (UiTM), Malaysia); Iza Sazanita Isa (Universiti Teknologi Mara, Malaysia); Mohd Firdaus Abdullah (Universiti Teknologi MARA, Cawangan Pulau Pinang, Malaysia); Noor Khairiah A. Karim and Nor Ashidi Mat Isa (Universiti Sains Malaysia, Malaysia); Slamet Riyadi and Yessi Jusman (Universitas Muhammadiyah Yogyakarta, Indonesia)

Breast cancer remains one of the most common and serious health concerns worldwide, particularly among women. Early detection substantially decreases death rates, which has fuelled the development of deep learning-based medical imaging analysis. This research focuses on recognizing calcification patches in Digital Breast Tomosynthesis (DBT) images using Convolutional Neural Network (CNN) architectures. To evaluate classification performance, several CNN architectures are compared, including ResNet-18, SqueezeNet, GoogleNet, as well as their modified versions. The enhanced models incorporate additional convolutional layers to improve feature extraction and classification of DBT patches. Specifically, the Modified ResNet-18, Modified SqueezeNet and Modified GoogleNet represent enhanced versions of the original architecture, with integrated designs to capture multi-scale features specific to DBT images and improve sensitivity to subtle calcifications. Experimental results show that the modified GoogleNet outperforms the conventional and other experimented networks, with accuracy, sensitivity, precision, F-Measure, and Jaccard Index values of 95.91%, 92.63%, 90.79%, 91.29%, and 0.844, respectively. Although numerically modest, these gains are clinically meaningful, as even small increases in sensitivity can translate into earlier detection of additional cases across large screening populations. This work highlights the potential of tailored CNN architectures to improve diagnostic accuracy in DBT imaging and support radiologists in early breast cancer detection.

3:45 Analysis of a Regression-Based Feature Set for Classifying Lung Lesion and Non-Lesion Regions in CT Scans

Nurul Najiha Jafery ('None', Malaysia); Siti Noraini Sulaiman (Universiti Teknologi MARA, Malaysia); Muhammad Khusairi Osman (Universiti Teknologi Mara (UiTM), Malaysia); Noor Khairiah A. Karim (Universiti Sains Malaysia, Malaysia); Zainal Hisham Che Soh, Ir (Universiti Teknologi MARA, Malaysia); Iza Sazanita Isa (Universiti Teknologi Mara, Malaysia)

Lung lesion classification in CT scans is critical for the early diagnosis of lung cancer and other pulmonary diseases. Accurate distinction between lung lesion and non-lesion regions can significantly support clinical decision-making. Existing feature extraction methods often rely on conventional image characteristics, which may not fully capture subtle shape variations or inter-slice dynamics. This study introduces a regression-based feature extraction approach designed to enhance lesion classification in axial CT images. Geometrical characteristics across consecutive slices were modelled as signal-like inputs for a hybrid deep learning framework, enabling the capture of both absolute values and inter-slice variations. Four regression feature configurations (RFE_1 to RFE_4) were systematically evaluated, incorporating combinations of roundness, diameter, centroid coordinates, area, and perimeter. Results showed that RFE_2, comprising roundness, diameter, and centroid coordinates, achieved the highest classification accuracy of 96%, outperforming other configurations. In contrast, including area and perimeter reduced accuracy, highlighting the negative impact of redundant features. These findings demonstrate that careful optimisation of regression-based features can improve the robustness and reliability of AI-driven lung lesion detection systems.

Tuesday, October 28 2:30 - 4:00

Track A2F7 ETS 2: Engineering Technologies & Society (ETS) 2

Room: F7. 506 Selingan

Chair: Yan Yan Farm (Universiti Malaysia Sabah, Malaysia)

2:30 A Graph Based Attention Model and Calibrated Random Forest for Breast Cancer Classification Using Histopathology Images

Dipti Deb (National Institute of Technology Rourkela, India); Ratnakar Dash (NIT Rourkela, India); Durga Mohapatra (NIT, Rourkela, India)

Artificial intelligence and computer vision advancements have revolutionized computer-aided diagnosis (CAD) systems, enabling more accurate breast cancer (BrCan) detection using histopathology images. This study proposes a classification framework that integrates Vision Transformers (ViT), Graph Attention Networks (GAT), and Calibrated Random Forest (CRF) to enhance diagnostic accuracy. ViT effectively captures rich visual representations and helps to form a graph-like structure, while GAT models the structural relationships within histopathology images, providing a more comprehensive understanding of tissue morphology. Extensive experiments were conducted with various model combinations, demonstrating that the ViT + GAT + CRF architecture achieved the highest performance. The experiment is carried out on the BreakHis dataset, and the model acquires an accuracy of 97.33%. These results highlight the effectiveness of incorporating both visual and structural features to improve diagnostic reliability. Our proposed framework represents a significant advancement in digital histopathology-based (BrCan) diagnosis and holds promise for broader applications in medical imaging.

2:45 Development of a Smart Financial Tool for Computing High-Yield Savings in Digital Banks to Advance Financial Literacy Through a Blended Agile Methodology

John Heland Jasper Ortega (FEU Institute of Technology, Philippines)

This study developed the Digital Banks PH Notebook, a mobile application designed to support financial literacy among Filipinos by optimizing savings through high-yield digital banking platforms. The application featured a savings portfolio tracker, interest forecasting calculator, and savings goal management to address gaps in financial planning and savings behavior. Development followed a blended Agile methodology integrating Scrum, Extreme Programming, and Feature-Driven Development, ensuring iterative improvements aligned with user needs. Software quality was assessed using the ISO/IEC 25010 model, while qualitative feedback was analyzed through word cloud visualization to capture user sentiment and key focus areas. Findings indicated that the application effectively enhanced users' understanding of savings strategies and promoted responsible saving practices. The tool successfully connected the opportunities presented by digital banking with the practical requirements of financial education, providing users with actionable insights to manage their savings more strategically. By leveraging agile development practices and rigorous evaluation frameworks, the project demonstrated that technology-driven solutions can play a significant role in advancing financial literacy and supporting sustainable financial behaviors in an evolving digital economy.

3:00 Inculcating Soft Skills in Requirements Elicitation: a Dynamic Role-Play Approach

Chien Ching Lee (SIT, Singapore); Ryan Fraser Kirwan (Singapore Institute of Technology, Singapore)

Requirements elicitation (RE) is a critical yet often underemphasized component of software engineering education. This study investigates the effectiveness of a dedicated workshop designed to introduce undergraduate computing students to RE through experiential role-play with instructors simulating dynamic client interactions. A structured four-step elicitation framework, grounded in industry practices, guided students in preparation, question formulation, documentation, and confirmation. Post-workshop surveys and tutor feedback indicated that while students improved in areas such as question scoping and clarity, many continued to face challenges in rapport building and adaptive communication-aligning with prior research on the importance of interpersonal skills in RE. Students valued the workshop's practical focus but expressed a strong desire for additional practice, particularly in probing and clarifying requirements in dynamic client environments. Findings suggest that controlling the complexity of sample systems allowed students to concentrate more effectively on interview techniques. Only a minority preferred chatbot-based elicitation, citing concerns about authenticity and emotional nuance. Consolidated tutor feedback confirmed frequent student mistakes in sequencing and probing, echoing established literature. Although the small sample size limits generalizability, the study highlights the importance of structured practice, feedback, and reflection in developing RE competencies. Future research should explore targeted interventions to strengthen rapport-building and questioning strategies. Overall, the workshop effectively raised students' awareness of the dynamic, human-centered nature of requirements elicitation and underscored the need for continuous skill development in this area.

3:15 Honing Internship Students' Social-Emotional Competence: Unpacking and Mitigating Challenges

Chien Ching Lee (SIT, Singapore); Ryan Fraser Kirwan (Singapore Institute of Technology, Singapore)

Technological innovations do not just happen. They are spurred by teams with strong technical skills and effective social-emotional competence. This paper presents an interpersonal skills training workshop conducted three months into students' internships which focused on improving students' social-emotional competence specifically, using Goleman's' Emotional Intelligence model to regulate their emotions and that of others, The Johari Window to raise their awareness about their level of openness and the Thomas Kilmann conflict management model to help them read situations more accurately. Data was gathered via pre- and post-surveys and a Communication Challenges Scenario worksheet where they reflected on communication incidents during their internships. The findings from the pre-survey showed that the more severe communication issues were in adapting to professional communication styles, communicating with international colleagues, and understanding technical jargon. The improvements from the workshop as reflected in the post-survey were mainly in students adopting a more proactive communication stance in task execution. The top three improvements were in practicing active listening, navigating cultural and hierarchical dynamics and providing progress updates. The students mentioned that after the workshop, they were more confident in identifying and regulating their emotions and responding to others with empathy and clarifying tasks. They needed more support however, in managing conflicts.

3:30 Study on Quality Evaluation Models for Collaborative Architectures

Yo Suzuki, Kei Sugawara and Sumie Morita (Akita Prefectural University, Japan)

This paper proposes a hierarchical decision-making-based architecture evaluation model (H-DRC) designed to quantitatively assess the quality of collaborative architectures in complex systems. The model introduces a structured methodology for evaluating architectural characteristics by applying weighted metrics derived from prior studies, which are mapped to ISO/IEC 25010 quality attributes such as reliability, maintainability, and performance efficiency. By integrating these metrics, the H-DRC model enables a comprehensive visualization of architectural suitability and facilitates effective evaluation of system design decisions. To validate the proposed model, we applied it to an in-house system, the BabaCAFE System, which incorporates a Context Broker also implemented in FIWARE, an open-source platform framework widely recognized as an "urban operating system" for smart cities and industrial IoT solutions. The evaluation demonstrates that the H-DRC model can accurately capture architectural strengths and weaknesses, even in small-scale systems, and confirms its potential applicability to large-scale industrial domains such as supply chains, smart cities, and healthcare systems. This work contributes a practical and generalizable evaluation framework that bridges academic research and real-world implementation scenarios.

3:45 Automated KOJI AWARENESS Physical Function Assessment Using Pose Estimation and Web-Based Real-Time Evaluation

Shura Osako (Fukuoka Institute of Technology, Japan & FIT, Japan); Hiroyuki Fujioka (Fukuoka Institute of Technology, Japan)

This paper presents a web-based system for automating a subset of the KOJI AWARENESS physical function test using pose estimation. The KOJI AWARENESS test consists of 50 items designed to evaluate mobility, flexibility, and posture. In this study, five items focusing on neck and shoulder mobility were selected for implementation. The system uses YOLOv8 and MediaPipe to detect body and hand keypoints, providing real-time evaluation through a browser-based interface. Built with React and Flask, the system requires no special software installation, enabling deployment in both clinical and non-clinical environments. Initial testing with six participants confirmed that the system could accurately detect postures and evaluate movements in real time. These results demonstrate the feasibility of using pose estimation techniques for automated physical assessments. Furthermore, this study suggests that such systems may serve as a foundation for accessible and scalable approaches to physical function evaluation, with potential applications in preventive healthcare, remote rehabilitation, and physical fitness monitoring.

Tuesday, October 28 2:30 - 4:00

Track A2F8 CCI 2.3: Computing & Computational Intelligence (CCI) 2.3

Room: F8. 507 Monsopiad

Chair: Jay Dave (BITS Pilani Hyderabad Campus, India)

2:30 CardioScan: a Multimodal Approach for Congenital Heart Disease Diagnosis Using PCG Signals

Aditya Svs (IIIT Naya Raipur, India); Sai Sriram Gonthina (International Institute of Information Technology, Naya Raipur, India); Debanjan Das (IIT Kharagpur, India); Rajarshi Mahapatra (IIIT Naya Raipur, India)

Congenital heart disease (CHD) is a leading cause of morbidity and mortality in infants and early diagnosis with access to specific diagnostic tools is often limited and the interpretation of heart sounds is subjective. Traditional auscultation relies heavily on clinician skill and is highly variable, while advanced imaging techniques can be expensive, or even impossible in low-resource settings. In this study, we present a multimodal machine-learning framework that combines demographic data and 66 hand-crafted features from denoised and resampled phonocardiogram (PCG) recordings. Signal preprocessing included a third-order Butterworth bandpass filter set between 65-1000 Hz and resampling to uniform time series recordings of 2000Hz to preserve signal integrity and reduce noise. For classification we utilize LightGBM achieving binary task accuracy of 94% and multi-class murmur classification accuracy of 87%. The entire system can be deployed to a Raspberry Pi 4 connected to a digital stethoscope and able to conduct real-time inference as an embedded system without cloud or internet access. Experimental results verified high accuracy and low latency operational characteristics making it well suited for an embedded deployment with such limited computation resources. The proposed end-to-end framework provides an affordable, portable, and clinically useful tool to assist in the early detection of abnormal heart sounds and has the potential to revolutionize CHD screening in resource challenged communities.

2:45 Dual-Stage Feature Refinement and Wavelet Denoising for Enhanced VIX Prediction Using Residual BiLSTM

Akanksha Sharma (Maulana Azad National Institute of Technology, Bhopal, India); Priya Singh (Vellore Institute of Technology, Tamil Nadu, India); Chandan Kumar Verma (Maulana Azad National Institute of Technology, Bhopal, India)

Derivative pricing and financial risk management rely heavily on volatility predictions. Given the dynamic and nonlinear nature of financial markets, accurately predicting volatility remains a persistent challenge. Traditional econometric models often struggle to capture the complex patterns and time-dependent behaviors present in market data. This research presents a Residual Bidirectional Long Short-Term Memory (ResBiLSTM) model-based deep learning framework for VIX price prediction. By integrating residual connections and bidirectional temporal processing, the model is able to successfully capture intricate patterns found in financial time series data. A complete array of 64 designed features, comprising technical indicators and wavelet-denoised inputs, was employed to train and assess the model. The suggested ResBiLSTM surpasses conventional models, including LSTM, GRU, CNN, BiLSTM, and Residual LSTM, across multiple criteria. The performance was additionally confirmed using 5-fold cross-validation and statistical significance assessment via paired t-tests across many experimental iterations. The findings illustrate the model's resilience, precision, and applicability for implementation in practical volatility forecasting scenarios.

3:00 A Predictive Approach to Energy Loss in Solar Panels Affected by Soiling

Yi Feng Law (Universiti Tenaga Nasional (UNITEN), Malaysia); Faridah Hani Mohamed Salleh (University of Tenaga Nasional, Malaysia); Murthy Parventanis (Universiti Tenaga Nasional, Malaysia)

The use of solar energy continues to grow, tackling challenges such as soiling is vital to sustain efficiency. Soiling, the accumulation of dirt, dust, and pollutants on solar panels, significantly reduces the sunlight reaching photovoltaic cells, thereby diminishing energy output. This study examines the key variables influencing energy loss from soiling in Sepang, Selangor, Malaysia, with a focus on irradiance and rainfall in a tropical climate. By using linear regression, decision tree, and the random forest models, we predict energy loss from soiling and validate the model accuracy with R-Squared, Mean Squared Error, and Root Mean Squared Error. The results show that the random forest model, driven solely by irradiance data, provides the most accurate predictions. Including rainfall did not enhance predictive accuracy, underscoring irradiance as the primary factor. This research highlights the impact of soiling in tropical climates, offering a valuable insight to improve solar panel maintenance and to optimize energy production.

3:15 Sensor-Based Gait Recognition Using Ensemble Network Unified with Independent Subnetworks

Sonia Das (National Institute of Technology, Rourkela, India); Pradosh Ranjan Sahoo (VIT-AP University, India)

Smartphone-based gait recognition is increasingly important for surveillance, security, and health monitoring. However, traditional deep learning methods often struggle to model long-term dependencies in gait sequences and suffer from the computational demands of large convolutional kernels. Although multi-kernel approaches attempt to address these challenges, their fixed sizes may fail to capture relevant variations in dynamic signals and are often difficult to train efficiently. This paper proposes a multi-scale deep ensemble network that overcomes these limitations by using independent subnetworks, each processing different temporal resolutions of down-sampled input signals. These subnetworks extract diverse and complementary gait features. A unified ensemble training strategy integrates the outputs using multiple loss functions, enhancing the model's robustness and generalization. Extensive experiments on two benchmark datasets demonstrate that our method captures both spatial and temporal complexities more effectively than existing approaches. The complementary learning achieved through multi-scale ensemble modeling leads to superior performance, setting a new standard in smartphone-based gait recognition.

Tuesday, October 28 4:30 - 6:00

Track A3F2 CSR 3: Control Systems & Robotics (CSR) 3

Room: F2. 501 Kadamaian

Chair: Siow Cheng Chan (University Tunku Abdul Rahman, Malaysia)

4:30 Fault-Tolerant Control for a Four-Wheeled Independently Steerable Power-Driven Mobile Robot

Gopika Gireesh (College of Engineering Trivandrum, India & Nil, India); Hari Kumar R and Lal Priya P S (College of Engineering Trivandrum, India)

This work extends a fault-tolerant control (FTC) strategy based on kinematic control, previously applied to four-wheel Mecanum wheel systems, to an omnidirectional mobile robot equipped with four independently steerable powered wheels, a configuration that remains underexplored at the kinematic level. Such a robot offers unparalleled maneuverability by independently controlling both the steering and rolling of each wheel, allowing effortless movement in any direction. This advanced mobility makes these robots highly suitable for real-world applications that require precise navigation within confined or dynamic spaces, including warehouse automation, healthcare, and search and rescue operations. The proposed approach maintains trajectory tracking effectively by exploiting kinematic redundancy, without relying on dynamic compensation. The system can successfully complete the task despite faults that affect up to two wheels; however, it cannot accommodate faults beyond this limit. The effectiveness of the control scheme under single and two-wheel actuator fault conditions has been validated through MATLAB simulations.

4:45 Conversational Simulator-Based Study on the Difference in Impressions of Turn-Taking Behaviors in Video and/or Audio

Masahide Yuasa (Shonan Institute of Technology, Japan)

To develop conversational robots or agents that can communicate smoothly with humans, it is essential to conduct studies that deepen our understanding of the social and emotional aspects of human communication. Previous studies have focused on turn-taking behaviors, which inherently involve social and emotional elements. These studies have explored how varying the timing of turn initiation or termination can create different emotional impressions. Although they have yielded valuable insights, a key issue remains: these studies either used a mix of visual and auditory stimuli or relied solely on auditory input. Therefore, the distinct influences of visual and auditory modalities have not been sufficiently examined. In this study, we investigated the differences in impressions created by visual and auditory stimuli in turn-taking behaviors using a conversational simulator. The experiment involved three types of turn-taking behaviors-Overlap, No-gap-no-overlap, and Gap-and three media conditions: Video+Audio, Video-only, and Audio-only. Participants watched or listened to conversations involving three virtual characters and rated their impressions. The results showed that impressions of agreeableness and politeness were rated significantly higher in the Video+Audio condition compared to the Audio-only stimulus. Moreover, there were no significant differences between the Video+Audio and Audio-only conditions for the factors of relaxation, modesty, respect, and closeness. This suggests that nonverbal cues expressed through video during turn-taking play a significant role in conveying politeness and agreeableness. These findings can inform the design of conversational robots or agents by highlighting the importance of incorporating visual cues into turn-taking behaviors, especially when considering differences between visual and auditory aspects.

5:00 Coordinated Dual-Arm Manipulation Using Reinforcement Learning: a Soft Actor-Critic Approach on the Poppy Humanoid

Allen Jacob George (Birla Institute of Technology and Science Pilani, India); Abhishek Sarkar (Birla Institute of Technology and Science Pilani, India & Hyderabad Campus, India); Joyjit Mukherjee (BITS Pilani Hyderabad Campus, India)

In this work, we present a reinforcement learning-based framework for dual-arm object manipulation using the Poppy humanoid robot platform. Our approach addresses the challenges of coordinated grasping and object transport by decomposing the task into two sequential stages. In the first stage, a single policy is trained using the Soft Actor-Critic (SAC) algorithm to simultaneously control both arms and the torso to perform object grasping. In the second stage, a separate SAC policy is trained to move the grasped object to a specified target location. To improve learning efficiency, we incorporate human demonstration data during training. This integration of expert guidance with sample efficient deep reinforcement learning enables the system to achieve robust, coordinated manipulation behavior. Further validation of the simulation results were tested by putting the Poppy robot joint trajectories generated from the simulation results. Both simulation and experimental results are shown to demonstrate that our method can successfully perform dual-arm object grasping and relocation with stable and synchronized motion.

5:15 Local Acceleration on Planar Mobile Platform with Front Differential Drive and Rear Omni Wheels

Chayapat Leardngammongkolkul, Kasem Hutapornprasert, Natchanont Phanphakdeewong and Ronnapee Chaichaowarat (Chulalongkorn University, Thailand)

Differential drive mobile platforms are widely applied in mobile robotics. Omni wheels provide a practical alternative to caster wheels, enhancing mobility and stability by enabling lateral motion while preserving the ground contact point. This research examines a planar mobile platform featuring front differential drive wheels and rear omni wheels, intended for an electric wheelchair. The chassis was constructed from carbon fiber tubes joined by 3D-printed components. Two 4-inch geared BLDC hub motors with integrated hall sensors, controlled by the ODrive motor controller, drive the front wheels, while two 125-mm omni wheels serve as passive supports on the rear axle. Experiments were conducted by varying the speeds of the front left and right wheels to observe lateral and longitudinal acceleration at three distinct positions: centrally located between the left and right wheels, slightly posterior to the front axle, and adjacent to the left and right omni wheels at the rear axle. During the rotation of the wheelchair around an instantaneous center of rotation, the magnitudes of centrifugal acceleration in the longitudinal and lateral components may vary depending on the passenger's location. The findings from this study can serve as a framework for regulating motor speeds during wheelchair turns to reduce discomfort.

5:30 Planetary Geared CVT Using Electromagnetic Brake to Adjust Slipping Torque of Ring Gear

Jormpoom Sukdaeng, Pacharawit Yokyong, Supanat Hathanglarn and Ronnapee Chaichaowarat (Chulalongkorn University, Thailand)

Planetary gears offering high reduction ratios with concentric input and output shafts are widely applied in various mechatronic systems. For optimizing the efficiency of machines across their range of operating conditions, this paper presents a design concept of the planetary geared continuously variable transmission (PG-CVT) using an electromagnetic (EM) brake to adjust the slipping torque of the rotatable ring gear. The input torque is applied to the sun gear while the output shaft is connected to the planet carrier. The secondary velocity source for driving the ring gear is replaced by the brake resisting the rotation, which requires less energy to operate. When the brake is fully locked, the planetary gear operates with stationary ring gear. The brake torque required to support the ring gear is the linear combination of the input torque at the sun gear and the output torque at the carrier. When the brake is freely slipped, the output torque is limited to zero although the sun gear is rotating. The variable transmission ratio can be achieved by adjusting the torque applied to the ring gear, which results in changing the torque conversion ratio. In this paper, the experimental prototype of the PG-CVT was designed and built. At different output torque and brake torque conditions, the constant speed tests were conducted to observe the input torque required for maintaining the constant speeds. The static friction and the damping of the PG-CVT were characterized. It is worth noting that the zero or negative damping phenomenon was observed from a constant brake torque. The findings of this study are fundamental for implementing velocity feedback torque control for achieving desired dynamic responses.

5:45 Fuzzy PID Control Modeled by T-s Fuzzy System for Train Speed Tracking in Virtual Coupling

Yiting Liang (Beijing Jiaotong University, China & 无, China); Jian Wang, Debiao Lu, Jiang Liu and Bai-gen Cai (Beijing Jiaotong University, China)

To address the challenge of stability analysis for traditional nonlinear fuzzy PID controllers in train virtual coupling, this paper proposes a dynamic modeling approach for speed errors based on the T-S fuzzy model. The T-S fuzzy model is constructed via fuzzy rules to achieve local linear approximation of nonlinear error dynamics. Leveraging Lyapunov stability theory, local asymptotic stability conditions are derived using Linear Matrix Inequalities (LMIs), and Particle Swarm Optimization (PSO) algorithm is employed for multi-objective optimization of PID parameters, considering response speed, control smoothness, and gain robustness comprehensively. Simulation results in virtual coupling following scenarios demonstrate that the controller achieves high-precision tracking, the mean error shows a 19.67% reduction compared to traditional fuzzy PID, the mean squared error exhibits a 20.51% reduction and control effort fluctuations are significantly reduced. This study establishes a local stability framework for T-S fuzzy control in virtual coupling systems, providing a theoretical basis for energy-efficient multi-objective optimization under complex dynamics.

Tuesday, October 28 4:30 - 6:00

Track A3F3 PES 3: Power, Energy & Electrical Systems (PES) 3

Room: F3. 502 Mesilau

Chair: Yu Zheng Chong (Universiti Tunku Abdul Rahman, Malaysia)

4:30 Design and Optimization of a Highly Efficient Dual-Absorber Heterostructure for Next-Generation Photovoltaics

Venkateswarlu G and Umakanta Nanda (VIT-AP University, India); Pratap Kumar Dakua (Vignan's Institute of Information Technology, India & Duvvada, Vishakapatnam, AP, India)

Perovskite solar cells (PSCs) face significant challenges such as instability, high recombination, poor charge transport, and mismatched band alignment, which limits their power conversion efficiency (PCE). These issues are more pronounced in multi-junction or heterojunction configurations. This study addresses them by proposing an advanced and novel dual-absorber thin-film heterojunction structure (THSC) is Au/CBTS/BiFeO 3/CIGS/PDINO/FTO/Ni using the Key Materials such as CBTS and BiFeO 3 improves high carrier mobility, optimal layer thickness, controlled defect levels, tailored doping concentrations, and minimized interface defects, CIGS enhances light absorption and provides efficient charge carrier generation due to its tunable band gap. This contributes to reduced recombination losses, improved photocurrent, and overall device efficiency. The structure of the THSC device was optimized and simulated to have remarkable photovoltaic parameters, impressive performance, with a PCE of 40.10%, Jsc of 35.81 mA/cm 2, Voc of 1.31V and FF of 88.51%. The novel architecture proposed guidance for future experiments and simulations aimed at developing high-efficiency, stable thin-film heterojunction solar cells in next-generation photovoltaics.

4:45 Simulation of M13 Bacteriophage-Based Piezoelectric Nanogenerators

Glenn C. Virrey, Himeko Andrei Noto, Mariah Jane Sicam, Mike Denver Tolentino, John Josea Umali and Leanne Vince Vergara (University of Santo Tomas, Philippines)

Piezoelectric nanogenerators (PENGs) based on biological materials, such as the M13 bacteriophage, present a promising avenue for sustainable and biocompatible energy harvesting. This study investigates the influence of electrode material, geometry, and thickness on the performance of M13-based piezoelectric nanogenerators through Finite Element Analysis (FEA) and MATLAB Simulink circuit simulations. Four electrode materials-gold, silver, copper, and stainless steel-were examined with varying thicknesses (20 µm, 120 µm, and 200 µm) and geometries (square and rectangular). Simulation results revealed that gold square electrodes at 20 µm achieved the highest voltage output, reaching 10.383 V. Statistical analysis using one-way ANOVA confirmed the significance of material selection and geometry in optimizing energy conversion efficiency. The test displayed scores of 0.2017, 0.1845, and 0.2953 for the square electrodes with thicknesses of 20 µm, 120 µm, and 200 µm, respectively, indicating minimal discrepancies between them. Furthermore, circuit simulations demonstrated that energy harvested using the optimal configuration could be effectively stored in a lithium-ion battery. Among the chosen electrode materials, gold generated the quickest charging time of 371.42 hours with and without load, while stainless steel charged the slowest. Notably, thinner electrodes produced better voltage and power outputs, while increased thickness resulted in diminished performance. These findings highlight the critical role of electrode design in enhancing the performance of biologically inspired PENGs and pave the way for their integration into self-powered wearable electronics and biomedical sensors. Future research should explore experimental validation and assess long-term environmental durability for real-world applications.

5:00 Multiport Current-Fed Asymmetric Bidirectional DC-DC Converter for Hybrid Polar DC Microgrid System

Shangyi Li, Mingjun Jiang and Ma Jianjun (Shanghai Jiao Tong University, China)

To promote sustainable development and reduce energy loss, hybrid polar DC microgrid system has been studied and put into use worldwide. However, most traditional symmetric DC-DC converters fail to take the capacity difference between PV and battery into consideration. It results in extra component cost for converters in PV energy storage system. This paper presents a multiport current-fed asymmetric bidirectional DC-DC converter (CF-ABC) to handle the capacity difference. The converter consists of a full-controlled bridge in primary side and two semi-controlled bridges in parallel in secondary side under forward operation mode. Under reverse operation mode, it works as a dual active bridge (DAB). The working principles and zero voltage switching (ZVS) conditions under both operation modes are analyzed and presented in this paper. Compared to traditional bidirectional DC-DC converter, the proposed CF-ABC can reduce the total cost of the converter. The effectiveness of the proposed CF-ABC is verified through experiments.

5:15 Sensor-Driven Solar Power Forecasting Using No-Code LSTM in KNIME: A Scalable Deep Learning Framework

Lakshmi Boppana (National Institute of Technology Warangal, India); Raghuram Kornepati (RVR & JC College of Engineering, India)

As a predominant renewable energy source, solar energy has gained a significant prominence due to its sustainable characteristics and environmental benefits. Precise forecasting of photovoltaic power generation constitutes a critical requirement for optimal energy management and the maintenance of stability in electrical grid operations. This work proposes a novel predictive modeling framework that implements long-short-term memory (LSTM) neural networks through the KNIME Analytics Platform to predict solar energy production utilizing high-resolution sensor data. The comprehensive data set incorporates time series measurements of key solar parameters, including solar irradiance, global horizontal irradiance (GHI) power density, plane-of-array (POA) energy yield, and module temperature, acquired from an industrial-grade solar monitoring infrastructure. Data preprocessing techniques and visualizations were used to improve model performance and interpretability. The LSTM model effectively captured temporal dependencies in the data, with evaluation metrics including mean absolute error (MAE), mean squared error (MSE), and root mean squared error (RMSE). The implemented LSTM architecture successfully learned complex temporal patterns inherent in the solar generation data, with rigorous performance validation conducted using standard evaluation metrics: mean absolute error (MAE), mean squared error (MSE) and root mean squared error (RMSE). The results demonstrate that a well-optimized LSTM can effectively model long-term solar patterns without the computational overhead of hybrid architectures while maintaining competitive accuracy, making it more scalable and deployable for real-world solar energy forecasting. This contribution addresses a fundamental research gap in the field by providing a simplified yet highly accurate forecasting solution, particularly beneficial for grid operators and renewable energy planners seeking efficient and reliable predictions.

5:30 Early Detection of Internal Short Circuit in Lithium Ion Battery Using Time-Frequency Analysis

Anup Appasaheb Kshirsagar and Jithendra Mani Kumar Kommuru (Chang Gung University, Taiwan); Cher Ming Tan (Chang Gung University & Center for Reliability Sciences and Technologies, Taiwan); Shuen-Lin Jeng (National Cheng Kung University, Taiwan)

Early identification of internal short circuits (ISCs) in lithium-ion batteries (LiBs) is critical for ensuring system-level safety and operational reliability in applications such as electric vehicles and grid-scale energy storage. Conventional diagnostic techniques-such as resistance monitoring, voltage deviation analysis, and statistical health modeling-typically detect faults only after substantial degradation has occurred, often overlooking transient electrochemical anomalies that precede ISC onset. To address this diagnostic limitation, the present study introduces a high-resolution, voltage-only analytical framework employing the Smoothed Pseudo-Wigner-Ville Distribution (SPWVD) for time-frequency spectral decomposition of discharge voltage signals. Experimental results demonstrate that the SPWVD technique can detect early ISC-related spectral changes as much as 96 seconds before the appearance of conventional ISC indicators. These findings highlight SPWVD's effectiveness in revealing subtle, early-stage anomalies associated with ISC development. The proposed framework contributes a low-intrusion, high-sensitivity strategy for real-time prognostic health management of LiBs. By enabling predictive fault detection, it offers significant potential to enhance battery safety protocols and supports in the development of advanced diagnostic tools for early warning and preventive control in energy storage systems.

5:45 Power Quality and Condition Monitoring of Inverter Duty Transformers at Grid-Connected Solar Photovoltaic Plants in India

M V Chilukuri (VIT University, Vellore, India); Kavita Sao (VIT University & Vellore Institute Technology, India)

A key area of concern is the performance of grid-connected solar photovoltaic (SPV) plants, particularly in maintaining power quality and operational reliability. As of 23 May 2025, India had installed 107.95 GW of solar PV capacity, much of which was through grid-connected solar photovoltaic (SPV) plants incorporating thousands of Inverter Duty Transformers (IDTs). However, a number of these IDTs have experienced failures during commissioning and operation, despite compliance with existing national and international standards. Currently, there is limited publicly available data on IDT failures at both the national and global levels. To address this gap, a technical study was carried out involving (a) surveys of SPV plants and (b) Monitoring of power quality and condition at selected sites. The aim is to gain a deeper understanding of the nature of these failures, particularly those involving IDTs, and to identify the root causes through modeling and analysis. Furthermore, CIGRE has recently established Working Group A2.68 to investigate these issues globally and publish its findings and recommendations. These insights will help national committees to revise standards and implement best practices. This paper discusses the challenges and opportunities identified during the SPV plant survey, as well as the results of monitoring power quality and conducting condition assessments. Highlighting the importance of power quality and condition monitoring in the smart grid for power supply reliability and quality for critical infrastructure.

6:00 Dynamic Solar Energy Optimization: Implementing DiTO-Based MPPT Control for Grid-Connected PV System

Praveen Kumar Balachandran (Vardhaman College of Engineering, India); Muhammad Ammirrul Atiqi Mohd Zainuri (Universiti Kebangsaan Malaysia, Malaysia)

Presently, solar PV systems are considered as an efficient and sustainable technology because of the ability by the systems to convert sunlight into electricity. But, due to the unpredictability of solar irradiation, there is a requirement for complex control methods to enhance the effectiveness of these systems. However, one of these mechanisms takes a crucial role to guarantee the maximum operating electrical power from the solar PV system according to the environmental conditions, which is called Maximum Power Point Tracking (MPPT). However, most of the previously mentioned techniques are still confronted by issues like slow response to sudden variations in irradiance, high computation, and higher implication costs. These are the discrepancies that this paper is set to eliminate by adopting a newly introduced Dipper Throated Optimization (DiTO)-based MPPT control strategy. The DiTO-MPPT control approach is therefore a dynamic real-time method that tries to maximize the power extracted from the PV system by changing its functioning parameters to settle for the MPP. The combination of the DiTO algorithm with the MPPT control is indeed a novel approach that promises to enhance the power output and contour the reliability of the SPV systems that are connected to the grid. Through experimental outcome and simulation it has been established that the proposed method is effective in achieving maximum energy conversion efficiency as compared to the existence approach used in solar PV systems.

Tuesday, October 28 4:30 - 6:00

Track A3F4 ECD 3: Electronics, Circuits & Devices (ECD) 3

Room: F4. 503 Dinawan

Chair: Mazlina Mamat (Universiti Malaysia Sabah, Malaysia)

4:30 Guidelines and Logistics for Manufacturing RISC-V Vanilla Silicon Chips Using SkyWater 130nm OpenPDK

Anand K Vinu (Cochin University of Science and Technology, India); Sahil Athrij (NVIDIA, USA); Asna V A (Cochin University of SCience and Technology, India)

The design and fabrication of Reduced Instruction Set Computer - Five (RISC-V) based silicon chips using the SkyWater 130nm Open Process Design Kit (PDK) and the OpenLane toolchain represent a significant step toward democratizing semiconductor development. Leveraging the open-source nature of the RISC-V instruction set architecture (ISA) offers substantial advantages in terms of flexibility, extensibility, and cost reduction. This open approach enables designers to fully customize their hardware without the licensing constraints typically associated with proprietary ISAs, making it an ideal candidate for academic, research, and low-volume commercial applications.

The design process begins with a clear definition of the desired functionality of the chip, followed by the development of Register Transfer Level (RTL) code implementing the five-stage pipelined RISC-V core. This includes modular components for the instruction fetch, decode, execute, memory access, and write-back stages. The RTL code is validated through extensive simulations using tools such as Verilator and ModelSim to ensure functional correctness under a variety of test scenarios. GTKWave is used to analyze simulation waveforms, offering visibility into signal transitions and pipeline behavior, and aiding in the detection and resolution of subtle design issues.

Synthesis is performed using Yosys as part of the OpenLane flow, which translates the RTL code into a gate-level netlist. The structured and modular design of the pipelined core allowed for relatively smooth synthesis, with only minor issues such as unconnected ports or redundant signals, which were resolved with small code refinements. Once synthesized, the design undergoes floorplanning, placement, clock tree synthesis, and routing using tools integrated into the OpenLane environment. Final checks and verification steps ensure design rule compliance and functionality before the layout is exported as a Graphic Data System II (GDSII) file, ready for fabrication.

Despite the challenges commonly associated with open-source flows-particularly in handling complex Verilog constructs-the project demonstrates that with careful design practices, efficient RTL structuring, and iterative verification, it is entirely feasible to produce manufacturable RISC-V silicon on the 130nm technology node. This work underscores the viability of using open-source tools and platforms for custom chip development and contributes to broader efforts aimed at making silicon design more accessible and innovative.

Index Terms- RISC-V, GDSII, SkyWater, OpenPDK, OpenLane, RTL Design, Physical Design

4:45 Advanced Three-Axis Shake Table System for Comprehensive Earthquake Simulation and Structural Dynamics Research

Pasindu A. Iddamalgoda and Dulan Sashik (Sri Lanka Institute of Information Technology, Sri Lanka); Jayakody Arachchilage Don Chaminda Anuradha Jayakody (Curtin University Technology, Sri Lanka & Sri Lanka Institute of Information Technology, Sri Lanka); Nalin Manchanayake (LinkNlabs, Sri Lanka); Raj Prasanna (Massey University, New Zealand); Pradeep Abeygunawardhana (Sri Lanka Institute of Information Technology, Sri Lanka)

The development of a three-axis shake table is essential for advancing research in sensor validation, earth- quake engineering, and structural dynamics. Traditional shake tables that exist provide one or two-dimensional motion where it limits the capability of three-dimensional (3D) movements in experiments conducted by engineers, researchers and users. This paper presents the design and development of a cost- effective 3-axis shake table which has the ability to produce independent motions in the X, Y and Z directions. The shake table consists of three dynamic plates, each axis supported by dual linear guides and driven by separate DC motors with a 12V voltage rating. This independent configuration of the axis allows the users for a precise control and generation of vibrations across all three axes, making it suitable for a wide range of testing applications. The design utilizes motor drivers for varying power to the motors and control the movement of the dynamic plates independently, which omits the requirement of encoders and reduce the production cost. The developed shake table offers a versatile and cost-effective solution for laboratories, earthquake engineering and structural health monitoring, where 3D motion capabilities are essential. Index Terms-Structural Health Monitoring (SHM), Earth Quake Simulation, Structural Dynamics, Vibration Analysis, Three Dimensional Motion

5:00 Cryptographically Secure Random Number Generator Utilizing Environmental Radiation

Tee Hui Teo (Singapore University of Technology and Design, Singapore); Maoyang Xiang (8 Somapah Rd & Singapore University Technology and Desgin, Singapore); Qianrui Lin, YiKai Zhang, Ziyue Pan, Au Hern Ng, Khant Htet Kaung and Kevin Chua (Singapore University of Technology and Design, Singapore); Hei Lam Shum (Auckland University of Technology, New Zealand)

This paper proposes a cost-effective Cryptographically Secure Random Number Generator (CSRNG) utilizing a Field-Programmable Gate Array (FPGA) integrated with a Geiger counter. The front end of this system is designed to capture ambient radioactivity through a Geiger counter system that emits pulses in response to such environmental stimuli. To complement this setup, a neoTRNG-based ring oscillator RNG is incorporated to enhance the randomness of the generated numbers. By combining these elements, the CSRNG can achieve a high level of unpredictability crucial for cryptographic applications. Moving to the system's backend, utilizing the BLAKE2s hash function is pivotal in counteracting any potential biases introduced by the frontend components. This hashing function ensures that the output random numbers remain robust and secure, free from discernible patterns or vulnerabilities. In essence, the methodology outlined in this paper offers a comprehensive guide to conceptualizing and implementing a cost-effective CSRNG. The challenges inherent in generating cryptographically secure random numbers can be effectively navigated through a meticulous integration of hardware components and cryptographic techniques. This approach sheds light on the intricate processes in creating a reliable source of randomness, essential for safeguarding sensitive information in various digital systems.

5:15 Fingerprint Tarot Fortune Teller Game Utilizing Hénon Map-Based Pseudorandom Number Generator

Tee Hui Teo (Singapore University of Technology and Design, Singapore); Maoyang Xiang (8 Somapah Rd & Singapore University Technology and Desgin, Singapore); YiKai Zhang, Junhan Li, Keith Zhengxian Lee, Wyndham Tian, Yi Sun and Yew Rei Leow (Singapore University of Technology and Design, Singapore); Miranda Chen (University of Waterloo, Canada)

The project is about integrating biometric security, hardware-based randomness, and symbolic visualization through tarot for a unique user experience. It utilizes a field-programmable gate array (FPGA) for biometric authentication using fingerprint input to enhance security. Sensitive data is encrypted within the FPGA, ensuring tamper resistance and mitigating threats such as replay attacks. The system utilizes the entropy from the fingerprint as a seed for a pseudorandom number generator (PRNG) to select a tarot card displayed on a Raspberry Pi and a narrative. The project explores the fusion of digital security and human meaning by combining secure biometrics, hardware-accelerated cryptography, and symbolic storytelling. It aims to provide personalized authentication, interactive installations, and secure entertainment interfaces for users. A custom enclosure CAD design was developed for the FPGA-integrated biometric system to improve user-friendliness. The design focused on touch-based interaction using a touchscreen and fingerprint scanner to simplify the user interface and enhance user enjoyment. This approach allowed the team to concentrate on perfecting the PRNG code rather than dealing with moving parts and manual updates for user instructions. The Hénon Map-based PRNG is implemented in the FPGA for real-time applications.

5:30 Entropy-Rich One-Time Password Generation Utilizing Sensors in a Hardware-Realized Chaotic Chua's Circuit

Tee Hui Teo (Singapore University of Technology and Design, Singapore); Maoyang Xiang (8 Somapah Rd & Singapore University Technology and Desgin, Singapore); Zhengyao He, Qianrui Lin, RK Suriya Varshan, Yee Kiat Lim, Jing Ting Leow and Ahmad Danish Bin Azli (Singapore University of Technology and Design, Singapore); Matthew Wong (University of Waterloo, Canada)

This paper introduces a novel hardware-based solution for generating one-time passwords (OTPs) using a field-programmable gate array (FPGA). By leveraging real-world analog noise sources like light, temperature, and sound sensors, the system ensures a high level of entropy to seed the random number generation process in a dedicated FPGA chaotic Chua's circuit. The design of this OTP generator is capable of producing secure 5-digit OTPs ranging from 00000 to 99999. These OTPs can serve various purposes, such as wireless applications when transmitted to an ESP32 microcontroller or authentication in access control systems. By integrating these OTPs directly into access control systems, organizations can enhance their security measures significantly. This integration allows for seamless and secure authentication processes, ensuring that only authorized individuals gain access to restricted areas. The proposed approach prioritizes high randomness and resistance to prediction, essential characteristics for secure embedded systems. By incorporating multiple noise sources and utilizing FPGA technology, the OTP generator guarantees a robust level of security. Overall, the hardware-based OTP generator presented in this paper stands as a reliable and innovative solution for enhancing security in embedded systems.

5:45 An Electrical Behavior Analysis Method of ESD Protection Structure in Chip Based on SPICE

Siyuan Shen, Xiangfen Wang, Bo Wan and Guicui Fu (Beihang University, China)

With the continued scaling of integrated circuits (ICs), strong electrostatic-discharge (ESD) protection is increasingly essential for device reliability. This paper presents a method for analyzing the behavior of SCR-based protection structures using SPICE. The workflow is driven by Transmission Line Pulse (TLP) measurements, which are used to characterize the electrical behavior and to set up the simulations. Key metrics such as triggering voltage, holding voltage, and snap-back are examined through both experiments and SPICE. The Silicon-Controlled Rectifier (SCR) device is used as the representative protection structure, known for its reliable and efficient performance. The proposed methodology provides a practical framework for simulation and experimental verification, supporting the design and optimization of ESD protection in ICs. In particular, hybrid SPICE models are shown to capture the overall protection performance of SCR structures. The analysis also offers a systematic process for comparing and balancing alternative ESD solutions, providing a reliable basis for optimization in semiconductor device and packaging design.

Tuesday, October 28 4:30 - 6:00

Track A3F5 CS 3: Communication Systems (CS) 3

Room: F5. 504 Madai

Chair: Azwan Mahmud (Multimedia University & Telekom Malaysia, Malaysia)

4:30 VAE-BiLSTM-IDS: A Two-Phase Deep-Learning Framework for Enhanced IoT Security

Siddhant Gond (Indian Institute of Information Technology, Guwahati, India); Bishal Chhetry, Rajdeep Kumar Dutta, Rakesh Matam and Ferdous Barbhuiya (Indian Institute of Information Technology Guwahati, India)

The widespread adoption of Internet of Things (IoT) devices has transformed industries such as healthcare, manufacturing, and smart cities. However, these devices often possess limited resources and weak security mechanisms, making them vulnerable to cyberattacks. Traditional Intrusion Detection Systems rely on known attack signatures and are ineffective against novel or unknown threats. Although recent machine learning (ML) approaches aim to detect anomalous activity, many continue to suffer from high false alarm rates and degraded performance under dynamic network conditions. To address these challenges, we propose a two-phase deep learning framework, VAE-BiLSTM-IDS, designed specifically for IoT networks. In the first phase, Variational Autoencoders (VAEs) learn normal traffic patterns and detect anomalies using adaptive thresholds that adjust to changing network behavior. In the second phase, a CNN-BiLSTM model leverages both spatial and temporal features to classify anomalies and identify specific attack types. Evaluated on the Edge-IIoT dataset, which contains realistic IoT traffic and zero-day attacks, our framework yields a detection accuracy of 98.89%. It significantly reduces false positives compared to state-of-the-art methods. This approach offers a robust and adaptive solution for improving IoT security.

4:45 Game-Theoretic Optimal Channel Allocation for LoRaWAN in Dynamic IoT Environments

Soham Raju Kadtan and Biraja Nanda Mohanty (BITS Pilani, India); Alekhya Gorrela, Anakhi Hazarika and Nikumani Choudhury (BITS Pilani Hyderabad Campus, India); Dipamani Choudhury (Royal Global University, India); Syed Mohammad Zafaruddin (BITS Pilani, India)

Low-power wide-area networks (LPWAN) have substantially improved the Internet of Things (IoT). LoRaWAN is a potential technology for IoT applications because it uses low-power, long-distance communication and offers excellent availability with low energy consumption. LoRaWAN power consumption can be reduced using the pure Aloha protocol at the MAC level. Optimizing orthogonal transmission parameters is still a major difficulty for enhancing network performance, even though they reduce packet loss and prevent collisions, especially in dynamic and heterogeneous networks. However, the challenge of random channel selection in LoRaWAN communication often leads to inefficient resource utilization and degraded network performance. This paper proposes a novel game-theoretic approach for optimal channel selection in LoRaWAN networks. Our method leverages real-time Received Signal Strength Indicator (RSSI) data and a non-cooperative game theory model to dynamically select channels, thereby improving throughput and reducing packet loss. Through extensive simulations and a real-world testbed, we demonstrate that our proposed mechanism outperforms existing approaches such as the Online Decision algorithm and MFMSF. Specifically, it achieves up to 22% improvement in throughput, 15% higher packet delivery ratio, and 18% reduction in latency, while consuming up to 25% less energy under heavy and dynamic traffic conditions. This work offers a significant advancement in enhancing the scalability and reliability of LoRaWAN networks, paving the way for more efficient IoT communications.

5:00 An Ensemble Learning Approach for Malicious Traffic Detection System for Computer Networks

Jay Fel Quijano (Mapua University, Philippines & Nexus Technologies, Inc., Philippines); Ramon Garcia (Mapua Institute of Technology, Philippines)

Threats in the digital landscape increased as different emerging technologies progress. Despite the various implementations of different defensive measures and tools to monitor and detect traffic anomaly, complexity and cost are the main challenges for small to medium enterprise networks. This study developed a malicious traffic detection system that utilized signature-based and anomaly-based techniques through packet analysis and network flow using machine learning algorithms. The system consists of a Raspberry Pi-based packet capturing tool, website application, implored with Ensemble Learning Model to detect and classify malicious network traffic. The ensemble learning model which includes weak learners like Decision Tree, Naïve Bayes and Support Vector Machine (SVM), in which individual results are combined and boosted using XG Boost algorithm. CTU-13 and CSE-CIC-IDS2018 datasets were used for training and validating the model. The ensemble learning model achieved an accuracy of 96.42%, precision of 98.99%, recall of 93.79%, and F1-score of 96.32% for the binary classification using the validation data. Moreover, the model also achieved an accuracy of 96.39%, precision of 95.82%, recall of 96.39%, and F1-score of 95.66% for the multiple classification. The ensemble machine learning model was also evaluated using generated traffic, resulting in a decline in performance: accuracy dropped to 60.94%, precision to 44.44%, recall to 30.77%, and the F1-score to 61.54%.

5:15 Age Optimal Scheduling for a Linear Multiflow Network with Transmission Constraints

Teena Mary Treesa (IIITDM Kancheepuram, India); Premkumar Karumbu (Indian Institute of Information Technology Design and Manufacturing Kancheepuram, India)

In this Paper, we consider a scheduling problem in a network with three nodes and two flows using an Age of Information metric. The paths of the flows are different, and hence, the flows affect the scheduling metrics differently. Each node is equipped with a one-buffer for each flow that passes through it, and the network has general interference constraints. For stochastic packet arrivals, we pose a problem with constraints on energy at each node that aims to minimise the Expected Weighted Sum Age of Information. We formulate the problem as a Constrained Markov Decision Process. The problem can be solved by a linear program, which results in the optimal policy. We also propose a near-optimal heuristic policy and a low-complexity Drift-Plus-Penalty based scheduling policy for the problem. The proposed policies are shown to have a near-optimal average age performance. The proposed heuristic and Drift-Plus-Penalty based scheduling policies have a complexity that increases linearly with the number of feasible scheduling actions.

5:30 ML Based Traffic and Packet Size Prediction for Scalable DBA Algorithms in 50G PONs

Shobhit Khurana and Ayushman Rathor (Birla Institute of Technology and Science Pilani, India); Malhaar Goswami, Nishit Prabhakar Shetty and Sukriti Garg (BITS Pilani, India)

With increasing demand for low-latency and energy-efficient communications in Passive Optical Networks (PONs), classical dynamic bandwidth allocation (DBA) algorithms have found it challenging to accommodate the dynamic nature of traffic flows. The study explores the trend of using machine learning (ML) models, namely, convolutional neural networks (CNNs) and extreme gradient boosting (XGBoost), for timestamp and packet size prediction that would allow for an intelligent DBA. The CNN models that we propose in this work are effective at predicting the packet inter-arrival time, with low mean absolute errors across the different periodic traffic scenarios. Additionally, we propose using XGBoost, an ensemble-based gradient boosting method, to predict packet size and bandwidth demand for large users and a network capacity of 50 Gbps. We can produce an effective root mean square error (RMSE) exhibiting strong predictive power at low computational cost. Our comparative analysis also reveals that XGBoost outperforms conventional DBAs and deep learning (DL) models such as long- and short-term memories (LSTMs) and artificial neural networks (ANN) from the perspective of reduction in delay and training time efficiency. This hybrid architecture allows the implementation of DBA algorithms driven by scalable predictions that achieve delay reduction of up to 10% upstream, a considerable extent that we can consider for next-generation optical access networks.

5:45 Optimal Active/Passive Beamforming and Positioning of RIS for Ship-Shore Communications

Deepthi M (Signal Processing and Communication, India); Poornima S (Cochin University of Science and Technology, India); Deepak S (Signal Processing and Machine Learning, India)

RIS offers a cost-effective solution to overcome the coverage issues of using next-generation communication systems. UAVs with mounted RIS can flexibly provide reliable connectivity to remote areas. In this paper, an RIS-mounted UAV is employed to improve the reliability of ship-to-shore communication. The proposed work aims to jointly optimize the beamforming vector of the transmit antenna (active beamforming), the phase shifts of RIS unit elements (passive beamforming) and the placement of an RIS-mounted UAV to maximize the receiver's capacity. The exact joint optimization for active/passive beamforming and positioning of RIS-mounted UAV is obtained by using Maximum Ratio Transmission (MRT) and Particle Swarm Optimization (PSO). An approximate joint optimization, which maximizes an upper bound of the SNR, is also solved using MRT, semi-definite programming (SDP) with a closed-form solution for optimum position. It is found that when the distance between BS and user is equal to twice the height of UAV (D = 2h), RIS should be placed vertically at the mid-point; for D >> 2h the RIS may be placed directly above the BS or the user. For lower distances, RIS placed vertically at the mid-point, at a fixed UAV-safe height, is best. The two distance optimized joint optimizations show superior performance compared to a benchmark scheme [3] and the case of random beamforming.

Tuesday, October 28 4:30 - 6:00

Track A3F6 CCI 3.1: Computing & Computational Intelligence (CCI) 3.1

Room: F6. 505 Sepilok

Chair: Yu Beng Leau (Universiti Malaysia Sabah, Malaysia)

4:30 A Hybrid Technique for Object Recognition

Rakesh Gangula (SRM University-AP, India); Rituparna Choudhury (International Institute of Information Technology Bangalore, India)

Object detection from a given image is a very important application for drones. The high accuracy in correctly recognizing the objects in an image is also paramount in this crucial application. So, in this paper, an efficient algorithm for the accurate detection of 7 different objects in a frame is proposed. In this model, a hybrid of Convolutional Neural Network (CNN) and Long-Short Term Memory (LSTM) is used for detection. The CNN is used for feature extraction and LSTM is used to perform classification. The hybrid use of two deep learning models integrates the strength of both the deep learning models to achieve high F1-score for all 7 classes. This model is proven to achieve around 96% accuracy which is observed to be much higher than the accuracy obtained by the existing models for the proposed application. This model is also found to perform better than other existing machine learning algorithms tested on the same application.

4:45 Detecting Adversarial Attacks on HiDDeN Watermarked Images

Huan Lin Oh (Nanyang Technological University, Singapore)

Adversarial attacks pose a significant threat to deep learning systems by introducing imperceptible perturbations that cause models to make incorrect predictions. This paper proposes a method to detect such attacks in watermarked images using transfer learning with a pre-trained convolutional neural network (CNN). Instead of focusing on message recovery, the approach detects tampering by analyzing visual distortions in images watermarked using the HiDDeN framework. A ResNet-based classifier was trained on watermarked images to distinguish clean from adversarial inputs. Projected Gradient Descent (PGD), a strong white-box attack, was used to generate adversarial examples. Experimental results show that such perturbations introduce detectable patterns, enabling the classifier to reliably differentiate between clean and tampered images. While the detection model was primarily evaluated on PGD attacks, training with additional examples generated using the Fast Gradient Sign Method (FGSM) improved generalization to weaker perturbations. This work demonstrates a promising direction for integrating neural watermarking with adversarial detection to strengthen the robustness of image-based deep learning systems.

5:00 Lightweight Deep Learning Models for Classification and Adulteration Detection of Philippine Rice Varieties

Joesmart Apan, Jose Miguel D. Domingo, Melvin K. Cabatuan and Edwin Sybingco (De La Salle University, Philippines)

Rice is an essential food source in the Philippines, yet ensuring its quality remains challenging due to the visual similarities among rice varieties. These similarities often lead to mislabeling and adulteration, undermining consumer confidence and affecting market integrity. This study presents a deep-learning approach to automated rice variety classification and adulteration detection. A custom image dataset containing 2,400 samples was acquired, comprising four classes: Dinorado, Malagkit, Sinandomeng, and adulterated rice. Three models were evaluated - DenseNet121, EfficientNetV2S, and Mobile Vision Transformer (MobileViT). EfficientNetV2S achieved the highest performance with a test accuracy of 100%, demonstrating a superior balance of accuracy, training time, and inference speed. It was found to be 45.69% as fast in inference as DenseNet121, which achieved a 99.5% test accuracy. The lightweight MobileViT model, while indicating underfitting, provided a test accuracy of 92% and the fastest inference time, proving to be at least 31% faster than EfficientNetV2S. The results highlight EfficientNetV2S as the most effective and reliable solution for accurate rice variety identification, while also indicating the significant potential of lightweight models like MobileViT for future real-time applications in resource-constrained environments with additional optimization.

5:15 Classification of Arrhythmia by Adopting the Hybrid Convolutional Neural Network and the Long Short-Term Memory

Adam Mohd Khairuddin and Siti Armiza Mohd Aris (Universiti Teknologi Malaysia, Malaysia); Ku Nurul Fazira Ku Azir (Universiti Malaysia Perlis, Malaysia); Noor Jannah Zakaria (Universiti Teknologi Malaysia, Malaysia)

In this research, the hybrid convolutional neural network (CNN) and the long short-term memory (LSTM) algorithms were adopted to classify five types of ECG arrhythmia: (1) normal (N); (2) ventricular ectopic (V); (3) supraventricular ectopic (S); (4) fusion (F); and (5) unknown (Q). The framework consisted of the following three main phases: (1) pre-processing; (2) feature extraction; and (3) classification was used to develop the hybrid classification model. The proposed hybrid CNN-LSTM model was evaluated by using the MIT-BIH arrhythmia database that contains 48 recordings of ECG signals. Random under-sampling was utilized to address the imbalanced classes in the database. The experimental results of the study achieved precision of 98.00%, recall of 98.00%, F1-score of 98.00%, and accuracy of 98.00%. Comparisons with prior studies revealed that the proposed hybrid CNN-LSTM model was able to attain comparable performance. However, challenges in interpreting the hybrid model as well as its generalizability to diverse dataset remains.

5:30 Smart Audio Surveillance System for Real-Time Violence Detection and Alarm Response Using Long Short-Term Memory

Robert G. de Luna (Polytechnic University of the Philippines, Philippines & University of Sto. Tomas, Philippines); Khristel B Biscocho (Polytechnic University of the Philippines, Philippines); Charles Adriel A. Del Rosario (Polytechnic University of the Philippines - Sto. Tomas Campus, Philippines); Mary Mizzy Clare O Elipse (Polytechnic University of the Philippines & PUP -STC, Philippines); Piolo O. Pecho (Polytechnic University of the Philippines, Philippines); Sophia Noviel O. Silvestre (Polytechnic University of the Philippines - Sto. Tomas Campus, Philippines)

Public safety has become the main priority nowadays, and one major area of research interest is violence detection. This study shows the development of a deep learning-based monitoring system for real-time violence detection. The audio classification employs Recurrent Neural Network (RNN), particularly Long Short-term Memory (LSTM), having a dataset of 13,141 audio data gathered using a microphone. The methodology incorporates Data Augmentation, Feature extraction using Mel-frequency Cepstral Coefficients (MFCCS), and Hyperparameter Tuning using Optuna and Hyperband pruning. Evaluation metrics including accuracy, precision score, F1 score, and recall were used to assess the model. The hardware implementation utilizes Jetson Nano, along with a microphone and alarm system for violence detection. The Recurrent Neural Network exhibited the best model, achieving outstanding accuracy of 99.85%. This study depicts the potential of a deep learning-based device in enhancing public security, however, further improvement requires recognizing limitations such as the need for diverse datasets. The study contributes to the increasing potential of AI-powered security systems that offer future advancements in audio-based violence detection.

5:45 Assessing the Impact of Atmospheric CO2 Concentrations on Rainfall Patterns

Rangani Anjana Wijesinghe and Deepika Suranjini Silva (Sri Lanka Institute of Information Technology, Sri Lanka)

This study examines how atmospheric levels of CO2, along with factors such as temperature, humidity, wind, and pressure, influence rainfall patterns in Colombo over 17 months. Using data from the National Building Research Organization and the Sri Lanka Meteorological Department, three machine learning models, such as Random Forest, XGBoost, and LSTM, were tested to predict rainfall. Among them, Random Forest delivered the most accurate results. The inclusion of CO2 data significantly improved the performance of the model. With CO2, the Random Forest model showed lower error rates and a higher R2 value, indicating more accurate predictions. Specifically, R2 improved from 0.886 to 0.921, and with further tuning, reached 0.998. XGBoost exhibited improvements, with R2 rising from 0.570 to 0.657 when CO2 was included, while LSTM saw a modest but consistent gain from 0.266 to 0.346. Even in predictive tests, the R2 increased from 0.558 to 0.660 when CO2 was considered. These results highlight the importance of incorporating CO2 data into rainfall prediction models, especially in regions like Sri Lanka, where it's not yet commonly used. Beyond academic insight, the findings have real-world implications for sectors such as agriculture, aviation, and fisheries, where accurate weather forecasts are crucial and can support more informed climate adaptation strategies.

Tuesday, October 28 4:30 - 6:00

Track A3F7 ETS 3: Engineering Technologies & Society (ETS) 3

Room: F7. 506 Selingan

Chair: Nur Ashida Salim (Universiti Teknologi MARA, Malaysia)

4:30 Automated Lung Lesion Segmentation with Minimal Number of Labeled CT Images

Ishita Maiti, Namrata Kadasi, Nihar Domala and Aman Soni (Indian Institute of Technology Kharagpur, India); Manjunatha Mahadevappa (Old NCC Building & Indian Institute of Technology Kharagpur, India); Nirmalya Ghosh (Indian Institute of Technology Kharagpur, India)

An arduous task in medical image analysis is the automated segmentation of infected regions in a organ (for example, in lungs, segmentation of lung lesion), which is crucial for assessing the volumetric severity of infections and evaluating the effectiveness of subsequent treatments. The affected regions in diseased lung cases, called lung lesions, vary widely in their intensity, texture, contrast, location, and size. The similarity of intensities between the lesion and the healthy surrounding tissue bordering the lung makes lung as well as lesion segmentation more challenging, especially by classical image processing techniques. One solution is machine learning (ML) or deep learning (DL)-based methods, that learn segmentation models from datasets with lung lesion labeled by experts. Unfortunately, acquiring any labeled data in the healthcare sector for ML/DL algorithms is prohibitively costly due to the participation of skilled clinicians. Hence, the current study proposes a Random Forest (RF) based ML-method that utilizes a significantly reduced number of expert-labeled data for lung lesion segmentation. Eight different experiments with varying amounts of labeled data from two challenging benchmark datasets demonstrated encouraging results, comparable to state-of-the-art methods, reaching the highest precision values of 86.4%, recall value of 78.1%, and F1 score of 82.0%.

4:45 Dual-Path Enhancement Framework for Masses and Calcifications in Mammograms: a Quantitative Evaluation of Preprocessing Techniques

Janindu Athukorala (University of Sri Jayawardenepura, Sri Lanka); Didulangani Deepashika (University of Sri Jayewardenepura, Sri Lanka); Tirush Wickramasingha and Senal Inovin Fernando (University of Sri Jayawardenepura, Sri Lanka); Uditha L. Wijewardhana (University of Sri Jayewardenepura & Faculty of Engineering, Sri Lanka); Umaya Bhashini Balagalla (University of Sri Jayewardenepura, Sri Lanka)

Breast cancer is a leading cause of mortality among women and early detection using mammograms is important to increase survival rates. However, due to low contrast and noise in mammograms, image enhancement techniques are required to assist in accurate diagnosis of breast cancer. This study proposes a dual-path image enhancement framework evaluated using the publicly available INBreast dataset and compared against existing mammogram enhancement methods. The proposed mass enhancement pipeline is compared against Contrast Limited Adaptive Histogram Equalization (CLAHE), Haze Reduced Adaptive Technique (HRAT) and Magma colour mapping. The proposed calcification enhancement pipeline is compared against Gaussian and Laplacian filtering. The proposed approach achieved a 72% improvement in Contrast-to-Noise ratio (CNR) and nearly a 100% increase in Structural Similarity Index (SSIM) over CLAHE for enhancement of breast masses. According to the results for enhancement of calcifications, the proposed method obtained an improved peak signal-to-noise ratio (PSNR) by 46% highlighting its potential in assisting the process of accurate diagnosis of breast cancer using mammograms.

5:00 ADASYN Driven Framework for Pothole Detection Using XGBoost

Tarun Kumar and Divya Lohani (DIT University, India); Debopam Acharya (DIT University, Dehradun, India)

Pothole detection is critical for enhancing road safety and enabling proactive maintenance, particularly in developing and densely populated regions. Determining the state of the road surface accurately is crucial for preventing accidents and road infrastructure health monitoring. To address this issue, we propose an Adaptive Synthetic Sampling (ADASYN)-driven framework for pothole detection using the Extreme Gradient Boosting (XGBoost) technique. This smartphone-based sensing system leverages Inertial Measurement Unit (IMU) data, comprising accelerometer and gyroscope readings, for the classification of road surfaces. To mitigate class imbalance, the system incorporates the ADASYN technique, which improves the representation of pothole instances. The augmented dataset is processed using the XGBoost algorithm, achieving a classification accuracy of 98%. The proposed work is cost-effective, scalable, and suitable for real-time deployment in intelligent transportation systems. This work also contributes to the United Nations' Sustainable Development Goals (SDGs), particularly SDG 9 (Industry, Innovation, and Infrastructure) and SDG 11 (Sustainable Cities and Communities) by facilitating safer and more efficient transportation infrastructure.

5:15 Text Neck Syndrome: Electromyographic Analysis of Forward Head Posture Induced Cervical Strain

S Saranya and Sharadha Gopal (Sri Sivasubramaniya Nadar College of Engineering, India); Rakshana R (SSN College of Engineering, India); Shravan Kumar Subbaraman (Sri Sivasubramaniya Nadar College of Engineering, Chennai, India)

Detrimental neck postures are unconsciously adopted during prolonged usage of handheld mobile devices (HHMDs). The strain on the neck muscles and spine from this posture can lead to chronic discomfort and, over time, severe musculoskeletal complications. This condition, commonly referred to as Text Neck Syndrome, has become increasingly prevalent in today's digital world. The main purpose of this study is to develop a wearable custom-made device that actively monitors neck posture to help prevent and mitigate the effects of Text Neck. This device integrates an Inertial Measurement Unit (IMU) to detect head positioning and acts as a monitor to remind users if their bad posture remains unchanged for a fixed period of time. These time and position thresholds are validated using an EMG-based data acquisition system. Detailed EMG analysis has been performed on the data collected to track the onset of fatigue and decide the corresponding time threshold. This provides users with real-time feedback on posture, encouraging prompt correction and inculcation of pristine ergonomics.

5:30 Development of an IMU-sEMG System for Hamstring Analysis in Static Movement Protocols

Michael John M Espino, Ken Marco C. Mercado, John Jacen D. Del Mundo, Carlos Miguel S. Estrada, Aaron Sam A. Gilla, Jan Tyrone Cabrera, Reil Vinard S. Espino, Ma. Belinda C. Fidel, Timothy Nazareno, Jazzmine Gale S. Flores, Warren Denzel F. Cheng, Sophia Nicole R. De Leon, Jairo C. Estopace, Renell Arthur A. Kalalang, Augie Louis A. Pador, Ma. Madecheen S Pangaliman, Consuelo B. Gonzalez-Suarez and Jehiel D. Santos (University of Santo Tomas, Philippines)

Existing data acquisition systems for lower-limb assessment typically rely on single-sensor modalities and device-specific protocols, limits accurate measurement of both knee angles and peak muscle activations during exercises like standing leg curls. To address these limitations, we developed a wireless device integrating IMU and sEMG sensors, synchronizing data via timestamps and dynamic time warping. The Kalman filter is used to obtain the IMU signals, while an adaptive filter is used to denoise the sEMG signals. From the combined IMU and sEMG recordings, clear trends emerge in peak leg curl power as a function of knee flexion angle; to visualize these patterns, we applied K-means clustering (k = 3) to knee flexion angle (40°-115°) versus peak leg curl power (0.2-1.0 W) across all repetitions, which revealed three regimes, low (≈ 60°-80°, 0.5-0.8 W), medium (≈ 80°-95°, 0.7-0.9 W), and high (≈ 95°-115°, 0.7-1.0 W), highlighting typical performance zones. Analysis of the resulting scatter plot showed peak muscle activation during concentric phases at flexion angles of 97°-113°. Prototype knee-angle measurements achieved RMSE 7.37°, MAE 4.76°, ICC 0.98, and Spearman's ρ = 0.9765 against ground truth, confirming the system's reliability in data acquisition. Overall, the integrated system provided reliable measurements of muscle activation and knee angles with acceptable error margins and consistency.

5:45 Integrating FDES-DPSIR Framework for Evidence-Based Climate Risk Assessment and Causal Modeling in Indonesia

Muhammad Miftakhul Romadlon (Monash University Indonesia); Miya Irawati and Taufiq Asyhari (Monash University, Indonesia)

Climate change poses complex and interconnected risks that demand structured, data-driven approaches to support effective adaptation and sustainable development. This study introduces a new integrated framework for assessing climate vulnerability in Indonesia by combining the Framework for the Development of Environment Statistics and the Driving Forces-Pressure-State-Impact-Response model. The framework leverages multi-dimensional data from 2014 to 2023, integrating socio-economic statistics and satellite-derived environmental indicators, which were mapped to the DPSIR structure for subsequent analysis. To analyze causal relationships among vulnerability components, Partial Least Squares Structural Equation Modeling (PLS-SEM) is used, revealing significant pathways along the DPSIR sequence. Notably, socio-economic drivers exerted strong direct effects on environmental pressures, while environmental degradation indirectly influenced institutional responses through its impact on climate-related risks. A composite Integrated Climate Vulnerability Index is developed by applying the Entropy Weight Method to quantify spatio-temporal vulnerability patterns across Indonesian provinces. Results show a national decline in vulnerability between 2014 and 2020, followed by a modest rebound post-pandemic, while regional disparities persisted-with eastern provinces such as Papua and Maluku exhibiting consistently high vulnerability. The framework evaluation demonstrates a promising way of integration of structured frameworks with statistical modeling, envisaging evidence-based approaches in supporting sustainable, region-specific climate adaptation and planning.

Tuesday, October 28 4:30 - 6:00

Track A3F8 CCI 3.2: Computing & Computational Intelligence (CCI) 3.2

Room: F8. 507 Monsopiad

Chair: Yin Qing Tan (Universiti Tunku Abdul Rahman, Malaysia)

4:30 AI-Driven EEG Insights into Music Tempo & Mode Impact on Consumer Behavior

Kirsten Lee and Sin Tian Choong (Hwa Chong Institution, Singapore); Aung Aung Phyo Wai (Nanyang Technological University, Singapore)

This study investigates the effects of background music-specifically, tempo and mode- on consumer decision-making during utilitarian online shopping, using electroencephalography (EEG). To address the limitations of traditional self-report methods, which are often affected by social desirability bias, and to fill the gap in neuromarketing research within e-commerce, we conducted a controlled experiment with twelve healthy participants. Subjects performed a series of low- and high-complexity shopping tasks while recording EEG data. Twelve classical music excerpts, categorized into four non-lyrical tempo-mode conditions, served as auditory stimuli. We extracted band-power features from EEG to differentiate cognition states, complemented by repeated-measures ANOVA and post-hoc analyses. Results showed a significant effect of music condition on decision (F(13,11) = 1.94, p = 0.039), with fast-tempo minor-mode conditions significantly enhancing accuracy in low-complexity tasks compared to fast-tempo major-mode conditions (F(2,22) = 9.91, p < 0.001). However, mode differences were not significant under high-complexity scenarios (t(11) = 0.29, p = 0.777). EEG-based frontal alpha asymmetry revealed positive affective states in non-music, fast-major, fast-minor, and select slow-minor conditions, contrasting negative affect in most other scenarios. Interestingly, these neural affect indicators did not correlate strongly with decision accuracy or subjective valence ratings, suggesting music may have subconscious emotional effects independent of overt self-report measures.

4:45 Improving Intersection Traffic Flow Using Deep Q-Learning Algorithms

Abhinav Mishra, Prabu Mohandas, Aparna Vijayan and Anisha N K (National Institute of Technology Calicut, India)

Efficient control strategies play a vital role in mitigating traffic congestion in urban areas. This study introduces an adaptive traffic signal control approach using Deep Q-Learning (DQN) family techniques, such as, the Double DQN variant. The conventional fixed-time control strategies, whose fixed signal timings often cause inefficiency, are usually not suitable, whereas the models in this paper can use real-time traffic data to achieve the regulated phases of signals in an optimal order. DQN improves decision-making by learning optimal signal times, while Double DQN improves further stability and accuracy by rectifying the Q-value overestimation issue by means of decoupled action selection and evaluation. It learns to improve signal timings based on changing traffic patterns. Experiments conducted using the SUMO simulator demonstrate that the proposed framework significantly reduces cumulative delay by 75% and queue length by 78%. The models are considerably better than fixed-timing schemes and traditional Q-Learning methods, and Double DQN shows better adaptability and stability under oscillating traffic conditions.

5:00 Automated Cabin System for Hygiene Compliance Monitoring with Mask, Hairnet, and Handwash Detection

Francis Jann A Alagon and Collien Princess Pepito (Mindanao State University - Iligan Institute of Technology, Philippines); Elaine Krissnell Miral (Mindanao State University-Iligan Institute of Technology, Philippines); Earl Ryan Aleluya (Mindanao State University - Iligan Institute of Technology, Philippines); Cherry Mae G Villame (Mindanao State University-Iligan Institute of Technology, Philippines); Carl John Salaan (Mindanao State University - Iligan Institute of Technology, Philippines); Jeralyn Alagon (Bohol Island State University, Philippines); Shamsudin Abu Ubaidah (Universiti Tun Hussein Onn Malaysia's, Malaysia)

Non-compliance with hygiene protocols-such as wearing a mask or hairnet, and performing proper handwashing-in food manufacturing facilities contributes to food contamination, thereby compromising product quality, consumer trust, and brand integrity. Manual inspection methods used to monitor compliance are susceptible to human error and lack objectivity. Thus, the need for an automated solution is prominent. In response, this study developed a cabin-based system integrated with two YOLOv8-trained models: one for detecting mask and hairnet usage, and another for recognizing handwashing gestures. These models were deployed on a mini-computer (Dell OptiPlex 3080). The compliance system follows the protocol outlined as follows: (i) personnel identification via RFID scanning of the employee card, (ii) detection of mask and hairnet usage through camera input, (iii) sequential detection of handwashing gestures, (iv) regulation of door access to food manufacturing areas based on the evaluation outcome, and (v) recording of compliance results for supervisory review. The system achieved a mean Average Precision (mAP) of 99.2% for mask and hairnet compliance, and 92.7% for handwashing compliance. These experimental results support the system's potential for deployment in food manufacturing settings to facilitate compliance monitoring and reinforce food safety assurance.

5:15 GRU-OptiCom: Revolutionizing Computation Offloading in Edge Computing Through Meta-Reinforcement Learning with GRU

Aditya Oza (IIIT Naya Raipur, India & No, India); Yash Vardhan Gautam, Anirudh Bhakar, Mallikharjuna Rao K and Kanika Malhotra (IIIT Naya Raipur, India)

Modern mobile devices often struggle with limited computational capabilities, hindering their ability to efficiently process data-intensive applications such as augmented reality, mobile healthcare, and intelligent navigation. Multi-access Edge Computing (MEC) presents a viable solution by enabling the offloading of complex computational tasks to geographically proximate edge servers. This offloading approach alleviates the processing burden on user devices and significantly reduces end-to-end latency, thereby enabling real-time responsiveness. In this work, we propose GRU-OptiCom, a novel task offloading framework that leverages meta-reinforcement learning (MRL) to dynamically optimize offloading decisions across varying environments. The model incorporates a GRU-based sequence-to-sequence neural architecture for capturing task dependencies, and employs Proximal Policy Optimization (PPO) to ensure stable and efficient training. We evaluate GRU-OptiCom using latency as the primary performance metric and compare its performance with baseline methods such as MRLCO and HEFT-based greedy algorithms. Experimental results demonstrate that GRU-OptiCom consistently achieves lower latency and improved task distribution, setting a new benchmark for adaptive and intelligent task offloading in MEC environments.

5:30 EEG-Based Classification of Abnormal Epileptiform Patterns

RH Sanjay (Vellore Institute of Technology Vellore, India); Harshita Patel (Vellore Institute of Technology, India); Dharmendra Rajput (VIT, India)

Electroencephalography (EEG) is a widely used non-invasive technique for monitoring brain activity, especially for diagnosing neurological disorders like epilepsy. However, EEG interpretation remains a complex and time-intensive task due to the high variability and noise inherent in the signals. This study proposes a comparative evaluation of two state-of-the-art classification models-CatBoost, a gradient boosting machine learning algorithm, and WaveNet, a deep learning architecture designed for temporal sequence modeling-for automated detection of abnormal epileptiform EEG patterns. Utilizing the publicly available HMS EEG dataset, both models are assessed on classification accuracy and computational efficiency. Results show that WaveNet outperforms CatBoost in classification accuracy, achieving a cross-validation score of 0.81 versus 0.74, though it incurs significantly higher computational cost. Conversely, CatBoost offers faster inference and enhanced model interpretability, making it more suitable for real-time clinical deployment. The study highlights key trade-offs between performance and efficiency, suggesting potential for hybrid approaches that leverage the strengths of both models to advance reliable, explainable, and scalable EEG-based epilepsy diagnosis systems.

5:45 Disciplinary and Transversal Competencies at Play in Work-Integrated Learning

Chien Ching Lee (SIT, Singapore); Ryan Fraser Kirwan (Singapore Institute of Technology, Singapore)

This study explores students' ability to connect the dots in their disciplinary and transversal competencies in an internship report writing workshop held mid-way through their internship. These students have undergone two 3-month internships and are currently participating in their final 8-month internship, as part of a work-study degree programme. The pre- and post-surveys, and teacher's feedback on their writing reflect that the students were able to apply disciplinary competencies effectively and elaborate on these with instructive details. They valued the opportunity to be involved in complex, industry projects and develop their technical skills. Despite their crucial nature, teamwork and communication challenges were elaborated only briefly; with students struggling to express their difficulties in these areas. This work underlines the need for students to be encouraged to share more on their application of transversal competencies to positively impact their long-term employability. Appropriate formats for them to do so could also be examined.

Wednesday, October 29

Wednesday, October 29 8:00 - 9:30

Track B4F1 CCI 4.1: Computing & Computational Intelligence (CCI) 4.1

Room: F1. Sipadan I

Chair: Rosli Nurfatihah Syalwiah (Universiti Malaysia Sabah, Malaysia)

8:00 Domain-Specific Health Text Generation Through Low-Rank Adaptation of a Transformer Architecture

Kamalesh Debnath and Shrinjoy Das (Assam University, Silchar, India); Mousum Handique (Assam University Silchar, India & Assam University, India); Arnab Paul and Lalzo S. Thangjom (Assam University, Silchar, India); Megha Arakeri (Manipal Institute of Technology Bengaluru, India)

The growing demand for accessible and reliable health information has motivated the adaptation of domain-specific large language models (LLMs). LLMs perform well on general natural language processing (NLP) tasks but require fine-tuning for healthcare applications. In this work, Mistral-7B, a 7.3B parameter Transformer model, is fine-tuned for health text generation and noncritical symptom understanding using three parameter efficient methods-Low-Rank Adaptation (LoRA), Quantized Low-Rank Adaptation (QLoRA), and Rank-Optimized Reliable Adaptation (RoRA). A synthetic dataset comprising medical question answering, symptom descriptions, and home remedies was curated from public sources. Experimental results demonstrate that RoRA achieved the highest BLEU-4 (0.52), ROUGE-L (0.65), and F1-score (0.84), outperforming baselines such as BERT, RoBERTa, and LLaMA- 7B while maintaining low GPU memory usage. This work supports the use of fine-tuned LLMs for safe and efficient health communication, especially in low-resource settings. It also demonstrates that lightweight adaptation using Parameter Efficient Fine-Tuning (PEFT) can deliver high-quality outputs while minimizing computational demands.

8:15 A Region-Specific Nutritional Model Using LSTM Encoder and Attention-Enhanced Decoder

Kamalesh Debnath and Shrinjoy Das (Assam University, Silchar, India); Mousum Handique (Assam University Silchar, India & Assam University, India); Arnab Paul and Lalzo S. Thangjom (Assam University, Silchar, India); Megha Arakeri (Manipal Institute of Technology Bengaluru, India)

Malnutrition is a persistent challenge in rural India, where generic diet recommendations rarely reflect local food habits or economic realities. Many rural families in India rely on regionally available foods, and generic nutrition models typically overlook these dietary patterns. This paper presents an attention-augmented LSTM encoder-decoder model designed to generate affordable, region-specific meal plans for rural communities in Assam, India. A curated dataset was built using regional recipes, local expert input, and automated web scraping of authentic Bengali and Indian sources. By explicitly modeling cultural and seasonal food patterns, our approach ensures meal suggestions are realistic and easy to adopt in daily village life. The lightweight architecture also enables practical deployment in low-resource healthcare settings without sacrificing performance. On a held-out test set, the proposed model achieved strong results with a BLEU-4 score of 0.42, ROUGE-L of 0.54, F1-score of 0.81, and a BERTScore F1 of 0.581, outperforming both transformer and standard LSTM baselines. These results indicate that compact, locally adapted neural models can offer practical nutrition guidance in underserved settings.

8:30 Breast Cancer Detection: a Comprehensive Review of Multimodal ML Datasets

Jayendra Kumar (Vellore Institute of Technology, India); Priyanka Singh (VIT-AP University, India & Victorian Institute of Technology (VIT), Australia); Samineni Peddakrishna (National Institute of Technology Silchar, India); Banee Bandana Das (SRM University Andhra Pradesh, India); Saswat Kumar Ram (SRM University, Amaravati, Andhra Pradesh, India)

Breast cancer continues to be one of the most prevalent and life-threatening diseases affecting women worldwide. Early and accurate diagnosis significantly improves survival rates, and in recent years, machine learning (ML) has emerged as a transformative tool in enhancing diagnostic precision. This paper presents a comprehensive state-of-the-art review of publicly available machine learning datasets specifically designed for breast cancer detection. The review categorizes and analyzes a wide range of datasets including tabular, imaging, and genomic types such as the Wisconsin Breast Cancer Dataset (WBCD/WDBC), BreakHis, Digital Database for Screening Mammography (DDSM), MIAS, CBIS-DDSM, and TCGA-BRCA. Each dataset is evaluated based on key parameters such as data type, feature richness, class balance, sample size, and its suitability for various ML tasks like classification, segmentation, and multi-modal learning. Additionally, the paper outlines the strengths, limitations, and real-world applicability of each dataset, providing critical insights for researchers in selecting appropriate benchmarks for model development. The study also highlights current challenges and suggests future directions for constructing more diverse, annotated, and standardized datasets to support robust and generalizable breast cancer detection systems.

8:45 Opinion Polarization on Social Networks Based on Political Discourse

Susmita Das, Sounak Sadhukhan and Arunabha Tarafdar (Bennett University, India)

Social media platforms have become the nerve center of campaign discussion in regards to global political landscape. Political candidates are wielding social media as an influential instrument to advance their own election campaigns. In this paper, we have perused the online exchanges and conversations among supporters of different political parties in the intense environment of US Presidential Election 2024. Sentiment scores, abusive speech and stance detection of the tweets have been considered for understanding the perspective of voters on social media platforms. Each post has been considered whether it is a hate speech which contributes in polarization in the various conversation obtained from mainstream social media platform X/Twitter. A novel model has been proposed that factors in sentiment, stance and and hate speech for opinion dynamics estimation. Our method has been evaluated on various publicly available datasets and satisfactory results have been obtained. It is observed that there is higher level of opinion polarization when hate speech is involved.

9:00 HyLapDFN: a Hybrid Approach for Infrared-Visible Image Fusion Using Laplacian Pyramid and Decoder Fusion Network

Nithin Eswarappa and Jeevan K. M. (Gandhi Institute of Technology and Management (GITAM), India); Shefali Waldekar (Nirma University, India); Bikram Kumar Vivek (Gandhi Institute of Technology and Management (GITAM), India); Koshy George (GITAM University, India)

Deep-learning (DL) methods are popular in image fusion because of their generalisation abilities and resulting performances. However, these large fusion models restrict their usage in resource-constrained applications such as remote surveillance using UAVs and handheld devices. In contrast, the Laplacian pyramid (LP) method for feature extraction has a much smaller footprint. Accordingly, in this paper, we propose a hybrid Laplacian pyramid and decoder Fusion Network (HyLapDFN) for fusing infrared and visible images. While LP extracts features, the decoder network constructs the fused image. The network is trained unsupervised as a ground truth fused image is unavailable. The performance of HyLapDFN is compared with five state-of-the-art DL-based image fusion methods using eleven image quality metrics. Qualitative analysis of the fused images from all the methods are performed on two sets of images. Additionally the correlation of colour fusion metric (CFM) is evaluated. Additionally, we scrutinise the computational complexities of these fusion methods.

9:15 Particle Swarm Optimization - Artificial Neural Network Model for Predicting Rebar Corrosion in Fiber-Reinforced Concrete

Bon Ryan Aniban and Kevin Lawrence De Jesus (FEU Institute of Technology, Philippines); Dante L Silva (Mapua University, Philippines); Jimmy G. Catanes (Commission on Higher Education, Philippines); Sheina Pallega (National University, Philippines & Mapúa University, Philippines); Donna Ville Gante (FEU Institute of Technology, Philippines); Meriam Leopoldo (Mapúa Malayan Colleges Mindanao, Philippines)

Chloride-induced corrosion (CIC) is a primary reason of deterioration in reinforced concrete (RC), particularly in marine structures which causes cracking, degradation, and decreased service life. Advances in the 4th Industrial Revolution have enabled utilization of machine learning techniques in different fields of civil engineering. This study develops an Artificial Neural Network (ANN) enhanced by Particle Swarm Optimization (PSO) to predict rebar corrosion in polypropylene fiber reinforced concrete (PFRC). Accelerated corrosion tests were performed using the impressed current method on samples with varying polypropylene fiber content, concrete cover (CC), and bar diameter (BD). Experimental results showed that the 3-7-1 network structure (NS) (3 input neurons (IN), 7 hidden neurons (HN), 1 output neuron (ON)) achieved the highest accuracy with correlation coefficient (R) of 0.98969, mean squared error (MSE) of 0.18846, and mean absolute percentage error (MAPE) of 7.832%. Employing the generated connection weights (CW) from the governing model (GM), through Olden's connection weights approach, observed that the concrete cover had the most significant influence on corrosion (-43.231%), followed by bar diameter (33.717%) and fiber content (-23.052%). It highlights that increasing concrete cover and fiber content significantly reduces corrosion in PFRC, which may be used by civil engineering professionals as it offers insights for enhancing the durability of reinforced concrete structures. This approach supports SDG 9 (Sustainable Development Goal 9: Industry, Innovation, and Infrastructure) by promoting resilient, innovative construction methods and contributes to SDG 11 (Sustainable Development Goal 11: Sustainable Cities and Communities) by enhancing the longevity and sustainability of urban infrastructure.

9:30 Medical Datasets for Machine Learning in Brain Tumor Diagnosis and Segmentation: A Review

Priyanka Singh (VIT-AP University, India & Victorian Institute of Technology (VIT), Australia); Jayendra Kumar (Vellore Institute of Technology, India); Samineni Peddakrishna (National Institute of Technology Silchar, India); Banee Bandana Das (SRM University Andhra Pradesh, India); Saswat Kumar Ram (SRM University, Amaravati, Andhra Pradesh, India)

Brain tumor detection through machine learning has gained significant traction due to its potential for early diagnosis, accurate classification, and automated segmentation in clinical settings. The success of such models is closely tied to the quality and availability of annotated datasets. This survey presents a comprehensive review of major publicly available datasets-including BraTS, Figshare Brain MRI, TCGA-GBM/LGG, REMBRANDT, CQ500, and IBSR-highlighting their imaging modalities, annotation protocols, dataset sizes, and clinical relevance. Special emphasis is placed on BraTS for segmentation and Figshare for multi-class classification. Genomics-integrated datasets like TCGA and REMBRANDT support multi-modal learning, while IXI and CQ500 are valuable for pretraining and emergency diagnostic models. The survey identifies key limitations, such as inter-observer variability, class imbalance, and inconsistent annotation formats. It also underscores the need for more diverse, standardized, and richly labeled datasets. By evaluating the strengths and weaknesses of existing resources, this work provides guidance for selecting suitable benchmarks and suggests future directions such as federated learning and synthetic data augmentation to improve clinical robustness.

Wednesday, October 29 8:00 - 9:30

Track B4F2 CSR 4: Control Systems & Robotics (CSR) 4

Room: F2. 501 Kadamaian

Chair: Rozita Jailani (University Teknologi MARA, Malaysia)

8:00 Real-Time Control of a Lab-Based Flexible Rotary Servo System Using Linear Matrix Inequalities

Mousumi Mukherjee (Indian Institute of Engineering Science and Technology, Shibpur, West Bengal, India)

We consider the problem of designing a linear quadratic regulator (LQR) for a lab scale flexible link rotary servo system using linear matrix inequalities (LMIs). First, data from the open loop system is collected in real-time. In particular, data corresponding to the two measured outputs are recorded in response to an input. This input-output data is used for identifying a model of the system. The identified model is compared with the given model for correctness. Once verified, the identified model is used to synthesize a linear quadratic regulator by solving linear matrix inequalities. It is known that the weighting matrices play a crucial role in the LQR design. The effect of the choice of the weighting matrices in the LQR design is examined, and accordingly a suitable pair of weighting matrices are chosen for the design. Thus, a state-feedback controller, based on measured input-output data is obtained. The design is experimentally validated by implementing the controller in real-time.

8:15 Preliminary Theoretical Study on Remote Outdoor Coordinate Measurement via Sensor Fusion

HyungTae Kim and Ohung Kwon (KITECH, Korea (South)); Sangwon Lee (Korea Institute of Industrial Technology, Korea (South))

This study presents a theoretical framework for estimating outdoor coordinates via using close-range laser sensing. The theoretical framework for estimating outdoor coordinates was formulated, incorporating a GPS receiver, a laser distance sensor, and an IMU. The laser distance sensor targets a point on a specific structure and measures the distance from the framework to that point. Then, the targeted GPS coordinate was calculated using the geometric relationships among the measured distance, GPS coordinate, and the framework's tilt angles. The geometric relationships can be described using Euler-angle and geodetic models, and the inverse of the geodetic models was obtained using optimal methods such as the simplex algorithm, gradient descent, and equal search. The spherical and WGS84 models, the most popular in geodesy, were adopted to verify the computations using reliable GPS distance calculators. The proposed method can be applied to surveying construction sites, inspecting large buildings, measuring hazardous places and designing outdoor performances.

8:30 Design and Development of an Integrated Water Spray and Brush System for Solar Panel Cleaning

Maizatul Zolkapli (Universiti Teknologi MARA, Malaysia); Mustaqim Muhizam (UiTM, Malaysia); Azween Hadiera Hishamuddin (Universiti Teknologi MARA, Malaysia); Shahril Irwan Sulaiman (Universiti Teknologi MARA Shah Alam, Selangor, Malaysia); Ahmad Sabirin Zoolfakar, Rozina Abdul Rani and Marianah Masrie (Universiti Teknologi MARA, Malaysia)

Solar energy facilitates the global transition to renewables; however, dirt accumulation can reduce efficiency by 30 percent, and existing cleaning technologies remain ineffective, expensive, and water-intensive. This project aims to develop an innovative solar panel cleaning system that integrates a water spray mechanism and a mechanical brush, all controlled by an Arduino-based system, to overcome these restrictions. The objective was to create an environmentally sustainable, cost-effective, and energy-efficient solution that consumes minimal water while assuring adequate cleaning. The system could autonomously traverse the panel, directed by limit switches that regulate the directional trajectory. The device maneuvered the cleaning brush along the panel via two motors, while a third motor regulated the rotation. The system's performance was assessed by comprehensive testing, emphasizing cleaning efficiency, water usage, and adaptation to various environmental circumstances. The system improves solar panel energy efficiency by decreasing soiling, while conserving water and reducing maintenance expenses, hence providing dependable performance under diverse environmental conditions. This project aims to enhance the efficacy, sustainability, and scalability of solar panel cleaning, thereby significantly benefiting the solar energy sector and promoting the broader adoption of renewable energy technologies.

8:45 Dehazing Algorithm Selection System with Feature Extraction for Single Image

Keizo Miyahara (Kwansei Gakuin University, Japan)

This paper proposes a selection system of dehaze-algorithm to apply an appropriate one for an input single image by means of feature extraction. It is known that the haze is one of the most critical challenges for camera image processing. In order to cope with the negative effect, a number of dehazing algorithms have been researched. Previous studies on both dehazing algorithms and feature descriptors were examined with the aim to select the most suitable algorithm for the input image based on the explicit indices of the image itself. The examined dehaze algorithms can be categorized in two groups: Image restoration based on physical model (GMRF, Optical filtering, DCP, Bayesian net, Pre-record data set) and Image improvement with image parameters (Wavelet, Retinex, High frequency enhancement, Histogram equalizing). The implemented system was validated through a series of object detection experiments using real-world hazy images. The results of the experiments confirmed the improvement of the evaluation metric, mAP, that depicts the effectiveness of the proposed system.

9:00 Cooperative Mapping Method with Distributed Autonomous Mobile Robots in Unknown Environments

Yuma Ikeda and Keizo Miyahara (Kwansei Gakuin University, Japan)

This paper describes a method for collecting on-site information in unknown environments, such as the size and location of obstacles, using a decentralized mapping algorithm with multiple mobile robots. Aiming at initial response to emergency disaster situations, each one of the homogeneous autonomous distributed robots determine own exploration direction by combining data clustering techniques based on only its local map at the time. The mobile robots communicate locally, when they encounter each other in the exploring field, to share the map information to enhance the efficiency of the entire mapping system. Even if some unexplored areas remained, another shape-fitting technique will be applied to the incomplete information obtained, and it enables the system to output the most-likely environment map for the subsequent principal task, such as intended rescue mission. A series of simulations were conducted to verify the proposed cooperative mapping method, and the results of the examination demonstrated its consistency and efficiency.

9:15 Modelling and Control of a 4-DOF Moving Base Manipulator

Aksa Santhosh (APJ Abdul Kalam Technological University, India); Lal Priya P S (College of Engineering Trivandrum, India); Sneha Gajbhiye (IIT Palakkad, India)

Moving-base robotic manipulators are increasingly utilized in various industries due to their flexibility and adaptability in diverse dynamic environments. This work focuses on the modeling and control of a 4-DOF robotic manipulator composed of a planar two-link manipulator mounted on a mobile base capable of translating along the dual axes. The dynamic behavior of the manipulator is derived using the Euler-Lagrange equation, which effectively captures the dynamic coupling between the manipulator and the base. Additionally, kinematic analysis is conducted to accurately determine the end-effector's position during operation. One of the main challenges in this setup lies in dealing with the system's nonlinear characteristics, external disturbances, and the need to manage the dynamic interactions between the base and the manipulator components. To address these challenges, a Sliding Mode Control (SMC) strategy is adopted, chosen for its well-known resilience to system uncertainties and external perturbations. The proposed control framework is implemented within the MATLAB Simulink and Simscape environments, where the entire 4-DOF system is modeled to reflect realistic dynamics and base-manipulator interactions. Through simulation, the effectiveness of the controller in achieving robust and precise motion control is demonstrated.

Wednesday, October 29 8:00 - 9:30

Track B4F3 PES 4: Power, Energy & Electrical Systems (PES) 4

Room: F3. 502 Mesilau

Chair: Nur Atharah Kamarzaman (Universiti Teknologi MARA, Malaysia)

8:00 Reinforcement Learning for Smart Grid Stability Using Adaptive Control and State Abstraction

Sourav Datto, Mustakim Ahmed, Md Eaoumoon Haque, Kazi Redwan, Sajedul Islam and Nasif Hannan (American International University-Bangladesh, Bangladesh); Mohammad Shah Paran (Lamar University, USA); Abu Shufian (American International University-Bangladesh, Bangladesh)

Smart grids are under pressure due to rising energy use, more renewable sources, and unpredictable consumption. When control systems fail to respond in time, they can cause frequency changes, power imbalances, and reduced grid reliability. This paper introduces a reinforcement learning (RL) framework using Q-learning to handle these challenges through real-time, adaptive control. The approach uses Principal Component Analysis (PCA) to reduce data complexity and discretizes continuous variables to make learning more efficient. A custom Markov Decision Process (MDP) models the grid environment, where the agent chooses actions: Increase, Decrease, or Hold based on the current state. A tabular Q-learning algorithm helps the agent learn the best decisions by maximizing rewards over time. Results show that the RL agent improves power stability by 22% over baseline methods and reacts accurately to supply and demand shifts, with action preferences distributed as Increase (58%), Hold (31%), and Decrease (11%). Heatmaps and 3D plots reveal clear action patterns and strong confidence in decisions, with more than 85% of states showing a decisive optimal action. The model adapts well to changes, proving useful for intelligent and stable grid control. This work supports smarter energy systems.

8:15 Load Frequency Control of Multisource Interconnected System Using a Nature-Inspired Optimization Algorithm

Vishal Rathore (Maulana Azad National Institute of Technology Bhopal, India); Dhananjay Kumar (Govt. Engineering College Siwan, India); Anchal Raghuwanshi (Maulana Azad National Institute of Technology (MANIT) Bhopal, India); Sushma Gupta (Maulana Azad National Institute of Technology Bhopal, India)

Load frequency control (LFC) principally entails the appropriate design of controllers. For improved LFC, the controller parameters should be properly tuned. Therefore, this article presents a Honey Badger optimization algorithm (HBA) to guide the controller, which is designed and proposed for LFC of an interconnected system with multiple fuel inputs (ISMFI). The frequency and power deviations of the tie-line act as controller inputs. Synchro-phasor technology is used to measure them. The measured signals are transmitted via communication channels. In this work, the signal transmission time delay is compensated with the Padé approximation method. The sum of integral-time-absolute-error (ITAE) of deviation is set to zero for objective parameter optimization. The ITAE for an ac tie-line is calculated as 0.0594, and settling times (STs) of frequency deviation (FDs) with tie-line power deviation (TLPD) are found to be 6.3894 s, 5.3577 s, and 8.0262 s. While for ac-dc parallel tie-line ITAE is 0.0711, and the minimum values of STs of FDs with TLPD are 5.2501 s, 16.6955 s, and 13.1596 s. The simulation results of the proposed algorithm are then compared with the recently reported algorithms; for each case, the time domain simulations are demonstrated. Furthermore, the performance of the proposed HBA-based controller is tested for random step load changes.

8:30 Accurate Fault Classification and Location Identification Using Various Machine Learning Models for Self-Healing in Smart Grids

P. Swati Patro (Birla Institute of Technology and Science, Pilani, Hyderbad Campus, India); N Praneeth and STP Srinivas (Birla Institute of Technology and Science Pilani, Hyderabad Campus, India)

Parametric monitoring using machine learning techniques offers a promising solution for accurately detecting and classifying faults in electric power grids, providing greater precision and efficiency than traditional methods while supporting the development of resilient, self-healing systems in smart grid environments. This paper evaluates the applicability and performance of decision tree, random forest, XGBoost, feedforward neural networks, and long short-term memory models for real-time fault detection, classification, and localization in power system protection using the IEEE 9-bus test system. The test system is designed in DIgSILENT PowerFactory, and data is obtained from electromagnetic transient (EMT) simulations of three-phase voltage and current signals under both prefault and post-fault conditions. Various short circuit fault types and locations are considered to enhance model generalization. The machine learning models are trained on these EMT time series signals, and their effectiveness is analyzed in terms of accuracy, robustness, and computational efficiency. The results offer valuable insights into the capabilities and limitations of each model in supporting intelligent fault management in modern power systems.

8:45 Real Time Analysis of Solar Based Electric Vehicle Charging Station

Rakeshwri Agrawal (Lakshmi Narain College of Technology, Bhopal, India & Maulana Azad National Institute of Technology, Bhopal, India); Vishal Rathore and Sushma Gupta (Maulana Azad National Institute of Technology Bhopal, India); Mukesh Kirar (MANIT, Bhopal, India)

The continuous depleting petroleum resources and hike in their prices have diverted the attention towards Electric Vehicles (EV). Moreover, EVs are ecofriendly leaving minimal carbon footprint into the environment, and with the technological updates and government incentives it is most widely adopted in the present era. The growing number of EVs has put in the additional burden on the utility grid, potentially causing voltage fluctuations. Hence independent hybrid charging stations are being developed which can be energized via renewable resources. This paper presents a topology for solar based hybrid charging station for EV. The performance of the charging station has been analysed under variable irradiations and its effect is studied on the battery system of the EV and results are validated through OPALRT OP-5600. A dual-switch bidirectional DC-DC converter is designed to maintain the DC-bus voltage of the charging station, the performance of which is also analyzed under variable solar irradiances.

9:00 Optimized Sizing and Impact of BESS and PV on Grid Frequency Under Varying Droop Coefficients with Partial Load Shedding Mechanism

Irfan Ahmed (Bangladesh University of Engineering and Technology, Bangladesh); Ahmed Zubair (Bangladesh University of Engineering and Technology, Bangladesh & Rice University, USA)

Along with the increasing adoption of renewable energy sources, maintaining grid continuity has become a growing concern. As solar photovoltaic (PV) systems and battery storage systems (BESS) proliferate, their contribution toward increasing system resilience and offering frequency response is becoming critical. This research focuses on frequency regulation and assesses how BESS increases stability by modifying the droop coefficient in primary frequency control. We present a simulation-based analysis for different droop settings and construct a BESS sizing algorithm to mitigate a 0.5 Hz frequency dip and advocate a partial load-shedding approach with BESS aid. In addition, the integration of PV systems contributed to a significant reduction in the required size of the BESS, as the PV systems were able to directly supply a portion of the energy demand. The findings demonstrate that adaptive droop improves frequency response, optimal BESS size enhances energy efficiency, and load-shedding with BESS support reduces the burden of excessive load shedding to increase reliability and balance in the system.

9:15 Enhanced Load Frequency Control in Interconnected Power Systems Using Shuffled Shepherd Optimization Algorithm (SSOA) for PIDn Tuning

Jonathan David G Quiling (Mindanao State University - Iligan Institute of Technology, Philippines & GNPower Kauswagan Ltd. Co., Philippines); Rovick Tarife (Mindanao State University- Iligan Institute of Technology, Philippines)

This study presents an improved Load Frequency Control (LFC) strategy for a two-area thermal power system by employing a PID controller with derivative filtering (PIDn) optimized using the Shuffled Shepherd Optimization Algorithm (SSOA). The SSOA algorithm leverages a combination of adaptive step-size adjustment and multi-community guidance to achieve a balance between global exploration and local exploitation in the search space. The main objective is to reduce frequency fluctuations and tie-line power deviations arising from sudden load variations, particularly under nonlinear conditions such as Generator Rate Constraints (GRC). The effectiveness of the proposed SSOA-PIDn controller is validated through simulation studies under two different operational scenarios. Comparative analysis reveals that the SSOA-based tuning achieves superior performance over classical and several well-known metaheuristic optimization methods in terms of Integral of Time-weighted Absolute Error (ITAE) and system settling time. Furthermore, sensitivity analysis under parameter uncertainties confirms the robustness and reliability of the proposed method for practical deployment in modern interconnected power systems.

Wednesday, October 29 8:00 - 9:30

Track B4F4 ECD 4: Electronics, Circuits & Devices (ECD) 4

Room: F4. 503 Dinawan

Chair: P. Susthitha Menon (Universiti Kebangsaan Malaysia, Malaysia & Institute of Microengineering and Nanoelectronics (IMEN), Malaysia)

8:00 Energy Efficient Negative Capacitance L-Shaped Tunnel Field Effect Transistor

Alok Kumar Kamal (ABV-IIITM Gwalior, India); Neha Kamal (SIIC IIT Kanpur, India); Avinash Lahgere (Indian Institute of Technology Kanpur, India); Somesh Kumar (ABV IIITM Gwalior India, India)

In this paper, we have proposed a negative capacitance (NC) L shaped channel tunnel field-effect transistor (NC-LTFET), which consists of a hafnium oxide (HfO_2) based ferroelectric (FE) layer in the gate stack. The presence of polarization phenomena in the FE layer tends to enhance the internal voltage and electric field, which result higher ON-state current and steeper subthreshold swing (SS). From well calibrated 2-D TCAD simulation results, it is revealed that the NC-LTFET outperforms the conventional LTFET in terms of both static and dynamic energy dissipation. The NC-LTFET exhibits ∼ 430 × and ∼ 10^4 × higher ON-state current and I_ON /I_OFF ratio, respectively, as compared to the conventional LTFET. In addition, the proposed NC-LTFET shows ∼ 2.2 ×, ∼ 10^2 × and ∼ 10^2 × low SS, switching delay and energy delay product (EDP), respectively as compared to the conventional LTFET. As a result, NC-LTFET is 10 × higher energy efficient for both memory and logic designs switching at ultra-low supply voltage (< 0.2 V) when compared to the conventional MOSFET and LTFET.

8:15 Design, Optimization and Verification of the AMBA APB4 Protocol for Low-Power SoC Applications

Avneesh Singh and Alok Kumar Kamal (ABV-IIITM Gwalior, India); Somesh Kumar (ABV IIITM Gwalior India, India)

The Advanced Peripheral Bus (APB4) protocol, a component of the AMBA 4 specification, is designed to connect low-bandwidth peripherals to high-performance system-on-chip (SoC) designs. This paper presents the design, implementation, and verification of the APB4 protocol using Verilog HDL and SystemVerilog-based Universal Verification Methodology (UVM). We extend the standard APB4 protocol by incorporating power gating techniques and burst-mode support to enhance power efficiency and throughput. Simulation and synthesis were performed using Xilinx Vivado and Synopsys VCS, with verification data showing 100% functional coverage and zero protocol violations. Simulation results using ModelSim and Synopsys VCS confirm accurate protocol behavior, while synthesis using Xilinx Vivado and power analysis via XPower Analyzer demonstrate a 66% reduction in dynamic power and 7% area optimization compared to prior APB implementations. This work positions the APB4 protocol as a viable low-power solution for energy-constrained SoC designs, providing a reusable and modular verification infrastructure aligned with modern digital design flows.

8:30 Interactive Pattern Repetition Game Design Utilizing Real-Time Hardware Pseudo-Random Number Generator

Tee Hui Teo (Singapore University of Technology and Design, Singapore); Maoyang Xiang (8 Somapah Rd & Singapore University Technology and Desgin, Singapore); Kai Cao, Yiyang Fu, Si Yuan Lai, Qistina Binte Mohd Sahril, Samuel Lim and Wei Rui Ho (Singapore University of Technology and Design, Singapore)

This paper presents the design and implementation of a hardware-based memory game developed on an FPGA platform to promote cognitive stimulation among older adults. The game features a 3×3 LED and switch matrix that displays pseudo-random light patterns that users must recall and replicate. To enhance randomness, a seed is extracted from ambient electromagnetic noise using an Analog-to-Digital (ADC) and fed into a multi-Linear Feedback Shift Register (LFSR) pseudo-random number generator (PRNG). The PRNG employs XOR-combined LFSRs initialized with non-overlapping seed transformations to ensure statistical independence. A finite state machine (FSM) governs the game logic, translating 9-bit PRNG outputs into spatial LED patterns and evaluating user input for correctness. Statistical analyses of PRNG outputs, using normality tests and Central Limit Theorem-based smoothing, verify the Gaussian conformity and entropy quality of generated sequences. The fully integrated system includes custom PCBs for ADC input, a mechanical button interface, and an LED display, housed in a 3D-printed enclosure. Results from both simulation and hardware validation confirm the system's effectiveness in generating high-quality randomness and delivering a simple, tactile, and accessible cognitive exercise platform.

8:45 Incorporating FPGA-Driven Pseudo Number Generator into Python Tetris Game

Tee Hui Teo (Singapore University of Technology and Design, Singapore); Maoyang Xiang (8 Somapah Rd & Singapore University Technology and Desgin, Singapore); Yiyang Fu, Zhengyao He, Xavian B Muhammad Yunos, Wei En Phua, Sarah Cherian and Ahmad Naufal Bin Rozaini (Singapore University of Technology and Design, Singapore)

This project focused on the development of a Tetris-like game that incorporates a pseudo-random number generator (PRNG) implemented on a Field Programmable Gate Array (FPGA) to enhance the gameplay experience. By utilizing a hardware-generated seed instead of relying solely on software-based randomness, the project introduces a novel approach to determining the sequence in which Tetris blocks appear on the screen. This integration of hardware and software not only adds a layer of complexity to the game but also showcases the seamless real-time communication between a digital system powered by an FPGA and an interactive Python-based gaming application. Overall, this project represents a significant step forward in the realm of game development by showcasing the potential of integrating hardware and software technologies to enhance gameplay and create more engaging experiences for players. The fusion of FPGA-driven hardware components with a Python-based game exemplifies the innovative spirit driving advancements in the gaming industry, paving the way for future developments that blur the lines between virtual and physical gaming environments.

9:00 A Novel and Simple Measurement Technique for Two-Wire Resistive Sensors in Remote Applications

Elangovan K (IIITDM, Kurnool, India)

This work presents a simplified and accurate measurement technique for two-wire resistive sensors placed in remote locations, addressing the challenge of cable resistance. The proposed circuit utilizes bipolar current excitation and diode switching to generate a square-wave output voltage, from which the sensor resistance is extracted through voltage averaging. Simulation results confirm high precision with a nonlinearity of 0.0006 % and a relative error of 0.007 %, while cable resistance dependency shows a maximum error of only 0.004 %. Experimental validation using RTD-Pt100 characteristics over a temperature range of -100 °C to 850 °C demonstrates linear performance with nonlinearity and relative error within 0.44 % and 0.91 %, respectively. The circuit also shows limited sensitivity to cable resistance variations, maintaining output errors below 0.28 %. With its simple architecture, low component count, reduced power consumption, and cost efficiency, the proposed method offers an alternative for accurate remote measurement of resistive sensors.

9:15 A Power Efficient One-Shot Rectangular Pulse Generator for Temperature Sensor

Kuntal Chakraborty (National Institute of Technology Arunachal Pradesh, India); Subhajit Das (University of Engineering & Management, Kolkata, India); Abir Chatterjee (UEM, India); Abir J Mondal (National Institute of Technology Arunachal Pradesh, India)

This work presents a power efficient one-shot rectangular pulse circuit made up of a delayed step pulse generator (MDSPG) followed by a variable rectangle pulse width generator (VRPWG) with auto termination. The MDSPG generates a delayed feedback signal and the VRPWG generates variable rectangular pulse with four different pulse width options. At 90-nm CMOS technology, the design occupies 0.0058 mm2 and limits the power consumption between 5.78 μW to 5.81 μW at modes M0 and M3, respectively. Additionally, the proposed architecture ensures a large span of rectangular pulse width ranging from 155.66 μs to 1247.96 μs at M0 and M3, respectively. The proposed design is a suitable choice for a multi-mode resolution and multi conversion time-domain temperature sensor. A subthreshold time-to-digital converter is accommodated to realize the sensor in a 90-nm CMOS and the area is limited to 0.011 mm2. Lastly, the resolution varies between 0.29OC and 0.036OC at modes M0 and M3, respectively.

9:30 Smart Temperature Tracker for Thermo-Regulatory Disorders

Olanrewaju B Wojuola and Kutlwano Benedict Tigele (North-West University, South Africa)

Monitoring body temperature is crucial for detecting illness and evaluating the effectiveness of treatment. Particularly, temperature monitoring plays an important role for those with thermo-regulatory disorders, as accurate temperature monitoring is crucial for managing their conditions and for preventing serious complications. This paper proposes a temperature monitoring system that can be used for assisting individuals experiencing such a disorder. We also present test results for a prototype that we developed. The prototype monitors temperature in real time, records temperature data every five minutes and sends alerts to users and medical services during severe hyperthermia or hypothermia. The temperature monitoring system is designed to operate efficiently with low power consumption while providing real-time alerts for abnormal temperature conditions. Utilizing an Arduino board, a temperature sensor, and an OLED display, the system integrates advanced sleep modes and user interaction features. The designed prototype has demonstrated good accuracy and reliability in measuring body temperature when compared to both clinical digital thermometers and infrared gun thermometers.

Wednesday, October 29 8:00 - 9:30

Track B4F5 CS 4: Communication Systems (CS) 4

Room: F5. 504 Madai

Chair: Mohamad Yusoff Alias (Multimedia University, Malaysia)

8:00 Logarithmic Hyperbolic Cosine Adaptive Filter with Variable Center Based Channel Estimation Under Non-Zero-Mean Non-Gaussian Noise

Mahima Chouksey (Madhav Institute of Technology and Science Gwalior, India); Sandesh Jain (ABV-IIITM Gwalior, India)

The design of robust adaptive filters is critical for applications such as system identification, channel estimation, noise suppression, and channel equalization, especially under non-Gaussian/impulsive noise environments. The existing maximum correntropy criterion, hyperbolic cost adaptive filter (HCAF), and logarithmic HCAF (LHCAF) based adaptive algorithms deliver suboptimal performance for non-Gaussian distortions having non-zero mean due to the incorporation of order statistics about the origin. In this paper, we propose a novel adaptive filtering algorithm called LHCAF with variable center (LHCAF-VC), which is robust against impulsive noise with non-zero mean, as it considers central order moments of the error distribution rather than origin-based moments. To further enhance convergence performance and adaptability in dynamic environments, a variable step-size (VSS) mechanism is also integrated into LHCAF-VC, resulting in a new VSS-LHCAF-VC algorithm. Simulation results for the channel estimation task under Gaussian-mixture noise conditions demonstrate that the proposed LHCAF-VC and VSS-LHCAF-VC algorithms achieve superior performance compared to existing state-of-the-art methods.

8:15 Learning to Identify RF Devices from Few Pilots via Reptile

Longqi Shen, Tomotaka Kimura and Jun Cheng (Doshisha University, Japan)

We examine a neural network-based system designed to identify $K$ devices, each with device-specific I/Q imbalances in the RF modulator, as they transmit signals to an access point using time-division multiple access. Traditional methods begin training with randomly chosen initial parameters and need numerous pilot symbols, which can be impractical in scenarios such as when a unmanned aerial vehicle (UAV) serves as the access point, collecting data from massive device sensor networks. In this work, we focus on adaptive learning with minimal pilot symbols to train the neural network to classify devices by their I/Q imbalances. Reptile is an efficient meta-learning algorithm designed to help models rapidly adjust to new tasks with minimal training data. By employing Reptile, we pre-train neural network model parameters offline, facilitating rapid online adaptation and fine-tuning for effective identification of new devices. Simulations indicate that Reptile surpasses conventional methods in device identification with fewer pilot symbols.

8:30 Machine Learning Models Accuracy Study Using P4 Programmable Data Plane in SDN - IOT Networks

Yashwanth A Doddegowda and Vidya Sagar Thalapala (Indian Institute of Information Technology Kottayam, India); Preeth Raguraman (IIIITDM Kancheepuram, India); Koppala Guravaiah (Indian Institute of Information Technology Kottayam, India)

Programmable data plane is the current trending area of research in networking. P4 is a programming language is using to handle different issues in data plane such as flow management, traffic management, and firewall management. Distributed Denial of Service (DDoS) attack is one of the crucial attacks in the network. Handling DDoS attacks at router level give more security to the network. This paper explores how the attack detection can be handled at router level in SDN-IoT Network. The proposed paper analyzes three different machine learning algorithms such as Decision Tree, Logistic Regression, and Tsetline Machine models on programmable data plane. Each machine learning model is implemented in a P4 programmable data plane environment using the BMv2 behavioral model and tested on a Mininet test-bed. The models uses the standard IoT data set of ICMP-based DDOS traffic features. The analysis explores the accuracy, memory efficiency, and feasibility for real-time deployment. The study proves that, novel adaptation of the Tsetlin Machine model into P4 logic, demonstrating its effectiveness and practicality for next-generation programmable networks.

8:45 QUIC-AID: Adaptive Intrusion Detection for QUIC Traffic Using Online Learning with ADWIN-Based Drift Detection

Carlo M Alamani, Mary Crizelle Jamielane T Figueroa, John Raphael Mundo and Jaybie A. de Guzman (University of the Philippines Diliman, Philippines)

QUIC is a transport protocol that is gaining adoption among content providers, offering reliability and speed by building an encryption layer similar to TCP's atop a UDP framework. Due to the vulnerabilities of both protocols, the demand for a QUIC-based Network Intrusion Detection System (IDS) continues to grow. QUIC-AID, an adaptive IDS framework designed for QUIC traffic, is proposed. Labeled network traffic flows were collected through a simulated environment for benign traffic and two types of QUIC attacks: flooding and fuzzing. QUIC-AID incorporates (1) dynamic feature selection-OFS and FIRES, (2) an online learning classifier-Adaptive Random Forest (ARF), and (3) drift detection-Adaptive Windowing (ADWIN). Prequential evaluation on the collected QUIC dataset (390,000 flows) showed that QUIC-AID outperformed other benchmarks (KNN and OC-SVM) in terms of true positive detection with a false positive rate below 0.1%. It also yielded the best results for ARF, achieving 99.94% recall. With dynamic feature selection, the rate of byte flow during attacks consistently had the highest weighting feature through FIRES, whereas OFS shifted weights based on the attack type.

9:00 Design of Optimal Reflection Coefficients and Low-Complexity Equalizer for IRS-OTFS System

Rakesh Kumar Yadav and Sai Kumar Dora (IIT ISM Dhanbad, India); Himanshu Bhusan Mishra (IIT (ISM) Dhanbad, India); Samrat Mukhopadhyay (IIT ISM Dhanbad, India)

For high Doppler scenarios, intelligent reflecting surface (IRS)-aided orthogonal time frequency space (OTFS) systems exhibit enhanced performances in terms of bit-error rate (BER), achievable rate (AR), signal-to-noise ratio (SNR), etc. These performances can be achieved by optimally designing the IRS coefficients through solving proper optimization problems. Note that in the existing literature, for IRS-OTFS systems, the optimal reflection coefficients were designed by minimizing the BER, which may not achieve a highly spectral-efficient system. Therefore, in this work, we design reflection coefficients by developing an AR optimization framework. We propose a root-mean squared propagation (RMS-prop) approach to solve this optimization problem. On the other hand, OTFS system comprises a high dimension delay-Doppler matrix, which can increase the computational complexity of linear equalization techniques. Thus, in this work, we also design low-complexity linear-minimum-mean-squared-error (LMMSE) equalizers, for OTFS system (with and without IRS), which relies on the principle of Cholesky-based decomposition. Our simulation results demonstrate the efficacy of the proposed AR optimization framework and low-complexity equalizers in terms of AR, BER and computational complexity, compared to the existing state-of-the-art techniques.

9:15 Possibilities of a Novel DIO Duplication Attack in Multi-Instance RPL Networks: an Empirical Study

Renya Nath N (National Institute of Technology Calicut, India); Jaisooraj J (Amrita Vishwa Vidyapeetham, Coimbatore); Hiran V Nath (National Institute of Technology Calicut, India)

The Internet of Things (IoT) is no longer a figment of imagination. With more ``things" acquiring IP connectivity, the scope of IoT applications is also widening. However, with greater opportunities presented by the IoT applications come equally critical security concerns. Low-power and lossy networks (LLNs), being a key IoT enabling technology, have to be immune to attackers for the IoT applications to work without disruptions. The IPv6 routing protocol for LLNs must ensure secure communication among IoT devices, as it is considered the de facto IoT routing protocol. The current literature concerning RPL lacks the exploration of attack possibilities in a multi-instance RPL context. This paper presents a novel DIO duplication attack at the LLN Border Router (LBR) level in an LLN with multiple RPL instances. The paper analyses the attack on three attack scenarios based on the number and position of malicious nodes. The simulation results provided in this paper justify the effects of the proposed DIO duplication attack on packet delivery ratio, power consumption of nodes, end-to-end delay, and packet overhead for all three attack scenarios.

Wednesday, October 29 8:00 - 9:30

Track B4F6 CCI 4.2: Computing & Computational Intelligence (CCI) 4.2

Room: F6. 505 Sepilok

Chair: Lorita Angeline (Universiti Malaysia Sabah, Malaysia)

8:00 Design and Implementation of an AI-Driven Academic Path Forecasting System Using Sequential and Classification Models

John Heland Jasper Ortega and Abigail Lopez Alix (FEU Institute of Technology, Philippines)

An AI-driven academic path forecasting system is proposed to support data-informed advising and early academic intervention in higher education. In the Philippine context, where delayed graduation, student dropouts and lack of personalized academic guidance persist, machine learning in education offers a scalable and intelligent solution. The system combines three educational data mining techniques: a Long Short-Term Memory (LSTM) network for course sequence prediction, a decision tree classifier for student progress classification as regular or irregular and a K-Means clustering algorithm for grouping students based on academic trajectories. These models are developed in TensorFlow and deployed on a web platform built with CodeIgniter, enabling functionalities such as academic path forecasting, curriculum tracking and real-time risk alerts. Evaluation shows that the LSTM model achieves strong precision and recall in predicting next-term courses, while the decision tree classifier accurately detects off-track students with interpretable decision rules. K-Means clustering reveals meaningful groupings aligned with academic outcomes, further supporting early identification of at-risk learners. Confusion matrix analysis confirms high model accuracy across tasks. By integrating AI into higher education through course prediction, student classification and cluster-based insights, the system offers a practical framework for enhancing student success through targeted academic support.

8:15 Flood Induced Economic Damage Assessment from Satellite Imagery Using Vision Transformers

Md. Ashrif Rahman Arian, Md. Mehedi Hasan Shishir, Sadman Islam Chowdhury Samin and Shahnewaz Siddique (North South University, Bangladesh)

Every year floods cause substantial threats to lives, livelihoods, agriculture and infrastructure. Rapid assessment of economic damage caused by floods is necessary for disaster management, resource allocation and policy making. In this study, we propose a novel method for calculating flood induced economic damage using before and after flood satellite imagery. By leveraging Vision Transformer techniques, we perform semantic segmentation to identify land cover changes after the disaster. By measuring the area loss per class and assigning economic value to each class, we provide a method to estimate the monetary damage due to the disaster. We used Segformer B3 for segmentation which is a model of the Vision Transformer and achieved much higher pixel accuracy (0.98) and mIoU (0.53) compared to state of the art segmentation models UNet and DeeplabV3+. Moreover, Segformer B3 demonstrated considerably higher computational efficiency compared to the two other models experimented. Our approach offers an innovative and automated solution for post disaster flood damage assessment.

8:30 Edge-Optimized Machine Learning Model for Real-Time Prediction of Feed and Water Intake Using Multimodal Sensor Data

Lalith Reddy Tekulapalli, Anshika Verma, Aditya Ray Baruah, Varshith Srinivasa Peddada, Diptanshu Malviya, Likhita Paul Indupalli and Anakhi Hazarika (BITS Pilani Hyderabad Campus, India)

The growing integration of digital technologies in agriculture is reshaping livestock farming by enabling automation, data-driven decision-making, and real-time monitoring. Among critical tasks, the timely and accurate prediction of feed and water intake is essential for ensuring animal health, early disease detection, and sustainable resource management. Traditional manual methods are labor-intensive, error-prone, and lack responsiveness to dynamic conditions, while existing smart solutions often rely heavily on cloud infrastructure that introduces latency, increasing operational costs, and limiting usability in rural or resource-constrained areas. This paper presents a lightweight, edge-compatible machine learning (ML) model for real-time prediction of livestock feed and water intake using multimodal sensor data. The proposed approach emphasizes optimizing the model size to minimize dependence on cloud infrastructure to make it suitable for deployment in connectivity-limited farm environments. The use of systematic feature selection and correlation-based modeling enhances the interpretability and accuracy of the ML models, including Random Forest, CatBoost, XGBoost, and Neural Networks. Experimental results validate the performance of the proposed solution for feed and water intake prediction that enables timely and autonomous interventions and promotes operational efficiency, sustainability, and scalability in modern farming practices.

8:45 Modified Viterbi Algorithm for Religious Text: A Part-of-Speech Tagging for Waray-Waray

Jeneffer A Sabonsolin (FEU Institute of Technology, Philippines); Robert R Roxas (University of the Philippines-Cebu, Philippines); Ace Lagman (FEU Institute of Technology, Philippines)

Part-of-speech tagging (POS) is a vital process in natural language processing, enabling the identification of grammatical categories within sentences. This research emphasizes the lack of attention given to POS tagging for Asian languages, particularly Waray-waray. Limited studies on Waray-waray religious texts have hindered linguistic documentation and the deeper understanding of its grammar and vocabulary. To address this gap, the study introduces a POS tagging system for Waray-waray utilizing a Modified Viterbi Algorithm, which also incorporates a strategy for handling unfamiliar words. Evaluated on a corpus of 50,000 religious text datasets, the algorithm demonstrates outstanding performance-achieving an accuracy of 93%, precision of 90%, recall of 90.52%, and an F1 score of 92%. These results underscore the algorithm's effectiveness in navigating linguistic challenges across specialized genres. Beyond technical contributions, the study promotes linguistic diversity and fosters inclusive language technologies, advancing the goals of the Sustainable Development Goals (SDGs). Specifically, it enhances language learning and literacy among Waray-waray speakers, supports inclusive education through computational tools for minority languages, and aligns with SDG 4 by providing foundational resources for mother-tongue instruction and educational content development. Additionally, it offers new insights into Waray-waray's grammatical structures, laying a robust groundwork for future linguistic and computational research. Beyond technical contributions, the study promotes linguistic diversity and fosters inclusive language technologies, advancing the goals of the Sustainable Development Goals (SDGs). Specifically, it enhances language learning and literacy among Waray-waray speakers, supports inclusive education through computational tools for minority languages, and aligns with SDG 4 by providing foundational resources for mother-tongue instruction and educational content development. Additionally, it offers new insights into Waray-waray's grammatical structures, laying a robust groundwork for future linguistic and computational research.

9:00 Design and Optimization of Graph Neural Networks for EEG-Driven Anxiety Classification at the Edge

Mugdha Gupta, Eshaa Aranggan, Kavya Ganatra, Abinav Venkatagiri, Chinmayee P, Sameera M Salam and Anakhi Hazarika (BITS Pilani Hyderabad Campus, India)

The growing burden of anxiety disorders highlights the urgent need for scalable and non-invasive systems for mental health monitoring. Electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs) offer a promising solution by capturing neural oscillations linked to anxiety. However, conventional detection methods oversimplify the brain's graph-structured connectivity and are computationally intensive, limiting their feasibility for real-time, edge-based deployment. To address these limitations, we propose two edge-optimized Graph Convolutional Network frameworks (GatedGCN and GAT) for anxiety classification using EEG signals. By modeling multi-channel EEG data as dynamic graphs, the system captures spatial-temporal brain dynamics critical for detecting anxiety-related patterns. The architecture incorporates adaptive graph construction, hierarchical spatio-temporal convolutions, and quantization-aware training to enable a reduced-size inference model with minimal accuracy loss. Our approach achieves real-time, resource-efficient performance on low-power edge devices that enables continuous, private, and accessible anxiety monitoring to pave the way for practical mental health interventions in wearable and mobile healthcare settings.

9:15 SOC Estimation in Electric Vehicles: a Comparative Evaluation of Kalman Filter and Coulomb Counting Methods

Hasanur Zaman Anonto (American International University-Bangladesh, Bangladesh); Md Ismail Hossain (American International University Bangladesh, Bangladesh); Azad Shahriyar, Tanay Banik and Abu Shufian (American International University-Bangladesh, Bangladesh); Md Mukter Hossain Emon and Md Akteruzzaman (Lamar University, USA)

The accurate estimation of the state of charge (SOC) in batteries is a critical component of battery management systems (BMS), especially in electric vehicles (EVs), where it directly impacts the efficiency, longevity, and safety of the system. This paper investigates two widely used SOC estimation techniques: the Coulomb counting method and the Extended Kalman filter (EKF) algorithm. The Coulomb counting (CC) method estimates SOC by integrating the battery current over time, making it simple and computationally efficient. However, it suffers from errors due to inaccuracies in the initial SOC estimate and does not account for battery self-discharge. In contrast, the Kalman filter algorithm is a dynamic estimation technique that uses a probabilistic model to estimate SOC, providing more accurate results even in noisy measurements and initial errors. This study compares both techniques by evaluating their performance in terms of accuracy, adaptability, and computational complexity in various battery usage scenarios. The Coulomb counting method, starting with an initial SOC estimate of 80%, shows a maximum estimation error of 15% over a six-hour charge-discharge cycle. The Kalman filter, initialized with a SOC of 80%, converges to the real SOC value of 50% within 10 minutes, achieving an estimation error of less than 5%. The results show that while Coulomb counting is effective in short-term applications with accurate initial estimates, the Kalman filter excels in long-term SOC estimation, offering superior performance in dynamic and noisy conditions. The paper concludes by discussing the practical applications of both methods and providing recommendations for choosing the appropriate technique based on specific system requirements.

Wednesday, October 29 8:00 - 9:30

Track B4F7 ETS 4: Engineering Technologies & Society (ETS) 4

Room: F7. 506 Selingan

Chair: S.M. Anisuzzaman Status (Universiti Malaysia Sabah, Malaysia)

8:00 Capsule Neural Network for Inertial Sensor-Based Autism Spectrum Disorder Detection Through Multiple Gait Activities

Jayeeta Chakraborty (KIIT University, India); Anup Nandy (National Institute of Technology Rourkela, India)

The cerebellar deficit in children with Autism Spectrum Disorder (ASD) leads to motor deficits, resulting in gait pattern abnormalities. Wearable inertial measurement unit (IMU) sensors have emerged as an acceptable alternative to high-end motion sensors for cost-effective gait assessments using automatic feature extraction techniques. In this study, we develop a capsule network model to train on a dataset acquired from a small group of children with ASD and healthy children using wearable IMU sensors outside the lab environment. The gait data are collected from participants performing various gait activities- walking overground, ascending, and descending stairs. The capsule network model is enhanced with transfer learning to differentiate between autistic and healthy children's gait patterns. The trained models are evaluated using the precision-recall (PR) curve across varying thresholds to analyze the overfitting problem. Comparative analysis with the state-of-the-art automated feature learning methods shows that the proposed model outperforms other methods with an average F1-measure of 89.09% and an average area value under the PR curve (PR-AUC) of 0.919. The result analysis also indicates that considering different gait activities for the dataset improves the performance and applicability of the proposed models.

8:15 Transformative Pedagogy in Object-Oriented Programming: the COOP Model and AI-Enhanced Case-Based Learning

Wen Zhan Chee, Chin Ann Ong and Owen Newton Fernando (Nanyang Technological University, Singapore)

Advancements in generative artificial intelligence (AI) and large language models (LLMs) offer new opportunities to enhance object-oriented programming (OOP) education. Building on these developments, this study implements a case-based learning pedagogy within an AI-driven framework. Specifically, the ‘Case-Based Learning for Classroom Management Problem Solving (CBL-CMPS)' model proposed by Choi and Lee is adapted to the OOP context, resulting in the development of the ‘Cooperative Object-Oriented Programming (COOP)' model. A chatbot based on the COOP pedagogy was developed and deployed for testing with computer science students. This chatbot incorporated modern features of generative AI, such as prompt engineering and multi-agentic workflow, to enhance the effectiveness of delivering the pedagogy. Findings indicate that the COOP model effectively enhances student motivation and understanding of OOP. Furthermore, a phase-wise evaluation of the COOP model suggests that, although generally effective, targeted refinements could further improve its impact. This study concludes by discussing the potential, challenges, and implications of integrating LLM-assisted case-based learning into programming education.

8:30 Cardiac Care IoT and ML: Portable Home-Based Cardiovascular Monitoring for Early Risk Assessment

Fariah Mahzabeen, Khondoker Ahmed Zubaier, Fatiha Tultul and Intesar Hassan Bhuiyan (North South University, Bangladesh); Riasat Khan (North South University, Bangladesh & New Mexico State University, USA)

Sudden cardiac arrest is a significant global health concern; however, triage of cardiac events typically occurs only in clinical settings, often when the patient is already experiencing a cardiac episode. Effective and efficient at-home cardiovascular virtual health monitoring is essential for early intervention, potentially preventing sudden fatalities. A highly accurate machine learning model combined with a Veroboard integrated cardiac care kit has been developed in this work, which addresses the high mortality rate of sudden cardiac arrest through home-based monitoring. This device integrates three critical parameters ECG, blood pressure, and heart rate-into a machine-learning model to analyze real-time cardiovascular health data. A combined dataset of 1,690 cardiac patients from the UC Irvine Machine Learning Repository is used for model training and testing, encompassing 11 health features, including critical cardiovascular indicators such as fasting blood sugar, ECG results, exercise-induced angina, and ST slope. While the dataset includes individuals across different age groups, it primarily focuses on individuals aged 40 to 60. Min-max scaling for continuous features and one-hot encoding for categorical features have been applied in the dataset preprocessing stage. A Stacking classifier is implemented, using Decision Tree, Random Forest, and Gradient Boost classifiers as base estimators, with KNN as the final meta estimator. The applied Stacking ensemble model achieves an accuracy of 95.6% and an F1 score of 95.9%. This proposed device ensures a user-friendly interface and high accuracy, making it suitable as a household monitoring tool to reduce fatalities from unanticipated cardiac events.

8:45 Enhancing Cognitive Engagement and Inclusion for the Blind and Visually Impaired Through Sensor-Based Board Game

Shahrinaz Ismail (Goolee Sdn Bhd, Malaysia & IEEE Consultants Network Affinity Group Malaysia, Malaysia); Hayatul Nabihah Khairul Anwar and Kamazulzzaman Ahmad (Goolee Sdn Bhd, Malaysia)

Humans take for granted of board games without realizing that they could nurture higher order thinking skills (HOTs). Not being able to see, or being blind and visually impaired, would unintentionally discriminate the opportunity to cultivate HOTs through board games. The question in mind is how to give the same opportunity of developing HOTs to the blind and visually impaired through a game only the sighted can play. With the mission to make the board game "visible" to the blind people, B-Goolee is developed. This study went through multiple cycles of designing, developing, integrating, and testing all elements necessary to make B-Goolee an assistive educational game technology. The context consists of the board with sensors, the 3D printed game pieces, the mobile device with installed AppsGoolee connected to the board with Bluetooth technology, and the manual book printed in Braille. With this ecosystem, the blind and visually impaired individuals could enjoy the game while challenging their cognitive skills, and the tests results have proven it. The actual performance metrics from the user evaluation results quantifies the major findings from this board game usage and usability to the users. This contributes to the assistive technology that leads to the educational outcomes for the blind and visually impaired individuals.

9:00 Smart Campus Evolution via IoT: Technical Constraints, Stakeholder Analysis, and Future Opportunities

Bishwajit Banik Pathik (Military Institute of Science and Technology, Bangladesh & American International University-Bangladesh, Bangladesh); Abir Tirtha Das and Md. Kothan Miad (American International University-Bangladesh, Bangladesh)

This article aims to address the technical challenges and potentials associated with implementing smart campuses in conventional university frameworks. Integration of cost effective IoT sensors, improved wireless communication, different machine learning algorithms for data analysis, sustainable energy infrastructure are valuable tools for facilitating smart campus architecture. To gain insight into the feasibility and future prospects of smart campus adoption, a comprehensive survey was conducted among various campus stakeholders including students, faculty, and administrative staff. A total of 138 responses were received from the survey which had eighteen questions inquiring various aspects of any smart campus. The survey responses not only highlighted diverse opinions and priorities on the implementation of advanced technologies in smart university campuses but also indicated future aspects of the same in terms of educational and administrative growth. This study provides valuable recommendations for universities seeking to transition toward smart campus architectures while addressing the technical challenges inherent in such initiatives.

9:15 Decentralized Cross-Border Financial Aid Distribution Utilizing Blockchain-Based Tokenization

Md. Raisul Hasan Shahrukh (University of Malaya, Malaysia); Nafees Mansoor (University of Liberal Arts Bangladesh, Bangladesh)

Cross-border financial aid distribution encounters substantial obstacles, such as inefficiencies, elevated transaction costs, complications in regulatory compliance, and transparency issues inherent in traditional banking systems. This study presents a decentralized system for financial aid distribution that utilizes blockchain tokenization and smart contract automation to tackle these difficulties. The proposed approach specifically incorporates Hyperledger Besu, a permissioned blockchain, to enable traceable, transparent and efficient international financial transactions. The system manages digital tokens within a blockchain-based system that utilizes its smart contract, facilitating rapid and verifiable aid transfers while ensuring compliance with international regulatory guidelines. Performance evaluations demonstrate improved transaction throughput up to 4.87% higher than Hyperledger Fabric alongside reduced latency and enhanced operational efficiency compared to existing blockchain solutions. Furthermore, the modular consensus mechanism in Hyperledger Besu ensures Byzantine fault tolerance and maintains sub-second transaction finality under simulated loads of over 1,000 transactions per second. Moreover, automating compliance using smart contracts minimizes human mistake and potential fraud, thus considerably improving the traceability and dependability of cross-border financial aid disbursements.

Wednesday, October 29 8:00 - 9:30

Track B4F8 CCI 4.3: Computing & Computational Intelligence (CCI) 4.3

Room: F8. 507 Monsopiad

Chair: Wan Sieng Yeo (Universiti Malaysia Sabah, Malaysia)

8:00 Parallel Lightweight Hybrid Attention BiGRU Framework for Multi Resident Human Activity Recognition with Sparse Sensor Data

Abisek Dahal (National Institute of Technology Meghalaya, India); Kaushik Ray (North Eastern Regional Institute of Science and Technology, India); Soumen Moulik (National Institute of Technology, Meghalaya, India)

Recognizing human activity in multi-resident smart homes is complex due to sparse sensor activations and overlapping occupant actions. This paper presents a Parallel Lightweight Hybrid Attention BiGRU framework, integrating Multi-Head Attention (MHA) with Bidirectional GRUs (BiGRUs) to address these challenges effectively. The model processes global sensor relationships and local temporal dependencies in parallel, overcoming information loss typical in sequential processing. Experiments on real world smart home ARAS datasets show that the framework achieves 97.99% accuracy in Multi resident settings in House A and 99.49% accuracy in multi resident scenarios in House B. It also generalizes well across different households, demonstrating strong adaptability and robustness. By combining attention mechanisms with recurrent neural networks, the proposed architecture efficiently captures key patterns in sparse and concurrent sensor data. This work marks a significant advancement in multi resident human activity recognition, offering a scalable and reliable solution for real world smart-home applications and establishing a strong foundation for future research in pervasive computing and ambient intelligence systems.

8:15 Action-Aligned Video Pairing for Video Augmentation

Randy C Wihandika (Kumamoto University, Japan & Brawijaya University, Indonesia); Israel Mendonca and Masayoshi Aritsugi (Kumamoto University, Japan)

Video augmentation is an effective strategy for improving the performance of action recognition models. A recent video augmentation strategy addresses scene bias by mixing human regions from one video with the background from another. However, this often produces artifacts due to limitations in the video mixing process, which degrade training quality. This study proposes a video augmentation strategy that produces compatible action-scene video pairs rather than choosing them randomly, to improve the quality of mixed videos. To achieve this, two compatibility metrics are introduced to guide this selection to significantly reduce the occurrence of visual artifacts and generate higher-quality augmented videos. Our method improves alignment between actions which leads to more effective augmentation. The performances are further enhanced by applying a temporal morphological operation to improve object detection consistency. Experimental results on the UCF101, HMDB51, and Kinetics-100 datasets show that our approach improves classification performance. Code is available at https://github.com/rendicahya/video-action-alignment.

8:30 A Context-Aware PDF Query Chatbot

Ravi Kishore Kodali (National Institute of Technology, Warangal, India); Sai Veerendra prasad Kuruguti (National Institute of Technology Warangal, India); Varsha Sanga (CloudAngles, India); Lakshmi Boppana (National Institute of Technology Warangal, India)

Modern Retrieval Augmented Generation often lacks an inherent understanding of document-specific relationships and structured knowledge. By combining large language models and graph-based retrieval, the PDF Query Chatbot presented in this research fills this gap and provides more precise and contextually aware responses. In order to explicitly record entity relationships and structural dependencies, the system uses Neo4j to create a knowledge graph after extracting textual content from the uploaded documents. To facilitate a semantic similarity search, the text is simultaneously shredded and embedded in a vector store. A hybrid retrieval system that combines vector-based search for contextual relevance and graph traversal for relational comprehension is activated when a user submits a query. To produce grounded, document-specific responses, the results of the two retrieval pipelines were combined and sent to the LLM. By synergizing graph databases, semantic search, and LLMs, this architecture provides a context-aware solution for intelligent document interaction, addressing key limitations in traditional LLM-based question resolution systems.

8:45 Causal LIME: Enhancing Local Explanations with Causal Perturbations for Military Sensor Data

Trupthi Rao (IIIT Allahabad, India); Navjot Singh (Indian Institute of Information Technology Allahabad, India); Sonali Agarwal (Indian Institute of Information Technology, Allahabad, India)

Interpretability is vital in safety-critical domains such as defense, where understanding model behavior is crucial for building trust, ensuring accountability, and supporting decision-making. Traditional local explanation techniques, such as Local Interpretable Model-Agnostic Explanations (LIME), often neglect the causal relationships among input features. This oversight can result in misleading or spurious interpretations, particularly in complex, high-stakes environments. To address this limitation, we propose Causal LIME, an extension of LIME that incorporates causal graphs to guide the generation of perturbations in a manner consistent with the underlying data-generating mechanisms. This ensures that explanations respect the causal structure of the domain, leading to more trustworthy insights. We evaluate Causal LIME along three dimensions: (i) comparative analysis with traditional LIME, (ii) validation against permutation-based feature importance, and (iii) application to real-world military sensor data for vehicle classification. Experimental results demonstrate that Causal LIME produces more stable, causally grounded explanations, reinforcing its value in mission-critical AI applications where interpretability, reliability, and trust are paramount.

9:00 Improving Dynamic Time Warping in Gesture Recognition for Autonomous Vehicles

Yu Wu, Pin-Yu Lin, Yu-Chiu Lin and Min-Te Sun (National Central University, Taiwan)

Gesture recognition is a key component in developing intuitive and efficient human-computer interaction systems, enabling machines to interpret human intentions through body movement analysis. A common approach employs the Dynamic Time Warping (DTW) algorithm to compute similarity between keypoint sequences extracted via pose estimation from both reference and live video streams. While effective and flexible, DTW has limitations, including high space complexity and sensitivity to keypoint misidentification often seen in pose estimation. To address these issues, we propose two enhancements to the DTW-based gesture recognition pipeline. First, we apply a space compression technique to reduce memory usage without sacrificing performance. Second, we introduce a frame-skip mechanism to mitigate the impact of incorrectly detected keypoints on recognition results. To evaluate our method, we construct a dataset of traffic gestures commonly used in Taiwan. Experimental results show that the proposed enhancements improve the efficiency, scalability, and accuracy of gesture recognition, making the approach more suitable for real-time use in constrained environments.

9:15 Efficient Task Scheduling Algorithms for Decentralized Large Language Model Serving

Sanjaya Kumar Panda (National Institute of Technology Warangal & NITW Techsammelan Private Limited, India); Sankalp Dubey (NIT Warangal, India); Siba Mishra (C. V. Raman Global University, Bhubaneswar, India)

Large language models (LLMs) have gained enormous popularity for processing and generating text. They are a subset of generative artificial intelligence (GenAI) that require higher availability of graphical processing unit (GPU) resources for inference services. However, making GPU resources available in a centralized infrastructure is quite challenging. Therefore, recent works have focused on decentralized physical infrastructure networks (DePIN) to utilize idle GPU resources, enabling scalable LLM inference services across the decentralized network. These inference services may experience inherent latency (i.e., measured in time per output token (TPOT)) due to communication overhead or time between GPU resources responsible for generating consecutive tokens. The task scheduling algorithm is crucial in decentralized LLM inference services to minimize TPOT and maximize GPU resource utilization, particularly when GPU resources are constrained by computational capacity. This paper introduces two task scheduling algorithms, the improved greedy heuristic shortest path algorithm (IGHSPA) and the dynamic programming-based task scheduling algorithm (DPTSA), for decentralized LLM serving to achieve these objectives. Each task involves assigning a layer to a GPU resource, which IGHSPA and DPTSA accomplish using greedy heuristic and dynamic programming. Both algorithms are extensively simulated and compared with one of the recent algorithms, namely the greedy heuristic shortest path algorithm (GHSPA), in terms of TPOT and execution time (ET). Our simulation results demonstrate that DPTSA improves TPOT up to 47.50% and 35.50% and ET up to 99.95% and 35.00%, compared to GHSPA and IGHSPA.

Wednesday, October 29 11:30 - 1:00

Track B5F1 CCI 5.1: Computing & Computational Intelligence (CCI) 5.1

Room: F1. Sipadan I

Chair: Jamal Ahmad Dargham (Universiti Malaysia Sabah, Malaysia)

11:30 Securing Face ID: Privacy Preservation for Non-Retentive Face Recognition System

Megan Chua, Chanelle Yue-Ting Yeow, Cassandra Xin-Yee Chwee, Shu-Min Leong and Raphael C.W. Phan (Monash University, Malaysia Campus, Malaysia)

Facial recognition technology is increasingly integrated into various applications. While face recognition systems have streamlined the authentication process and reduced the need for manual verification, the advancement in artificial intelligence (AI) generating realistic-looking media raises significant privacy concerns due to the potential misuse of biometric data. While current security protocols ensure data protection, storing biometric data in the system is a latent risk. In cases where the stored data is compromised, the users are susceptible to attacks such as face swapping, deepfake and identity theft. To address this, this paper presents a privacy-preserving algorithm that omits the need to store human raw biometric data in the system. This is achieved by utilizing a new combination of Locality-Sensitive Hashing (LSH), salting, and RSA encryption for face recognition. The proposed method ensures data security by securely hashing and encrypting facial features while maintaining high recognition accuracy. The proposed framework is evaluated on the Labeled Faces in the Wild (LFW) and achieves a comparable performance with the state-of-the-art techniques.

11:45 The AI Data Analyst: A Framework for Autonomous Data Analytics, Highlighting LLM and AI Agents

Vichayada Laosubinprasert, Proadpran Punyabukkana and Atiwong Suchato (Chulalongkorn University, Thailand)

This work presents an automated data analytics framework that integrates large language model (LLM) and artificial intelligence (AI) agents to perform end-to-end analysis based on Google's six-step methodology: Ask, Prepare, Process, Analyze, Share, and Act without human involvement throughout the analytics process. The system requires only user input containing the objective, data, context, and prior hypotheses. Then, the system autonomously generates prompts and executes tasks through specialized AI agents. AI agents perform the roles of planning tasks and directing the actions of agents, leveraging LLM for generation of ideas and reasoning to inform their plans and actions. Experiments on two datasets across domains, including education and business, show strong performance. Average analytics scores range from 8.6 to 9.4 out of 10, with average execution times varying between 1.9 and 6.6 min, and error rate varying between 0.2 and 1.2 occurrences per run. Additionally, the system can perform complex tasks such as coding, statistical analysis, machine learning modeling, and visualization generation. The results demonstrate the potential of LLM to function as a virtual data analyst, enabling fully automated, domain-independent analytics.

12:00 An Ensemble Clustering Approach to Recognize Flight State in Pilot Training

Anam Iqbal and Graham Wild (University of New South Wales, Canberra, Australia)

The aviation industry has been growing rapidly, and the success of the sector depends heavily on pilots receiving rigorous training, which guarantees the safety of air travel. During various flight states of an aircraft, pilots encounter different situations; to anticipate and manage potential hazards, pilots must possess detailed knowledge of these flight regimes and maintain appropriate control. This study employs an ensemble clustering approach on flight data simulated through Monte Carlo Simulations to determine and recognize crucial flight states of an aircraft, providing the basis for assessing pilot performance within each state. The clustering performance of various individual algorithms and their combinations is evaluated. Among single clustering models, Shared Nearest Neighbor (SNN) achieved the highest performance measures, while ensemble clustering models further enhanced clustering performance, with the combination of SNN and Fuzzy C-means performing particularly well. When comparing individual and ensemble approaches, it is observed that ensemble methods detect and distinguish flight states more accurately than any single clustering algorithm. This research marks a significant advancement in the use of ensemble approaches to improve the diagnosis of pilot performance during specific flight states, thereby enhancing training and flying safety.

12:15 Attack-SH: Adversarial Attacks on Self-Healing Material Properties Prediction Model

Min Xuan Tan (Monash University Malaysia Campus, Malaysia); Pei Sze Tan, Raphael C.W. Phan and Shu-Min Leong (Monash University, Malaysia Campus, Malaysia)

AI is now increasingly applied in diverse domains. The recent Nobel prizes for Physics and Chemistry awarded to computational scientists shows the significant impact that AI has on real-world scientific applications. Adversarial attacks pose a significant threat to the reliability of AI systems, particularly in high-stakes applications such as those in the materials sciences domain, which affect interactions with materials that exist in the real world. This paper examines the impact of two widely used adversarial attack approaches, notably the Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD), on a target AI model's performance for real-world materials. Experimental results demonstrate that both methods effectively degrade predictive accuracy, with PGD showing a more severe effect. Notably, high Structural Similarity Index (SSIM) scores across all perturbed samples suggest that the attacks introduce imperceptible changes, increasing their potential risk as such attacks are then undetectable. Further analysis using metrics such as Mean Squared Error (MSE), Adversarial MSE (A-MSE), and Relative Error Increase (REI) confirms substantial shifts in model output and reconstruction quality. These findings highlight critical vulnerabilities in current model architectures and emphasize the urgent need for more resilient defense strategies, such as adversarial training and input pre-processing.

12:30 Deepfake Detection Using ResNet50V2 with Machine Unlearning Integration

Md Serajun Nabi (Multimedia University, Malaysia); Dema Yuden (Albykhary International University, Malaysia); Mohammad Faizal Ahmad Fauzi (Multimedia University, Malaysia)

The advent of deepfake content has been an enormous challenge in digital media security that necessitates an effective detection system. This study proposes a deepfake detection model based on ResNet50V2 with an associated machine unlearning (MUL) approach to enable selective forgetting of generated data. The model is trained and evaluated on the FaceForensics-1600 dataset, which consists of real and deepfake video frames, and then undergoes a retraining phase, excluding the forgetting set. Performance before and after unlearning is quantified in terms of classification reports, confusion matrices, and ROC curves. Experimental results show that the model maintains a steady accuracy of 96% and ROC-AUC of 0.97 even after unlearning. The findings suggest that MUL can complement model adaptability in dynamic data environments. It also supports compliance with data privacy laws requiring data deletion. The results demonstrate that practical unlearning can be applied to deepfake detection systems without impacting performance, offering a promising solution for ethically adaptive and privacy-preserving AI.

12:45 Preserving Cluster Identity Across Time: an Incremental Cosine Similarity Approach

Adarsh Suresan Nair (Yum India Global Services Private Limited, India); Aishwariya K K (IBM India Private Limited, India)

In many real world applications, such as customer behavior analysis, data distributions evolve gradually over time.Traditional clustering methods, which retrain models from scratch at regular intervals, often fail to preserve the fine grained dynamics within clusters as they evolve. In this paper, we propose an incremental clustering framework that maintains cluster continuity across sequential time periods. Our approach clusters new incoming data independently and then maps the resulting clusters back to existing clusters by computing cosine similarity between cluster centroids. If a sufficiently similar match is found, the cluster is continued; otherwise, a new cluster is initialized.This method enables accurate tracking of cluster evolution overtime, capturing subtle shifts in cluster characteristics without abrupt reassignments. Experimental results on synthetic and real-world datasets demonstrate the effectiveness of our approach in preserving cluster lineage and detecting emerging behaviors.Our framework offers a simple yet powerful solution for time-sensitive applications where maintaining historical cluster context is critical.

Wednesday, October 29 11:30 - 1:00

Track B5F2 CCI 5.2: Computing & Computational Intelligence (CCI) 5.2

Room: F2. 501 Kadamaian

Chair: Nordin Ramli (MIMOS Berhad, Malaysia)

11:30 Gesture-Driven Cursor Control Interactive Projection System

Lance Eman M Hernandez, Jhon Lester Malabuyo, Carl Symon V Ofrin, Rocxel Roi C Puso and Reniel M Cornejo (FAITH Colleges, Philippines)

The researchers introduced an interactive projection platform that enables users to control on-screen content through natural hand gestures, enhancing the way people interact with digital media. This innovative system integrates gesture recognition technology, high-definition cameras, and projectors to detect user movements in real time, allowing seamless navigation of presentations and educational materials. By eliminating the need for traditional remote controls and whiteboards, it fosters a more engaging and dynamic experience in both academic and professional environments. The system's core advantage lies in its precise gesture recognition and smooth performance, powered by advanced machine learning algorithms based on Google MediaPipe and supported by high-quality hardware components. Its modular design allows for quick setup, instant response times, and handheld functionality, ensuring adaptability across various settings. Cost-effectiveness was a key consideration, with extensive testing and refinement addressing detection accuracy, system compatibility, and environmental challenges. Ultimately, this gesture-based interface aims to transform presentation methods by promoting immersive, intuitive, and highly interactive user experiences.

11:45 Blockchain Networks Metrics Collection for Validation in Blockchain Simulator

Tze Yee Choo (Heriot-Watt University Malaysia, Malaysia); Timothy Tzen Vun Yap (Heriot-Watt University, Malaysia); Ian K.T. Tan (Heriot-Watt University, Malaysia & Innov8tif Solutions Sdn Bhd, Malaysia); Zi Hau Chin (Heriot-Watt University Malaysia, Malaysia)

The accuracy and reliability of the blockchain networks simulation depend on the realistic network and simulator configuration. However, many existing blockchain simulators may use outdated parameters that fail to reflect the dynamic and constantly evolving nature of real-world blockchain systems. Hence, a simulator validation is crucial before starting the process of improving blockchain performance. In this paper, we collect the recent blockchain-related data from different sources and re-parametrize the blockchain parameters. A different network setting, namely a 7 regions simulation, was proposed to be used in the blockchain simulator to improve the reliability of the blockchain simulator. We then compare the block propagation time between the proposed settings with the original settings and the results of the DSN network, which reflects the Bitcoin network using the rate of change rule. The results show that the proposed settings are slightly closer to the Bitcoin network compared to the original settings in terms of block propagation time. The results are then validated using the standard deviation to verify consistency. We also suggest further collecting the data for a longer duration to achieve higher reliability on the simulator.

12:00 Transformer and CNN-Based Lightweight Ensemble for Retinal Disease Classification Using OCT Imaging

Aditya Bhongade (Yeshwantrao Chavan College of Engineering, India); Yogita Dubey (Yeshwantrao Chavan College of Engineering, Nagpur, India); Punit Fulzele (Directorate of Research & Innovation, SPDC Datta Meghe Institute of Higher Education & Research Wardha, India)

Automated retinal disease classification plays a crucial role in the early detection, monitoring, and treatment planning of ocular disorders, ultimately helping to prevent vision loss. This paper introduces a robust and effective ensemble model that combines the strengths of ResNet-50 and Vision Transformer (ViT-B16) to classify retinal images from the widely used OCT 2017 dataset. The ensemble leverages the local pattern recognition and fine-grained feature extraction capabilities of ResNet-50, along with the global contextual understanding and long-range dependency modeling of ViT-B16. By integrating both convolutional and transformer-based paradigms, the proposed method enhances classification accuracy and generalizability. The model achieves state-of-the-art (SOTA) performance with an accuracy of 0.9959, an AUC of 1.00, and F1-score, precision, and recall of 0.9959, significantly outperforming several existing approaches. These results demonstrate the effectiveness and reliability of the ensemble for real-world clinical deployment, offering a promising solution for automated retinal disease diagnosis and advancing modern medical image analysis.

12:15 Hybrid FEM-ANN Modeling of Rutting in Flexible Pavements Reinforced with Coir Geotextile at the Subbase-Subgrade Interface

Sheina Pallega and Dante L Silva (Mapúa University, Philippines); Jimmy G. Catanes (Commission on Higher Education, Philippines); Mark B. Ondac (Mapúa University, Philippines)

The increasing demand for resilient and sustainable road infrastructure has led to the integration of innovative materials and advanced computational techniques in pavement engineering. This study investigates the rutting performance of flexible pavements reinforced with coir geotextile, a biodegradable material derived from coconut husk, offering an eco-friendly alternative to synthetic reinforcements. A finite element model (FEM) was developed in ABAQUS to simulate rutting behavior under static and dynamic loading, incorporating varying material properties and layer thicknesses. Model validation using experimental data yielded a MAE of 0.184, confirming its predictive accuracy. A total of 200 datasets were extracted from the validated FEM and used to train an Artificial Neural Network (ANN) for rutting depth prediction. The ANN, structured as a feedforward backpropagation model with 20 input parameters, one hidden layer with 41 neurons, and one output, achieved high correlation coefficients across all phases: training (R = 0.99051), validation (R = 0.96830), and testing (R = 0.99036), with an overall R of 0.98874 and MSE of 0.19331. Sensitivity analysis using Garson's algorithm ranked input parameters based on their relative influence, identifying asphalt elasticity and thickness, and subgrade stiffness as the most critical factors affecting rutting performance. This research offers a robust, data-driven approach to pavement design by integrating FEM, ANN, and sensitivity analysis, providing accurate rutting depth predictions and valuable insights for material optimization. The findings support the development of cost-effective and environmentally sustainable pavements, aligned with global sustainability goals.

12:30 Real-Time Vision Inspection with AI-Based Edge Processor for Printed Circuit Board Assembly Quality Control

Jun Le Lai (Swinburne University of Technology Sarawak, Malaysia); Hudyjaya Siswoyo Jo (Swinburne University of Technology Sarawak Campus, Malaysia)

In modern electronic manufacturing, particularly in printed circuit board (PCB) assembly, ensuring product quality through automated inspection is essential. While traditional automated optical inspection (AOI) systems are effective, they often face limitations when dealing with complex assemblies and are typically designed for specific board types, limiting flexibility and scalability. This research presents the development of a universal, real-time inspection system for PCB assembly using artificial intelligence (AI) integrated with edge computing. The system is trained on a diverse dataset containing common PCB assembly defects, such as missing components, solder bridging, and insufficient solder. A lightweight AI model is developed to enable efficient inference on low-power edge processors. The proposed system is evaluated in real-world testing scenarios using different PCB assemblies to validate its adaptability and feasibility for deployment in practical manufacturing environments. Results demonstrate the potential of AI-based edge solutions in enhancing the flexibility and performance of automated quality control processes.

12:45 Classification of Natural Disaster Images Using Convolutional Neural Network Models

Mahmoud Yehia Emam Selim Rehan and Noramiza Hashim (Multimedia University, Malaysia); Khairil Anuar (Multimedia Universiti, Malaysia); Wan Noorshahida Mohd-Isa (Multimedia University, Malaysia)

This paper explores the use of Convolutional Neural Networks (CNNs) for disaster classification, focusing on the EfficientNet architecture to classify four major natural disasters: floods, earthquakes, cyclones, and wildfires. EfficientNet stands out due to its novel compound scaling approach, which significantly enhances feature extraction from diverse image data while maintaining high computational efficiency. The model was trained using transfer learning on a carefully balanced dataset sourced from multiple disaster imagery repositories. Its performance was evaluated through accuracy, precision, recall, F1-score, and confusion matrices, ensuring a rigorous and reliable assessment of classification effectiveness. Experimental results demonstrate that EfficientNet consistently outperforms six competing models-VGG16, ResNet50, MobileNet, ARCNet-MobileNet, RescueNet, and ARCNet-VGG16. Notably, EfficientNet achieved the highest accuracy of 94% while requiring the shortest training time of only 13.5 minutes for 10 epochs. These findings highlight EfficientNet's scalability, robustness, and reliability, making it an excellent candidate for deployment in real-world, image-based disaster management and early warning systems.

Wednesday, October 29 11:30 - 1:00

Track B5F3 PES 5: Power, Energy & Electrical Systems (PES) 5

Room: F3. 502 Mesilau

Chair: Nik Hakimi Nik Ali (Universiti Teknologi MARA & Shah Alam, Selangor, Malaysia)

11:30 Optimizing Microgrid Profits Through Automated P2P Energy Trading Using Blockchain and BESS

Sajarupan Tharumaraja (University of Sri Jeyawardenapura, Sri Lanka & University of Jaffna, Sri Lanka); P. L. M Prabhani and Akila Wijethunge (University of Sri Jayewardenepura, Sri Lanka); Janaka Ekanayake (University of Peradeniya, Sri Lanka)

The integration of Distributed Energy Resources (DERs), such as rooftop photovoltaic (PV) systems and Battery Energy Storage Systems (BESS), enables peer-to-peer (P2P) energy trading in microgrids, enhancing grid flexibility and optimizing operational management. This study presents an automated, blockchain-enabled framework for very short-term (VST) P2P trading, tested using Sri Lanka's tariff data to harness the economic and operational potential of decentralized energy systems. The Intelligent Prosumer Energy Node (IPEN) facilitates autonomous energy trading through real-time monitoring, VST demand forecasting, recommendations from the OpenDSS Demand-Side Management (O-DSM), and user-guided decisions. Similarly, the Intelligent Consumer Energy Node (ICEN) autonomously executes trading based on power demand monitoring, forecasting, and O-DSM guidance. The blockchain network, built on Hyperledger Fabric, secures and transparently manages transactions across five organizations, supported by multiple channels and smart contracts. Three trading models, Feed-in Tariff (FiT), P2P without storage, and P2P with BESS, were evaluated across prosumer-to-consumer ratios of 25:75, 50:50, and 75:25. Results show that automated P2P trading outperforms FiT, with BESS providing the highest economic gains. Prosumers achieved up to 35.1% higher profits, while consumers reduced costs by up to 15.2%, demonstrating the system's potential for scalable microgrid deployment.

11:45 Innovative Solutions for Smart Grids: Direct Ammonia Fuel Cells and Smoothing Filters for Solar and Wind Power Stabilization

Ahmed Intekhab Rohan and Tasfia Akter Ridita (Islamic University of Technology, Gazipur, Bangladesh); Hasanur Zaman Anonto (American International University-Bangladesh, Bangladesh); Md Ismail Hossain (American International University Bangladesh, Bangladesh); Anup Kumar Roy and Sudipto Roy (Lamar University, USA); Riadul Islam and Abu Shufian (American International University-Bangladesh, Bangladesh)

The paper proposes a combined approach of Solar and wind power fluctuation equalization through Electrochemical Ammonia Synthesis (EAS) and Direct Ammonia Fuel Cells (DAFCs), supplemented by different smoothing filters. When full of renewable energy, the surplus is turned into ammonia and stored and later turned back into electricity when there is a shortage. It uses Moving Average, Moving Median, Moving Regression, Gaussian and Savitzky Golay filters on real wind and solar profiles in MATLAB and uses EAS/DAFC models in Engineering Equation Solver (EES). Findings indicate that the MR filter provides the best compromise between noise attenuation and trend fidelity and peak capacity demands of ammonia production and fuel cell output are lowered by approximately 10-20% relative to raw profiles. SG and Gaussian filters have nearly similar advantages, whereas MA and MM are suboptimal because of either lag or inconsistent trend. This is the novelty of the work because a comparative evaluation of advanced smoothing methods in an ammonia-based storage cycle has never been carried out, and results are used to inform actionable recommendations regarding storage cycle sizing and cost minimization. The suggested methodology increases the stability of the grid, reduces the sizing of subsystems, and enables the cost-efficient implementation of ammonia fuel cells to integrate renewable energies.

12:00 Towards Sustainable Smart Cities: IoT and Big Data-Driven Anomaly Detection in Building Management

Irin Laila Parvin (Bangladesh University of Engineering and Technology, Bangladesh); Md. Arif Rahman (Ahsanullah University of Science and Technology, Bangladesh); Tahsina Farah Sanam (Bangladesh University of Engineering and Technology, Bangladesh)

For the development of sustainable smart cities, the combination of Internet of Things (IoT) and Big Data Analytics plays a vital role while IoT constitutes interconnected sensors to generate a data-rich environment, enabling real-time measurements and contextual information for smart buildings with significant benefits and challenges in security, data acquisition, processing, and storage. To identify anomalous devices in smart buildings, there are various existing statistical and classical Machine Learning models. But they are significantly inefficient to deal with non-linearity with average reported performance. Comprising the LSTM Autoencoder model and implementing advanced data analysis and machine learning techniques, this study represents recent research on anomaly detection with the unique CU-BEMS data set, which offers valuable insights for research applications. This technique includes various data-intensive approaches, along with lightweight methods tailored for edge and in-node computing. The paper highlights state-of-the-art methods for detecting anomalies in sensor systems by addressing challenges such as sensor miniaturization, energy efficiency, security, and data heterogeneity.

12:15 Occupancy Driven Optimization for Comfort and Energy Management in Smart Building

Ghulam Fizza (Universiti Kuala Lumpur, British Malaysian Institute, Malaysia); Kushsairy Kadir (Universiti Kuala Lumpur British Malaysian Institute, Malaysia); Haidawati Nasir (Universiti Kuala Lumpur, Malaysia); Mohammad Rashid (Universiti Teknologi Malaysia (UTM), Malaysia)

Achieving an optimal trade off between comfort and energy consumption in smart buildings remains a complex challenge, especially under dynamic occupancy conditions. Traditional optimization frameworks often neglect occupancy variation, resulting in energy inefficiencies or compromised occupant comfort. This study presents an occupancy aware optimization framework in which the objective weight between Comfort Index (CI) and Energy Gain (EG) remains fixed, while environmental constraints are dynamically adjusted based on occupancy. The approach is tested using three metaheuristic algorithms: Harris Hawks Optimization (HHO), Slime Mould Algorithm (SMA), and Arithmetic Optimization Algorithm (AOA), on a real world smart office dataset comprising 528 hourly observations. Results show that HHO achieved the highest average CI during occupancy (0.9341) with comparatively low energy consumption (1327 kWh), whereas SMA delivered the highest CI during non occupied periods (0.7195) with an average energy consumption of 1311 kWh. These findings validate the effectiveness of incorporating occupancy signals into smart building control, improving comfort satisfaction and energy efficiency without adjusting the objective weight.

12:30 Integrating Future Load Profile Nominations into Predictive Estimation of System Inertia in Distributed Power Networks: a Review

Ryan D Abella and Abdul Aziz G. Mabaning (Mindanao State University - Iligan Institute of Technology, Philippines)

Power system is undergoing a transition toward renewable energy sources (RES). While the high penetration of RES at the distribution level offers significant benefits, it also introduces challenges-particularly the reduction of rotational inertia, which poses serious risks to frequency stability and grid reliability. To ensure stable operation, it is essential to analyze and accurately estimate system inertia to maintain frequency stability. This paper reviews the emerging concept of an enhanced predictive estimation model that integrates Future Load Profile Nominations (FLPN). FLPN provides utilities with improved foresight into upcoming demand changes by enabling early identification of periods and locations that may experience high inertia stress. This review consolidates current research on system inertia and explores how FLPN can improve existing demand estimation practices to support better coordination in distributed energy networks. Aligned with the United Nations's Sustainable Development Goals (SDG) 7 (Affordable and Clean Energy) and 13 (Climate Action), the findings suggest that FLPN holds potential as a foundation for future developments in control strategies, virtual inertia planning, and the broader goal of establishing resilient, low-inertia power systems.

12:45 Collaborative Battery Scheduling of Microgrids: N -Agent Reinforcement Learning Approach

Alpha M M (APJ Abdul Kalam Technological University, India & College of Engineering Trivandrum, India); Hari Kumar R and Lal Priya P S (College of Engineering Trivandrum, India)

This paper proposes a collaborative battery scheduling framework for interconnected microgrids using an (n)-agent reinforcement learning (RL) approach, with the primary objective of minimizing the net cost to the main grid. Each microgrid is modeled as an autonomous agent equipped with local photovoltaic (PV) generation, load demand, and a battery energy storage system (BESS) . The agents learn optimal charge-discharge strategies using multi-agent Q-learning while coordinating energy sharing with neighboring microgrids when beneficial. In order to analyze how well the proposed approach performs, simulation studies are conducted for two scenarios: independent microgrid operation and interconnected cooperative scheduling. A custom Markov Decision Process (MDP) environment is designed to incorporate battery dynamics, power balance and shared actions. The proposed methods are trained and tested using real world load and solar profiles. The results demonstrate that interconnection and collaboration among agents lead to significant reductions in grid dependency and overall cost, compared to isolated operation. The framework ensures efficient battery utilization and promotes distributed energy cooperation in smart grid environments.

Wednesday, October 29 11:30 - 1:00

Track B5F4 ECD 5: Electronics, Circuits & Devices (ECD) 5

Room: F4. 503 Dinawan

Chair: Zuhaina Zakaria (Universiti Teknologi MARA, Malaysia)

11:30 Transparent Wideband Fractal Antenna for Modern Communication Systems Using Screen-Printed Silver Nanoparticles

Khaloud Mohammed Nasser AlJahwari (Chung-Ang University, Korea (South)); Abdullah Abdullah (University of Oulu, Finland); Hamza Ahmad (Universiti Teknologi Malaysia, Malaysia); Fauziahanim Che Seman and Ayesha Ayub (Universiti Tun Hussein Onn Malaysia, Malaysia); Nida Nasir (NED University of Engineering and Technology, Pakistan)

This work presents a transparent, wideband fractal antenna which can be fabricated using silver nanoparticles (AgNPs) and screen-printing technology. The antenna features an octagonal-shaped mesh monopole patch and a mesh ground plane, both printed on a transparent polyethylene terephthalate (PET) substrate. The proposed antenna has a compact size of 10 × 11 × 0.55 mm³. The antenna operates over a wide frequency range, from 7.6 to 20.5 GHz, covering both the X-band and Ku-band, and partially overlapping K-band with a total bandwidth of 12.9 GHz. The proposed antenna achieves a peak simulated realized gain of 1.97 dBi, a radiation efficiency of 93%, and a fractional bandwidth of 91.8 %. The use of flexible and optically transparent PET substrate enables deployment on curved or see-through surfaces. With the combination of compact size, wideband performance, cost-effective fabrication, and optical transparency, the antenna shows strong potential for use in radar systems, Satellite communications, and some military and aerospace applications.

11:45 ECG P-QRS-T Wave Peak Based Interval Variability for Interpretable Coronary Artery Disease Screening

Dhaladhuli Jahnavi and Ashutosh Dash (Indian Institute of Technology Kharagpur, India); Kayapanda Mandana (Fortis Healthcare Limited, India); Sundeep Khandelwal and Aniruddha Sinha (Tata Consultancy Services, India); Nirmalya Ghosh (Indian Institute of Technology Kharagpur, India); Amit Patra (Indian Institute of Technology, Kharagpur, India)

There is growing interest in developing non-invasive, widely accessible screening approaches for coronary artery disease (CAD) that are compatible with wearable technology. Prior studies have examined the association of CAD with abnormalities in the QRS-T and PR segments of the electrocardiogram (ECG), which require accurate identification of the onsets and offsets of P-QRS-T waves. This study proposes an alternative approach that extracts variability features from interval time series defined solely by ECG P-QRS-T wave peaks, thereby overcoming the persistent challenge of precise onset and offset detection. The proposed supervised hybrid feature selection approach identified an optimal feature combination that achieved 92% CAD/Non-CAD classification accuracy on the validation dataset. It surpassed popular feature selection algorithms, including LASSO, mRMR, BBA, BCS, and BGW. On a blind test dataset, the selected features enabled the final trained ensemble classifier to achieve 88% accuracy, correctly identifying 86% of subjects with CAD and 92% of subjects without CAD-outperforming state-of-the-art methods evaluated on the same dataset. Furthermore, validation on a manually annotated QT database revealed notable correlations between proposed features extracted from peak based and clinically relevant interval time series, supporting their potential interchangeability when automated onset-offset detection is unreliable.

12:00 Design of a Capacitive-Based PFM DC-DC Converter with Adaptive Stage Control for WSN Applications in 65nm CMOS Technology

Gene Fe P Palencia (Mindanao State University - Iligan Institute of Technology, Philippines); Abdulbasit Gamoranao and Nieva Mapula (MSU-Iligan Institute of Technology, Philippines)

This paper presents the design and simulation of a capacitive-based Pulse Frequency Modulation (PFM) DC-DC converter with adaptive stage control, implemented in 65nm CMOS technology. Targeted for low-power energy harvesting in Wireless Sensor Networks (WSNs), the proposed architecture mitigates limitations of conventional charge pump (CP) designs, including threshold voltage losses, reversion loss, and inefficient power conversion efficiency (PCE) under varying loads. The converter integrates a high-efficiency Modified Cross-Coupled Charge Pump (MCCCP) and an adaptive stage controller using a hysteresis comparator to dynamically adjust the voltage conversion ratio (VCR) based on input voltage. Post-layout simulation results show peak PCEs of 88.23% in Doubler mode and 81.47% in Tripler mode, with consistent voltage regulation across a 0.45 V to 0.7 V input range. Compared to existing solutions, the design achieves better output power, efficiency, and adaptive performance. These attributes make the converter highly suitable for ambient energy harvesting applications in low-power Internet of Things (IoT) nodes.

12:15 UVB Generation Using TIR-Based ORQPM Technique Stimulated by Faraday Rotation

Moumita Saha and Boilla Srinu (VIT-AP University, India)

The analysis numerically demonstrates the generation of 320 nm, a continuous-wave, ultra violate B (UVB), second harmonic using a thin film coated magneto optic (MO) crystal. The considered propagation manner is total internal reflection (TIR). The adopted phase-matching technique is optical rotation quasi phase matching (ORQPM), which significantly enhances second harmonic generation (SHG) efficiency. The required polarization rotation has been made possible by applying an adequate magnetic field. The applied magnetic field triggers the Faraday rotation inside the MO crystal. The thin film, has been utilized to modulate the phase-shifts due to the p- and s-polarized light as they propagate through the interface between slab and film at the time of TIR. A peak conversion efficiency of 22 % has been attained by a computer aided simulation. In the UVB spectrum, it signifies a high performance. The analysis accounts for surface roughness, absorption losses, and nonlinear law of reflection, resulting in near realistic simulation results. The proposed technique provides a controllable way for producing UVB radiation, with potential uses in ultrafast spectroscopy and dermatological phototherapy.

12:30 Comparative Analysis of Marker Based and Marker Less Motion Capture Systems for Shoulder Joint Angle Prediction

Deepa Sri R (Anna University, India & Sri Sivasubramaniya Nadar College of Engineering, India); Pravin Kumar (SSN College of Engineering, India); Kavitha A (Professor & HoD, India); S Saranya (Sri Sivasubramaniya Nadar College of Engineering, India)

Marker-based motion capture system (MCS) is the gold standard for analyzing human motion. But in the real world its usage in large-scale applications is limited by its inherent inaccuracy and practical difficulties. These real-world difficulties can be addressed with a marker-less motion capture system. However, its accuracy in quantifying joint Range of Motion (ROM) has not been verified across shoulder movements. In this study, we simultaneously captured marker less and marker-based motion data on six healthy participants performing shoulder abduction and adduction movements. Based on the results we calculated the RMSE value for abduction 0.80° and for adduction 0.683° between both the systems during each movement. The findings imply that the accuracy of the markerless Inertial Measurement Unit (IMU) system in ROM measurement is comparable to that of the marker-based system. However, particularly when it comes to shoulder ROM measurements, the direction and positioning of the reflective markers of the OptiTrack system have a significant impact on measurement accuracy.

12:45 Dual Band Wearable Antennas Designed on Day to Day Used Jeans for IEEE 802.11 Network

Subhrashil Nanda (India); Rajendra Prosad Ghosh (Vidyasagar University, India)

In recent days, due to a large-scale increase in healthcare activities, the use of various health monitoring devices and the transfer of data from these devices through Body Area Network (BAN) are rapidly increasing. The antenna used in BAN is a wearable antenna, where wearable fabrics are used to design the antenna. Research reported so far has used low-loss engineered wearable fabric to design antennas. In our work, attempts are made to design antennas on day-to-day used materials. The dielectric constant and loss tangent of a used jeans' fabric have been characterised by using an RF impedance analyzer and a dielectric probe kit that uses Open Open-Ended Coaxial Probe (OECP) technique. The dielectric loss is very high (loss tangent equals 0.1152) with a dielectric constant of 1.7976. Two dual-band antennas operating in the IEEE 802.11 designated Wi-Fi bands are reported here. The antenna height is optimized to get higher radiation efficiency. The maximum radiation efficiency achieved (Antenna-2) is 21% in both bands. The antennas are simulated in CST Microwave Studio, and the antenna with maximum radiation efficiency is fabricated for experimental verification.

1:00 Design and Optimization of Complex Quantum Circuits Targeting near-Term Quantum Processors Using Custom Algorithms and Qiskit Transpiler

Kazi Redwan, Mustakim Ahmed, Md. Faruk Abdullah Al Sohan and Sajedul Islam (American International University-Bangladesh, Bangladesh); Birbal Tamang and Rasmila Lama (Lamar University, USA); Ruja Shrestha (Islington College, Nepal); Abu Shufian (American International University-Bangladesh, Bangladesh)

Quantum computing faces challenges such as noise, short coherence time, and limited qubit connections. These challenges worsen as quantum circuits become more complex. One major issue is the increasing depth of quantum circuits. This research proposes an optimization framework targeting depth and gate count reduction in quantum circuits, specifically for Noisy Intermediate-Scale Quantum (NISQ) devices. The proposed approach combines unitary merging of single-qubit gates, CNOT cancellation, gate commuting, and rotation gate rewriting strategies. Consecutive gates acting on the same qubit, such as R x(θ 1) · R x(θ 2), are algebraically merged into a single rotation, while pairs of redundant CNOT gates are eliminated based on gate identity relations. The technique is implemented using Qiskit and validated across five diverse circuits including complex, random, and multi-qubit configurations. Experimental results show an average depth reduction of 33.33% and gate count reduction of 32.14%, with runtime improvement of up to 25%. For instance, an input circuit with a depth of 7 and gate count of 11 was reduced to a depth of 2 and 4 gates. All optimized circuits preserve functional correctness with a fidelity F ≥ 0.99. High-resolution circuit diagrams are presented to visually demonstrate improvements before and after optimization. Additionally, global phase shifts such as e iπ/4 are preserved or analytically characterized where relevant. This work enhances the viability of quantum computations on near-term hardware and opens pathways for future AI-driven quantum optimizations.

Wednesday, October 29 11:30 - 1:00

Track B5F5 CS 5: Communication Systems (CS) 5

Room: F5. 504 Madai

Chair: Aroland Kiring (Universiti Malaysia Sabah, Malaysia)

11:30 12 Gbaud Visible Light Coherent Communication Based on RRC Pulse Shaped BPSK Modulation and Simplified Coherent Detection Scheme

Zhilan Lu, Fujie Li, Jifan Cai, Fang Dong and Zengyi Xu (Fudan University, China); Chao Shen (Fudan University, USA); Junwen Zhang and Nan Chi (Fudan University, China)

The advancement of current data-intensive services imposes increasingly demands on next-generation mobile communication systems. Visible light communication (VLC) offers significant potential for the development of next-generation communication networks. It has an abundant spectrum resource of up to 400 THz, exhibits immunity to electromagnetic interference, and its blue-green band coincides with the underwater transmission window, enabling high-speed communication with high signal-to-noise ratio (SNR). In this paper, we demonstrated a 532 nm visible light coherent communication (VLCC) system based on a lithium niobate phase modulator, and root raised-cosine (RRC) pulse shaped binary phase-shift keying (BPSK) and coherent detection scheme. Compared to intensity modulation with direct detection schemes, our system mitigates signal distortion induced by frequency chirp and relaxes the demand for high sensitivity at the receiver photodetector. Finally, we successfully achieved 12 Gbaud BPSK signal transmission. To the best of our knowledge, this represents the highest reported data rate for blue-green band visible light coherent communication to date.

11:45 Performance of Active and Passive RIS with Co-Channel Interferers in Cellular System

Nur Adriana binti Mawan Iryawan (Multimedia University, Malaysia); Azwan Mahmud (Multimedia University & Telekom Malaysia, Malaysia); Azlan Abdul Aziz (Multimedia University, Melaka, Malaysia); Syamsuri Yaakob (Universiti Putra Malaysia, Malaysia); Nor Azhar Mohd Arif (ELMU, Malaysia)

This paper presents performance analysis and comparison of active and passive Reconfigurable Intelligent Surface (RIS)-aided wireless systems in cellular system with co-channel interferers under realistic channel conditions. A simple and efficient analytical framework based on the Moment Generating Function (MGF) approach is developed to evaluate the downlink ergodic capacity and energy efficiency, considering Nakagami-m fading, path loss, and co-channel interference (CCI) from neighbouring base stations. In the passive RIS configuration, the reflected signals are phase-shifted without amplification, limiting the received signal strength under severe path loss. In contrast, the active RIS employs signal amplification at each element, enhancing the received power but introducing additional amplifier noise and increased circuit power consumption. Closed-form expressions for both ergodic capacity and energy efficiency are derived and validated against Monte Carlo simulations, showing a close match with theoretical predictions. The results reveal that the active RIS-aided system consistently outperforms the passive counterpart in terms of capacity and energy efficiency across various scenarios, such as the number of RIS elements, path loss exponent, amplifier gain, and interference probability, despite its higher power consumption.

12:00 BLSQ: AI-Enhanced Performance Framework for Wireless Multihop Networks

Zhihan Cui (Japan Advanced Institute of Science and Technology, Japan); Yuto Lim (Japan Advanced Institute of Science and Technology (JAIST), Japan); Yasuo Tan (Japan Advanced Institute of Science and Technology & National Institute of Information and Communications Technology, Japan)

Multi-server wireless multihop networks (MWMNs) are critical for modern communication systems, enabling efficient data transmission between devices and servers. However, the complexity of determining optimal server selection and multihop path planning in such networks often results in high interference, high network latency, low network capacity, and reduced network performance. To address these challenges, this paper proposes a two-stage network optimization scheme for MWMNs, using Broad Learning System and Q-learning, called BLSQ. First, the Broad Learning System (BLS) is employed to allocate servers to devices based on their location and computational requirements. Second, a Q-learning algorithm is introduced to optimize multihop path selection, aiming to maximize network capacity while minimizing interference. The proposed approach is evaluated based on different path selection methods in extensive simulations. Results demonstrate that our method significantly reduces network interference, increases network capacity, and achieves lower transmission time, providing a possible approach for optimizing wireless in MWMNs.

12:15 Reconfigurable Intelligent Surface-Aided Spatial Modulation with Signature Constellation

Fanyu Zeng (Macao); Yuyang Peng, Qi Jin, Ming Yue and Runlong Ye (Macau University of Science and Technology, Macao); Liping Xiong (Dongguan Polytechnic, China)

In recent years, a number of new technologies have emerged in the sixth generation (6G) wireless communication area. Reconfigurable intelligent surface (RIS) technologies have been widely studied because of its strong flexibility for signal adjustment. RIS is composed of multiple reflective units which are capable of adjusting their radiation characteristics. By dynamically adjusting the phases of the units, RIS can precisely reflect signals to the receiver and improve the quality of received signals over current transmission systems Combined with transmit spatial modulation (TSM), the RIS-aided TSM (RIS-TSM) system can significantly boost spectral efficiency (SE) while enhancing the received signal quality. In this paper, in order to overcome the effect of correlation among transmit antennas in RIS-TSM systems, we propose an RIS-aided transmit signature constellation based spatial modulation (RIS-TSSM) scheme with a complete system model and expressions. Simulation results depict that RIS-TSSM system can achieve better performance than the RIS-TSM system in the presence of antenna correlation.

12:30 Uniformity Tests on Image Steganography Based on Syndrome-Trellis Codes Without Stego-Keys

Hoover H. F. Yin (The Chinese University of Hong Kong, Hong Kong)

Images are a common type of digital media in computer networks, appearing in web pages, instant messaging, cloud storage, etc. Minor distortion of an image is probably not detectable, so it is possible to hide secret messages inside images sent through the network, thus becoming a potential vulnerability. In image steganography, most steganalysis tools focus on classifying whether each input image is a stego image or not. The algorithm for extracting/decoding secret messages is not used by these tools, although we can assume the knowledge of this information under Kerckhoffs's principle. To adhere to Kerckhoffs's principle, one-time stego-key can be used, but key exchange is challenging in scenarios that apply steganography. We consider steganography based on syndrome-trellis codes (STC) without stego-keys, where STC is a powerful embedding scheme that can minimize the embedding distortion and handle wet pixels without extra effort. As secret messages are usually considered as uniformly random strings, we investigate whether the decoded strings from normal images are also uniform, i.e., statistical detectability. By applying the NIST SP 800-22 Rev. 1a statistical test suite, we show that these decoded strings are instead highly non-uniform, unless the pixels are randomly shuffled. We also demonstrate that the non-uniformity may be applied for pooled steganalysis in theory, even when the extraction includes random shuffling. This suggests that minimizing distortion is not the only metric for measuring security.

12:45 From Noise to Clarity: Emerging Trends in Speech Enhancement for Real-Time Communication

Preethi Sunke and Senthil Mani (Google, India)

Real-time communication demands speech that is both intelligible and natural, even in noisy environments. This paper traces the progression of speech enhancement from traditional DSP techniques-such as spectral subtraction and Wiener filtering-to modern deep learning and diffusion-based models. While classical methods offer low latency and interpretability, they falter in complex, non-stationary noise. Deep neural networks, including CNNs, RNNs, and transformers, brought adaptive, data-driven noise suppression with superior performance. Most recently, diffusion models have redefined the state-of-the-art, enabling high-fidelity speech reconstruction from heavily corrupted inputs. We present a comparative analysis of these approaches in terms of effectiveness, latency, and deployment feasibility, and highlight the promise of hybrid models that unify DSP precision with generative AI power.

Most recently, diffusion models have redefined the state-of-the-art, enabling high-fidelity speech reconstruction from heavily corrupted inputs. We present a comparative analysis of these approaches in terms of effectiveness, latency, and deployment feasibility, and highlight the promise of hybrid models that unify DSP precision with generative AI power.

Wednesday, October 29 11:30 - 1:00

Track B5F6 CCI 5.3: Computing & Computational Intelligence (CCI) 5.3

Room: F6. 505 Sepilok

Chair: Raja Jamilah Raja Yusof (Universiti Malaya, Malaysia)

11:30 Mobile Application for Enhance Sustainable Tea Farming in Sri Lanka

Shashika Lakmini Lokuliyana (Sri Lanka Institute of Information Technology, Sri Lanka); Pipuni Wijesiri (University of Moratuwa, Sri Lanka & Sri Lanka Institute of Information Technology, Sri Lanka); Sahan Chamuditha Kulathunga, Navodi Perera and Moksha Koongahage (Sri Lanka Institute of Information Technology, Sri Lanka)

Ensuring sustainable tea farming requires intensive monitoring of plant conditions, nutritional status, and disease infections. To that end, this research presents a smartphone application that is powered by machine learning to assist Sri Lankan tea farmers in identifying fertilizer and chemical deficiencies, predicting tea yield quality, and detecting diseases at early stages. The system makes use of a trained machine-learning model to scan images of leaves for relevant characteristics to provide instant feedback through a user-friendly smartphone interface. The app offers advice to farmers to improve yield and reduce crop loss. This approach enhances accuracy in farming, minimizes reliance on over-fertilization, and assists in efficient farming methods. The given system is designed to target small scale and far-away farmers to make it more popular in diversified agricultural lands. The research involves mass-scale agricultural image dataset collection and processing, deep learning model training, and deployment of a robust mobile application for field implementation. Outputs strive to contribute to Sri Lankan smart agriculture by allowing farmers to make data-driven decisions to ultimately improve productivity and sustainability.

11:45 A Machine Learning Framework for Data-Scarce Regression Using SMOGN with Joint Hyperparameter Optimization: A Case Study with Cricket Performance Prediction

Harthik Manichandra Vanumu (Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, India); Paranjay Lokesh Chaudhary (Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, India.); Usha Moorthy (Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, India); Syed Anwar Ali (Manipal Academy of Higher Education, Bengaluru, India)

This study presents a machine learning framework to improve predictive accuracy in regression under data scarcity, a prevalent challenge in predictive modeling. A key contribution is a joint hyperparameter optimization strategy that integrates data augmentation with model training, outperforming traditional sequential approaches. Our approach simultaneously tunes SMOGN (Synthetic Minority Over-Sampling Technique for Regression with Gaussian Noise) and multiple regression models using Optuna, identifying optimal parameter combinations that sequential methods may miss. The framework was evaluated using k-fold cross-validation and multi-seed experiments on season-long batting performance prediction in the Women's Premier League (WPL), an emerging cricket league with limited historical data. The results show that tree-based ensembles consistently outperform linear models, with top performers achieving mean test R 2 values above 0.89. CatBoost achieved the highest mean test R 2 of 0.9075, with a standard deviation of 0.0162. An ablation study without SMOGN confirms the importance of this integrated augmentation strategy. By treating the pipeline as a fully integrated system, the framework provides a practical approach to predictive modeling under severe data constraints, with applicability across domains such as sports analytics, finance, and healthcare, and can serve as a blueprint for enhancing data synthesis within AutoML pipelines.

12:00 Enhanced Phishing Payload Detection Using Fine-Tuned DistilBERT and XAI-Based NLP Models

Sourav Datto, Delower Hossen Tuhin, Mustakim Ahmed, Kazi Redwan, Md. Faruk Abdullah Al Sohan and Abu Shufian (American International University-Bangladesh, Bangladesh); Birbal Tamang and Rasmila Lama (Lamar University, USA); Ruja Shrestha (Islington College, Nepal)

Phishing attacks are a major cybersecurity concern. These attacks continue to grow in complexity and often bypass traditional detection systems by imitating legitimate communication payloads. Many existing models, especially classical machine learning techniques, lack the ability to detect hidden or adversarial phishing payloads. They also offer limited transparency in their predictions. This research presents a phishing payload detection approach using a fine-tuned DistilBERT model. The methodology includes dataset preprocessing, model fine-tuning, adversarial training, explainability analysis, and performance evaluation. DistilBERT, a lightweight transformer model, is fine-tuned to detect phishing payloads with improved accuracy and robustness. Adversarial training is applied to defend against input manipulation. Explainable AI (XAI) techniques such as LIME and SHAP are used to interpret the model's predictions. This research shows that DistilBERT achieves a classification accuracy of 98.52% and an AUC score of 0.9993, outperforming traditional machine learning models. It also maintains low false positives and high recall. This research improves the reliability of phishing detection and provides interpretable outputs for security analysis. The results demonstrate that the proposed framework strengthens phishing detection strategies and increases resilience to adversarial attacks. The results are based on a single publicly available phishing email dataset and further validation across diverse datasets and real-world environments is required, with the scope of the findings limited to email-based phishing detection.

12:15 Efficient SpMV for GPUs Using Variable-Sized Vectors and CSR Restructuring

Vishal Manjibhai Pateliya (IIIT Allahabad, India); Anshu S Anand (Indian Institute of Information Technology Allahabad, India)

Sparse matrix-vector multiplication (SpMV) is crucial in many engineering applications, including machine learning, data analytics, and numerical simulation. As matrix sizes expand exponentially, efficient parallel algorithms become increasingly crucial. This work reviews some of the well-known parallel computing strategies, exploring their effectiveness in enhancing algorithmic performance. We focus on techniques for load balancing and data distribution in parallel SpMV, aiming to maximize computational resource utilization. We propose two algorithms for SpMV that attempt to overcome the shortcomings of Flat-SpMV, namely the use of fixed-size vectors and the performance degradation due to irregular matrices. In the first work, we use variable-sized vectors to reduce the partial results, as opposed to fixed-sized vectors used in Flat-SpMV. Further, as the irregular nature of the matrix caused Flat-SpMV to perform slower in warp-level analysis, we developed a new method by reorganizing the sparse matrix structure and applying a variant of Flat-SpMV to it. It outperformed other approaches on more than 70% of the input matrices.

12:30 A Multilingual Intelligent Document Processing System

Ravi Kishore Kodali (National Institute of Technology, Warangal, India); Varsha Sanga (CloudAngles, India); Sai Veerendra prasad Kuruguti (National Institute of Technology Warangal, India); Lakshmi Boppana (National Institute of Technology, Warangal, India)

In today's digital world, processing multilingual documents is critical for business, legal tasks, and information retrieval. This study describes a Multilingual Document Processing System that uses Optical Character Recognition (OCR) and Retrieval-Augmented Generation (RAG) to extract, query and summarize text in multiple languages. The system employs advanced OCR models to correctly recognize text from scanned documents, images, and handwriting in various scripts. By incorporating RAG, it improves comprehension and response generation, allowing users to retrieve and summarize information in English even when the original language is different. This approach takes advantage of recent advances in natural language processing, large language models (LLM), and multimodal AI to address challenges in multilingual data accessibility, knowledge synthesis, and real-time communication. The system provides a scalable AI-driven solution to improve document processing, eliminate language barriers, and increase global user engagement. AWS services support scalable document processing but cold starts in AWS Lambda hinder real time tasks.

12:45 AI-Based Visual Inspection System for Oxygen Indicator Detection in Food Packaging

Ari Aharari (Sojo University, Japan); Kosei Oghushi (SOJO University, Japan)

Oxygen indicators are widely used in packaged processed foods to visually signal oxygen levels by changing color-typically from pink (safe) to blue (defective). However, relying on human visual inspection during final quality checks often results in oversight, leading to the unintentional shipment of faulty products and subsequent customer complaints. This research proposes an AI-based defect detection system that automates the inspection process using the YOLOv8 object detection algorithm. The system consists of a real-time visual recognition module integrated with a laptop, USB camera, photoelectric sensor, and shielding unit, enabling precise detection of color changes in oxygen indicators after product packaging. The proposed system successfully classifies oxygen indicator and on-site evaluations demonstrated a significant reduction in undetected defects and improved inspection efficiency. While challenges such as misaligned packaging and occluded indicators remain, the results highlight the potential of AI-powered visual inspection systems to enhance quality assurance, reduce labor dependency, and support smarter manufacturing practices in the food industry.

Wednesday, October 29 11:30 - 1:00

Track B5F7 ETS 5: Engineering Technologies & Society (ETS) 5

Room: F7. 506 Selingan

Chair: Melvin Gan (Universiti Malaysia Sabah (UMS), Malaysia)

11:30 Feature Characterization to Aid in Patellofemoral Pain Syndrome Diagnosis

Gabriel Rodnei M Geslani, Edison Roxas, Emmanuel Guevara, Seigfred V. Prado, Paul Desmond C. Ong, Bernard B Graycochea, Nhaya Marella D Antonio, Julian T Lucina, Sean Clarenz C Joson, Warren Denzel F. Cheng, Consuelo B. Gonzalez-Suarez, Jan Tyrone Cabrera, Timothy Nazareno, Emily Rose D Nacpil, Ivan Neil Gomez and Jazzmine Gale S. Flores (University of Santo Tomas, Philippines)

Patellofemoral Pain Syndrome (PFPS) is a condition that causes pain at the front of the knee, particularly affecting active individuals such as athletes and military personnel. Accurate diagnosis remains challenging, as Magnetic resonance imaging (MRI) methods are often costly. Various applications, including preprocessing and segmentation techniques, have assisted clinicians by improving the quality of patellar tendon imaging. However, clinical observations and measurement of PT-TG distance were affected by factors like practitioner probe angles and patient skin thickness. Therefore, this study aimed to aid clinicians by analyzing key features from ultrasound (US) imaging, focusing on textural and morphological characteristics alongside biological markers. Datasets were collected by the Research Center for Health Sciences at the University of Santo Tomas and included twenty-seven (27) participants, fourteen (14) with PFPS and thirteen (13) without PFPS. Fifty-one (51) features were extracted and analyzed through Feature selection techniques, including Statistical Analysis with Principal Component Analysis (PCA), Recursive Feature Elimination (RFE), and Mutual Information in selecting the most optimal features. These features were validated using sensitivity, specificity, recall, and accuracy. Twenty-three (23) features were selected using statistical analysis with PCA, composed of fifteen (15) textural features, four (4) morphological features, and four (4) biological features were selected. The final model achieved an F1 score of 89% for classifying non-PFPS and 81% for classifying people with PFPS, with overall accuracy of 86%. By analyzing these selected features, the study aims to enhance the evaluation of ultrasound images and biological markers for PFPS detection, contributing to better patient care.

11:45 A Machine Vision-Based FSL Tutor with Static and Dynamic Gesture Recognition and Real-Time User Feedback Using MediaPipe Frameworks

Pocholo James Loresco (De La Salle University, Manila & Far Eastern University Institute of Technology, Philippines)

Filipino Sign Language (FSL) is an invaluable tool for communication within the deaf and mute communities, yet there is a shortage of proficient special education teachers and accessible learning materials. Current research on FSL recognition is limited to basic detection, often invasive, and lacks comprehensive systems that provide feedback to users. Additionally, FSL features unique static and dynamic gestures, including contractions, distinct from other sign languages. This study presents the development of a machine vision-based FSL tutor that leverages the MediaPipe framework-specifically, MediaPipe Hands for static gesture recognition and MediaPipe Holistic for full-body dynamic gesture tracking. LSTM networks were used to classify dynamic gestures based on sequential landmark data to capture temporal dependencies in sign execution. The system supports a desktop application platform enabling learners to engage in interactive modules with real-time feedback through visual prompts and audio cues. It utilizes 42 static hand feature landmarks and over 1,662 key points derived from hand, pose, and facial data to ensure accurate recognition and feedback. A total of 50 essential FSL gestures-aligned with the kindergarten curriculum-were modeled, covering alphabet knowledge, vocabulary development, self-introduction, and polite expressions. Performance evaluation using computer vision metrics demonstrated high recognition accuracy for both gesture types. In addition, the System Usability Scale (SUS) and statistical comparisons with traditional instruction methods confirmed the platform's effectiveness and user acceptability. The results validate the system as a comprehensive and accessible solution for FSL education, particularly suited for early learners and self-guided instruction.

12:00 Public Auditability is Not Enough: on the Importance of Separation of Duty in e-Voting Systems

John Ultra (University of the Philippines Diliman, Philippines); Susan Pancho-Festin (U. of the Philippines, Philippines)

Although the e-voting protocol literature is rich, most proposals focus only on the security of certain phases of an election process, such as voting and counting, while assuming the security of other phases. Voting protocols exploit the concept of public auditability to justify the security of the schemes. However, we argue that any irregularity observed later in the election process casts doubt on its integrity. The cost of fixing a mistake or correcting a wrong outcome increases over time. The ability to detect and correct these mistakes, deliberate or not, early on in the election process is critical to maintaining integrity and transparency. To this end, we explore traditional access control concepts such as role-based access control and separation of duty as mechanisms for ensuring the security of elections using e-voting systems. We make two contributions. First, this paper presents the design and implementation of Helios RBAC, an extension of the Helios web-based voting system that supports role-based access control (RBAC) and separation of duty policies. Second, we provide a security analysis of separation of duty policies in e-voting systems, where we examined different threat models. Contrary to intuition, we show that a simple majority is insufficient to ensure an honest majority decision in the presence of imperfect and malicious election administrators.

12:15 Urban Heat Island and Heat Vulnerability Assessment with Ground Validation - a Case Study for Makati City

Charles Andrew C Pasion (Adamson University Philippines, Philippines & National University Philippines, Philippines); Mark Angelo C Purio (Adamson University, Philippines)

Urbanization, characterized by the movement of populations from rural to urban areas, is reshaping societies globally. This transformation is driven by economic, social, and environmental factors that make urban areas appealing, offering better jobs, access to healthcare and education, and improved infrastructure. However, urbanization also presents significant challenges, including the Urban Heat Island (UHI) effect, which exacerbates living conditions in densely populated cities by increasing surface and air temperatures compared to surrounding rural areas. This study addresses the UHI phenomenon and heat vulnerability by conducting an Urban Heat Island and Heat Vulnerability Index (HVI) assessment, incorporating ground validation, for Makati City, Philippines. The UHI Map was developed using Landsat 8 satellite Land Surface Temperature (LST) data. The HVI Map was developed using Landsat 8 Data Product and Demographic data to identify areas at higher risk of adverse heat-related effects. Each indicator scores were normalized, scored, and aggregated to calculate an HVI score for each barangay. Ground validation was performed to verify the results from the UHI and HVI maps. A prototype device was designed, consisting of sensors for ambient temperature and relative humidity, a memory device for data storage, and a battery with a one-month operational lifespan. Three devices were deployed in barangays identified by the HVI Map: two in the most vulnerable barangays and one in the least vulnerable barangay. Over a month, the devices recorded temperature and humidity data, which were analysed to corroborate the maps' findings. The UHI and HVI maps identified areas within Makati City that are most susceptible to heat-related health impacts, offering critical insights for policymakers and urban planners. The integration of socio-economic and environmental factors ensures a holistic understanding of heat vulnerability, enabling evidence-based strategies to mitigate risks. The findings support urban planning efforts aimed at reducing heat exposure through increased vegetation, improved building designs, and the development of heat-resilient infrastructure. Additionally, this research contributes to achieving Sustainable Development Goal (SDG) 11, Sustainable Cities and Communities, by promoting strategies for creating inclusive, safe, and sustainable urban environments.

12:30 A TAM-Guided Mobile Solution to Support Mental Wellness in Higher Education

John Angelo Repollo and John Heland Jasper Ortega (FEU Institute of Technology, Philippines)

Mental health concerns are on the rise among college students in the Philippines, where academic stress and limited access to counseling services continue to pose serious challenges. With mobile technology becoming more integrated into daily life, it offers a practical opportunity to support student well-being through accessible, self-help tools. This study presents the design and evaluation of a mobile application that combines art therapy and sound therapy to help reduce stress and promote relaxation among students in higher education. Guided by the Technology Acceptance Model (TAM), the research explored how users perceived the app's usefulness, ease of use, and overall experience. The app was developed using a blended Agile approach and tested by 50 purposively selected college students experiencing academic stress. Results showed strong user acceptance, with high ratings in ease of use (x̄ = 4.56), satisfaction (x̄ = 4.36), and intention to use (x̄ = 3.96). Perceived usefulness was strongly correlated with both satisfaction (r = 0.73) and continued use (r = 0.78), indicating that the app effectively supported stress relief and user engagement. This study contributes practical insights for integrating mobile wellness solutions in Philippine education, particularly in settings where traditional mental health support remains limited. It encourages the adoption of simple, evidence-based digital tools that promote emotional well-being and help bridge gaps in student support systems.

12:45 FedLEM: Federated Learning with Local Episodic Memory for Data Heterogeneity

Nafas Gul Saadat (Cochin University of Science and Technology & None, India); Santhosh Kumar G (Cochin University of Science and Technology, India)

Federated Learning (FL) is a new learning paradigm that allows collaborative learning among multiple nodes without revealing raw data. However, one important problem faced by the present federated learning algorithms in real-world applications is data heterogeneity. i.e data distribution across clients is different, resulting in low model accuracy, and making the averaged model significantly diverge from the optimal solution. In this study, we propose FedLEM, an efficient federated learning method to address the challenge raised by non-IID data. FedLEM utilizes a small local episodic memory module to selectively store model updates that have been trained on IID batches of data, identified through entropy-based estimation. When a batch exhibits high non-IID characteristics, FedLEM blends the current update with the stored IID updated from memory. This strategy stabilizes optimization and improves model performance under heterogeneous data. By integrating past IID knowledge, this approach helps prevent catastrophic forgetting, improves convergence, and reduces computation and communication overhead in federated learning. FedLEM's effectiveness has been examined on the CIFAR-10 dataset, and the result compared to baseline algorithms: FedAvg, FedProx, FedOpt and Scaffold. FedLEM surpasses baseline algorithms in accuracy and reduces training time by around 16%.

Wednesday, October 29 11:30 - 1:00

Track B5F8 CCI 5.4: Computing & Computational Intelligence (CCI) 5.4

Room: F8. 507 Monsopiad

Chair: Jackel Vui Lung Chew (Universiti Malaysia Sabah, Malaysia)

11:30 Kisan-Mitra: Empowering Farmers with AI-Driven Generative Assistance Transformer Network for Agricultural Advancement

Aditya Oza (IIIT Naya Raipur, India & No, India); Rahul Yadav and Mallikharjuna Rao K (IIIT Naya Raipur, India); Rimjhim Sharma (IIIT NAYA RAIPUR, India)

Agriculture remains the backbone of India's economy, employing over two-thirds of the population and contributing approximately 19% to the national GDP. This paper introduces the Generative Agricultural Transformer (GAT), a novel transformer-based architecture tailored for the Indian agricultural domain. GAT powers Kisan-Mitra, a multilingual generative AI chatbot that delivers context-aware, region-specific guidance aligned with government schemes and agronomic best practices. Unlike generic language models such as BERT or GPT, GAT integrates domain-specific attention mechanisms, cross-lingual embeddings for 12 regional languages, and real-time connectivity with government databases, including the Kisan Call Centre (KCC) and agricultural subsidy portals. As a result, the model is fine-tuned for tasks such as crop advisory, disease diagnosis, seasonal planning, fertilizer optimization, and market price forecasting. GAT achieves a response accuracy of 89.6% and covers over 85% of key agricultural topics, significantly outperforming baseline transformer models. The proposed system is scalable, fully trainable end-to-end, and effectively bridges the digital divide by providing accessible, accurate agricultural assistance to rural communities. This study presents the complete development pipeline of Kisan-Mitra, including dataset construction, architectural innovations, training strategies, and deployment approach. Results indicate that task-specific transformer architectures-when grounded in local context and institutional integration-can substantially enhance the impact and inclusivity of AI-driven agricultural services.

11:45 Benchmarking Hybrid Deep Learning Models for Early Weather Prediction

Deepika Gupta (Indian Institute of Information Technology Vadodara, India); Kushagra Taneja, Chitransh Kumar and Kartik Chugh (IIIT Vadodara International Campus Diu, India)

Weather forecasting is essential for agriculture, disaster management, energy planning, and infrastructure protection. Accurate and timely forecasts support critical decisions, helping to reduce losses and optimize resource use. However, traditional numerical weather prediction (NWP) systems demand high-performance computing resources and long-term historical data, making them impractical for newly deployed or remote weather monitoring stations. To address this limitation, this research proposes and evaluates deep learning-based architectures for weather time series forecasting under varying data availability conditions. Specifically, three standalone models and several novel hybrid models are tested across four temporal scales: 10 years, 5 years, 1 year, and 1 month. The study shows that hybrid models consistently outperform standalone models in terms of accuracy and robustness. Among them, the Temporal Convolutional Network - Long Short-Term Memory (TCN-LSTM) hybrid achieves the best performance, with (R^2) of 0.88 for short-term and 0.99 for long-term temperature forecasting, while remaining computationally efficient. These models offer scalable, accurate forecasting solutions well-suited for early deployment in resource-constrained and data-scarce environments.

12:00 Depth-Based Volume Estimation of Filipino Food Items Through YOLOv8 Custom Dataset

Klarisse Anne C. Mañibo, Francisco G. Joaquim Da Silva, Marie Faye S. Palsimon, Mark Angelo C Purio, Evelyn Q. Raguindin and Melannie B. Mendoza (Adamson University, Philippines)

Accurate food intake tracking is key to a healthy lifestyle, especially for managing diet-related illnesses. Traditional self-reporting methods are often unreliable due to memory and portion size estimation errors. This study introduces a new system that uses object detection and depth mapping to measure the volume of Filipino food items. The system uses a custom dataset with images of three food classes taken from top and side views, each annotated with bounding boxes. It employs the YOLOv8 model for accurate object detection and depth estimation to create 3D food models for precise volume calculation. Achieving 95.4% detection accuracy and a 7.8% average volume error, the system shows promise for dietary monitoring applications. Experimental results prove the system's ability to process the custom dataset effectively, achieving accurate object detection and volume estimations. Through the integration of innovative computer vision methods with culturally appropriate food data, this solution provides a promising method for boosting dietary monitoring and nutritional evaluation in the Filipino community setting. While the model performed well on the three predefined food classes, misclassification of non-target items (e.g., Biko) highlights the absence of a background or rejection class in the training dataset.

12:15 Context-Aware Scene Text Alignment Classification for Mobile Devices

Selvakumar K (Vellore Institute of Technology Vellore, India & VIT Vellore, India); Ayush Kumar and Vivin Varshan S (Vellore Institute of Technology, India); Saptarshi Manna and Sunil Gangele (Samsung R&D Institute, India)

This paper presents a novel lightweight model for text block alignment detection, specifically optimized for on- device deployment in mobile applications. Accurate alignment classification (left, right or center) is critical for downstream tasks such as mobile-based image translation and OCR post- processing. The proposed architecture employs MobileNetV4 as a backbone for efficient feature extraction, integrated with a Feature Pyramid Network (FPN) to enhance context-aware representation across scales. A novel mask-based feature filtering mechanism is introduced to suppress irrelevant visual content and isolate alignment-specific cues. Subsequently, the proposed regions are fed into a lightweight classification module. To ad- dress the inherent differences in contextual dependencies between single-line and multi-line text blocks, the model employs a hybrid feature representation: standard FPN features are used for single- line blocks, while lightweight pyramidal features are used to construct a Pyramidal feature hierarchy for multi-line blocks. Experimental evaluations demonstrate that the hybrid approach achieves superior accuracy compared to using traditional FPN features alone. Furthermore, the model is benchmarked on multiple mobile hardware platforms using Qualcomm's on-device AI execution APIs and the end-to-end inference latency is <10 ms, validating its practicality for real-time deployment.

12:30 Data Oriented Fairness in Cross-Silo Federated Learning

Tomsy Paul (Cochin University of Science and Technology, India & RIT Kottayam, India); Santhosh Kumar G (Cochin University of Science and Technology, India)

Fairness is an active research area in Federated Learning (FL). And Fairness in Contribution Evaluation is an important type in the current literature. Its significance is increased in Cross-silo setting. However the most important component of the FL system, the data is often ignored while studying it. We propose three fairness algorithms from the perspective of the data owner, leveraging respectively the local sample sizes, the sample distributions and both the sample size and sample distributions, offering a unique perspective on fairness in FL. We also provide the implementation of the algorithms in Decentralized FL, a recent area in Federated Learning. A container based implementation makes the development and deployment of the algorithms easy in academia and industry. The experimental evaluation of the algorithms on a typical Cross-silo FL setting with 16 parties show that all the three algorithms provide good fairness to the data owners based on the quantity of data and/or the quality of data.

12:45 Enhanced YOLO Object Detector for Insulator Defect Detection in Power Line Infrastructure

Seema Choudhary (Academy of Scientific and Innovative Research (AcSIR) & CSIR-Central Electronics Engineering Research Institute, India); Sumeet Saurav (CEERI Pilani, India); Ravi Saini (CSIR Central Electronics Engineering Research Institute, India); Sanjay Singh (CSIR-Central Electronics Engineering Research Institute (CSIR-CEERI), India & Academy of Scientific & Innovative Research (AcSIR), India)

Deep learning has shown remarkable capabilities in automatic defect detection in power line infrastructure, but the scarcity of defect-specific labeled datasets often limits its effectiveness. This work addresses the critical challenge of detecting missing disc insulator defects under data-limited conditions by proposing a data augmentation-enhanced YOLOv12 framework. Starting with only 128 original defect images, we systematically applied geometric augmentations, including multi-angle rotations (10°, 20°, 30°) and spatial shifts (horizontal/vertical shifts of 0.1-0.3) to generate 27 distinct variations per image. This strategy expanded the dataset to 3,456 synthetic samples, enriching defect diversity while preserving realistic defect characteristics. The YOLOv12-based framework was evaluated using 5-fold cross-validation, with parallel GPU training used to fully utilize computational resources and reduce training time. Experimental results demonstrate that the diversity of synthetic data, combined with the advanced detection capabilities of YOLOv12, significantly improves model robustness, achieving a 30-34% increase in mAP over non-augmented training and surpassing existing augmentation-based and improved fault detection methods. This study provides a practical approach to overcome data scarcity and advance reliable defect detection in power line inspection applications.

Wednesday, October 29 3:15 - 4:00

Track B6F6 OES 1: OES Session 1

Room: F6. 505 Sepilok

Chairs: Rosmiwati Mohd-Mokhtar (Universiti Sains Malaysia (USM) & Engineering Campus, Malaysia), Mohd Rizal Arshad Status (Xi'an Jiaotong-Liverpool University, China)

3:15 Snail Parasite Detection via Convolutional Neural Network

Glenn C. Virrey, Gregorio L. Martin I, Alexander Clarkk Castro, Jullian Kristof Escubil, Aeon Fran Galvez, Jeremie Langreo, Melaijah Johannz Manansala and Arman Mapoy (University of Santo Tomas, Philippines)

Agriculture plays a crucial role in the Philippine economy, but its proximity to freshwater environments increases the risk of snail-borne diseases such as Fascioliasis, which severely affects both human and livestock health. Detecting parasitic infections in snails is essential to mitigating the spread of such diseases, yet traditional laboratory-based diagnostic methods are time-consuming, invasive, and resource-intensive. While recent studies have applied image recognition techniques to classify snail species, few have focused on detecting parasitic infections directly. This study proposes the use of Artificial Intelligence, specifically a Convolutional Neural Network (CNN), to facilitate mass detection of parasite-infected snails. Snail samples were collected by the Faculty of Pharmacy and imaged using a digital microscope at 175x magnification. These images were pre-processed and used to train and test a CNN model using a 70/30 data separation for training and implementation. The CNN achieved an average testing accuracy of 90.11% with an F1 score of 58.46 in detecting parasite manifestations. During live trials, the model maintained an accuracy rate of 91.21%, and a statistical comparison with expert parasitologist evaluations showed no significant difference (t = -2.39, p = 0.39), indicating the model's reliability as a diagnostic tool. The results demonstrate that CNN-based detection offers a faster, non-invasive alternative to traditional methods, which can significantly aid parasitologists in fieldwork and improve disease monitoring and control in agricultural areas. Future research is recommended to integrate automated imaging systems into diagnostic prototypes and develop real-time detection capabilities for broader deployment.

3:30 Decadal Land Cover Change Analysis and Forecasting in the Mahiga River Watershed Using GIS and CA-ANN Modeling

Leo Nhel D. Abao (Cebu Technological University, Philippines); Ricardo L. Fornis and Jonah Lee I. Bas (University of San Carlos, Philippines)

This study thoroughly examined land cover changes in the Mahiga River watershed using advanced remote sensing and geographic information system (GIS) techniques. Land cover data for the years 2000, 2023, and a future projection for the year 2030 were systematically mapped, processed, and analyzed in detail. The 2030 land cover was accurately predicted using the MOLUSCE plugin in QGIS, a modeling tool that incorporates both cellular automata and artificial neural network models for spatial prediction. Results revealed a significant and measurable increase in built-up areas from 9.33 km² in the year 2000 to 13.19 km² projected for 2030. In contrast, wooded land, covered land, and bare regions showed a consistent and notable decline, decreasing from 5.34 km², 3.51 km², and 0.17 km² in 2000 to 3.98 km², 1.16 km², and 0.02 km², respectively, by 2030. These observable trends underscore the ongoing transformation of the watershed due to urban expansion and land use change, emphasizing the importance of land cover monitoring for effective and sustainable environmental planning.

3:45 Evaluation of Water Hyacinth-Polypropylene Hybrid Adsorbents for Oil Spill Remediation in the San Cristobal River Station of Laguna de Bay

John Aries P Cruz (National University, Philippines); Ma. Kathleen Duran (National University, Philippines & Mapua University, Philippines); John Michael Dellova, Abby Joyce U Oquendo and Raiza H. Viernes (National University, Philippines)

Oil spills in freshwater ecosystems continue to pose critical environmental and economic threats, particularly in highly urbanized watersheds like Laguna de Bay. In this study, we developed and evaluated a hybrid adsorbent composed of water hyacinth (Eichhornia crassipes) and polypropylene for oil spill remediation. Three blend ratios (25:75, 50:50, 75:25) were fabricated and tested in both laboratory and field settings to determine their oil adsorption capacity (OAC) and oil recovery efficiency (ORE). Laboratory analysis followed ASTM F726-12 protocols using gravimetric methods and Soxhlet extraction. The 75:25 blend (PP: WH) achieved the highest OAC (1.631 g/g) and ORE (90.81%), confirmed through statistical tests (ANOVA, Kruskal-Wallis, p < 0.05). Field deployment in the San Cristobal River validated its performance under real conditions. Though more costly per gram of oil adsorbed than commercial polypropylene pads, the hybrid adsorbent offers key advantages in biodegradability and local material sourcing. These results demonstrate the feasibility of developing eco-efficient hybrid materials for oil remediation in tropical aquatic systems.

Wednesday, October 29 4:30 - 6:00

Track B7F6 OES 2: OES Session 2

Room: F6. 505 Sepilok

Chairs: Rosmiwati Mohd-Mokhtar (Universiti Sains Malaysia (USM) & Engineering Campus, Malaysia), Mohd Rizal Arshad Status (Xi'an Jiaotong-Liverpool University, China)

4:30 GIS-Based Flood Vulnerability Map Using the Fuzzy Analytical Hierarchy Process and Response Surface Method: Flood Risk Reduction Program for Valenzuela City

Mark B. Ondac and Dante L Silva (Mapúa University, Philippines); Sheina Pallega (National University, Philippines); Jimmy G. Catanes (Commission on Higher Education, Philippines)

Flooding has been a persistent issue in Valenzuela city due to the overflowing of two major channels such as Meycauayan River and Tullahan River. This study aimed to understand the complex interaction of various factors to flood vulnerability in priority catchments identified using integrated approach. Two priority subbasins were practically selected and clipped from the watershed based on the peak flow simulated using HEC-HMS. Normalized weights computed from FAHP revealed that elevation having a weight of 30.70% is the most influential factor that contributes to flooding while the least is population density with a weight of 2.50%. Final overlay map generated using GIS highlighted that Barangays Marulas, Gen. T. De Leon, and Ugong were classified as highly to very highly vulnerable to flooding while Barangays Malinta, Parada, and Karuhatan were under low to very low vulnerability. RSM provided a better understanding in the effect of the individual parameters and combined effect of parameters on flood vulnerability. 2FI-model emerged as the best model with adjusted R2 and predicted R2 values of 0.8783 and 0.5772 respectively. The interaction between elevation and flow discharge was observed to be significant based on the regression model that was used to generate the contour plots. The illustration of the combined effect of elevation and flow discharge to flood vulnerability helped in the development of flood mitigation strategies. Declogging of canals and retrofitting of drainage system was suggested to Barangays Marulas and Gen. T. de Leon due to low elevation and poorly, clogged drainage. Elevated areas such as Barangay Ugong remained vulnerable to flooding because of large volume of water that must be controlled by installing retention basins and ensuring that upstream drainage is connected to a properly sized downstream drainage.

4:45 Utilization of Mask Region-Based Convolutional Neural Network (RCNN) for Fish Body Length and Height Measurement

John Peter Austria, Kierra Manuel Francisco, Lucas Andrew Lumotan, Charlos Dhanniel Macalinao and Catherine Montalbo (National University Philippines, Philippines); Herbert V Villaruel (De La Salle University, Philippines)

This project leverages computer vision and deep learning technologies to enhance fish sampling processes, providing valuable support to various sectors, including the academe, agriculture, aquaculture, and scientific research. It is specifically developed to assist the manual sampling procedures of the National Freshwater Fisheries Technology Center (NFFTC), focusing on the precise measurement of fish length, width, and weight. Accurate morphometric data are essential for monitoring fish growth, assessing health, and improving yield projections-critical components in both research and aquaculture operations. Traditional manual measurements are often prone to inconsistencies and human error, which can affect data reliability. By integrating intelligent technologies, the system automates data collection, resulting in improved accuracy, efficiency, and consistency. This innovation reduces human intervention while accelerating the sampling process, making it more scalable and repeatable. Overall, the project introduces a data-driven, technology-enhanced solution that strengthens the reliability of fish sampling and supports evidence-based decision-making in fisheries management, education, and sustainable aquaculture practices.

5:00 Assessing Tidal Energy Potential in the Visayas: Viability of the San Bernardino, San Juanico, and Cebu Straits

Justin Ricafort and King Harold A Recto (Ateneo de Manila University, Philippines)

By laying the groundwork for sustainable tidal energy infrastructure, this study contributes to advancing the Philippines' renewable energy portfolio and supports the global transition to clean energy solutions. With the country's extensive coastline and rising energy demands, tidal energy presents a largely underutilized yet promising resource that can address both local and global energy challenges. Tidal energy is highly predictable, stable, and environmentally friendly, offering a reliable alternative to conventional energy sources like coal and natural gas, which are often subject to price volatility and environmental concerns. The study focuses on the Visayas region, a prime candidate for tidal energy development due to its dense population centers, major seaports, and high energy consumption. Integrating tidal energy into the national grid could reduce the Philippines' reliance on fossil fuels and lower carbon emissions. This research explores tidal stream turbines' potential, addressing challenges like maritime traffic and environmental considerations, while suggesting strategies for overcoming these obstacles. Ultimately, this study supports the transition to a cleaner, sustainable energy future.

Thursday, October 30

Thursday, October 30 8:00 - 9:30

Track C6F1 CCI 6.1: Computing & Computational Intelligence (CCI) 6.1

Room: F1. Sipadan I

Chair: Rosalyn R Porle (Universiti Malaysia Sabah, Malaysia)

8:00 VoCare AI: a Multi-Agent LLM Workflow for Improved Clinic Operational Efficiency

Derrick Jo Han Lim, Pai Chet Ng and Malcolm Yoke Hean Low (Singapore Institute of Technology, Singapore)

In Singapore's polyclinics, touch-based self-service kiosks are widely used for administrative functions such as appointment scheduling and billing. However, these systems pose accessibility challenges for elderly patients, often resulting in increased staff workload and longer wait times. This paper presents \textit{VoCare AI}, a voice-first conversational assistant designed to streamline healthcare administrative tasks through multi-agent workflows powered by Large Language Models (LLMs). The system integrates a graph-based orchestration framework (LangGraph) with specialized agents that manage NRIC-based identity verification, appointment handling, billing queries, and general FAQ responses. It features a fine-tuned Automatic Speech Recognition (ASR) model adapted for Singapore-accented English (Singlish), trained on the IMDA National Speech Corpus to handle accent variability, code-switching, and elderly speech patterns. Evaluation results show that the best-performing ASR model achieved a Word Error Rate (WER) of 22.22%, while the Retrieval-Augmented Generation (RAG) module demonstrated strong performance with 1.00 groundedness and 0.92 retrieval relevance. By focusing on Singapore's linguistic diversity and incorporating localized authentication mechanisms, this work demonstrates how AI-driven conversational agents can enhance accessibility and operational efficiency in public healthcare environments.

8:15 QMADM-W: A Hybrid MADM Framework for Cloud Service Selection with Unavailable Data

P Navya (NIT Warangal, India); Sanjaya Kumar Panda (National Institute of Technology Warangal & NITW Techsammelan Private Limited, India); Rashmi Ranjan Rout (National Institute of Technology Warangal, India)

The rapid expansion of cloud computing has made it increasingly difficult for users to determine the most appropriate cloud service provider (CSP). The provider offers diverse services, typically assessed based on quality of service (QoS) attributes, including throughput, reliability, availability, latency, and response time. Researchers often present these QoS attributes in a decision matrix and apply multi-attribute decision-making (MADM) algorithms to evaluate and rank the CSPs. However, in practical scenarios, not all CSPs satisfy every QoS attribute, leading to unavailable performance measure values in a decision matrix. To address this challenge, we develop a hybrid MADM framework for CSP selection that handles an incomplete decision matrix. The framework integrates QoS-aware MADM (QMADM) algorithms, QTOPSIS-W and QVIKOR-W with attribute weights (QMADM-W). It employs three imputation techniques to determine unavailable performance values: minimum (min), maximum (max), and mean. The weights are derived using the analytic hierarchy process (AHP) and the analytic network process (ANP). Simulation results using the QoS for web services (QWS) dataset demonstrate the framework's effectiveness in QTPOSIS-W, with consistent and robust performance observed under the mean imputation technique through sensitivity analysis. The proposed algorithms offer a reliable solution for selecting an optimal CSP, even for an incomplete decision matrix.

8:30 From Encoded Features to Quantifying Disfluency: a Deep Learning Approach for Stuttering Severity Classification

Ashita Batra (Indian Institute of Technology, Guwahati, India); Manpreet Singh Saluja, Prarbdh Tiwari and Pradip Das (Indian Institute of Technology Guwahati, India)

Stuttering is a speech disorder marked by interruptions in the natural flow of speech, affecting millions globally. Accurately assessing its severity-categorized as mild, moderate, or severe is essential for guiding effective interventions and enhancing speech therapy outcomes. In this study, we address the task of stuttering severity classification using two publicly available datasets: FluencyBank Timestamped and LibriStutter. We extract diverse feature representations from Whisper, Wav2Vec2.0, and MFCCs to capture both learned and handcrafted speech characteristics. These features are evaluated using three deep neural network classifiers: CNN, ResNet50 and Transformer. Among the combinations tested, Whisper-Small embeddings paired with ResNet50 yielded the highest performance, achieving an overall accuracy of 82.1% and an F1 score of 0.778. While, the other Whisper variants also performs fairly competitive with an accuracy of 77.7% on Base and 76.6% on tiny model using ResNet50. Our proposed setup establishes a strong benchmark and a potential state-of-the-art-for future research in automatic stuttering severity assessment.

8:45 Person Re-Identification with Structural Semantic Graphs in Resource-Constrained Environments

Munir Bin Rudy Herman and Pai Chet Ng (Singapore Institute of Technology, Singapore); Olivia Shanhong Liu (Singapore University of Technology and Design, Singapore)

Person re-identification (ReID) aims to retrieve images of the same individual across disjoint camera views in surveillance networks, where resource constraints and privacy concerns often make traditional deep learning approaches impractical. This paper presents a lightweight, training-free ReID framework based on structural semantic graphs, designed for deployment in resource-constrained environments. Each person is represented by a multi-layer graph encoding body parts, fashion items, and dominant colors, extracted using pretrained pose estimation, fashion detection, and color clustering models. Without requiring any task-specific fine-tuning or GPU acceleration, our method enables interpretable matching through a hybrid similarity function that blends cosine similarity over weighted features with Jaccard similarity over attribute sets. Evaluated on the DukeMTMC-reID dataset, our method achieves 27.06% Rank-1 accuracy and 8.10% mAP, outperforming attribute-based baseline and random retrieval method. Further experiments validate that our hybrid similarity computation enhances retrieval performance and confirm that our bag-of-features graph encoding offers an effective solution for training-free ReID in resource-constrained environments.

9:00 Beach Litter Detection Using SwinTransformer Optimized Model for Drone Image

Poorna Pushkala K (Vellore Institute of Technology - Chennai Campus, India); Subbulakshmi P (Vellore Institute of Technology, India)

Beach litter detection (BLD) is the process of detecting and classifying abandoned materials in the beach environment. Drone image acquisition in BLD plays a crucial role in environmental conservation and waste management, particularly in unmanned coastal areas. Existing models primarily rely on traditional image classification techniques, which involve manually extracting features from the images. However, these methods often struggle with high false positives, low accuracy, and inefficiencies in processing large datasets from drone imagery. To overcome these limitations, this research proposes a new model, the Hybrid Swarm Gray Optimization-based Hybrid SwinNetCAM (SGO-SwinNetCAM) framework. The SGO-SwinNetCAM model contributes by integrating the Swin Transformer to capture both global and local features of the litter images, EfficientNetV2 for computational efficiency, and CBAM (Convolutional Block Attention Module) for refined feature representation. This hybrid combination optimizes performance in detecting and classifying beach litter. Additionally, the SGO algorithm fine-tunes hyperparameters, enhancing model accuracy and generalization. The proposed model aims to enhance the accuracy, efficiency, and robustness of BLD in drone imagery, even under challenging environmental conditions.

9:15 ActivityNet-HE: An Encryption-Enabled Deep Learning-Based Framework for Secure Human Activity Monitoring

Ahsanul Islam, Sadia Akter and Tahsina Farah Sanam (Bangladesh University of Engineering and Technology, Bangladesh)

An increasingly popular non-intrusive monitoring technique in smart environments is the recognition of human activities using Wi-Fi Channel State Information (CSI). However, the exposure of raw CSI data during processing poses significant privacy risks. This paper presents ActivityNet-HE, a deep learning framework designed to perform secure activity recognition through homomorphic encryption. The framework utilizes Principal Component Analysis (PCA) to minimize the dimensionality of the data makes it efficient to compute and extracts 29 statistical and signal-based features from time and frequency domains for each PCA stream. A lightweight neural network with polynomial activation enables encrypted inference without revealing sensitive input data. Evaluated on the CSI-HAR dataset, the model achieves 94.05% accuracy on both encrypted and plain data, demonstrating that strong privacy preservation can be achieved without sacrificing performance. While the system ensures secure inference, it incurs additional latency due to encryption overhead, which may limit its use in low-latency real-time applications. This makes the framework especially relevant for privacy-sensitive cases like monitoring the stereotypical motor movement (SMM) of autistic children in home or healthcare settings.

Thursday, October 30 8:00 - 9:30

Track C6F2 ECD 6.1: Electronics, Circuits & Devices (ECD) 6.1

Room: F2. 501 Kadamaian

Chair: Bih Lii Chua (Universiti Malaysia Sabah, Malaysia)

8:00 Attentive Depth-Mapped Dice Loss for Accurate Segmentation of Smaller Objects in Medical Images

Dibakar Malakar, Joyita Bhattacharjee and Rinku Rabidas (Assam University Silchar, India)

Accurate segmentation of medical images plays an important role in clinical diagnosis, and has gained attention in recent years. Deep Learning based approaches are exploited for lesion segmentation in medical images due to its promising performance. In Deep learning, loss functions are crucial for proper training and evaluation. Dice loss, being simple and effective, is the most commonly used region based loss function for semantic segmentation, but has several limitations which includes insensitivity towards the boundaries and the distance between non-overlapping regions resulting in poor delineation of small lesions. To mitigate this issue, an Attentive Depth-Mapped Dice (ADD) loss function is proposed that incorporates distance maps derived from ground truth masks via Distance Transform and Signed Distance Transform, guiding the networks focus towards hard-to-segment boundary regions. Compared to Dice loss, the proposed approach achieves 24.32% and 12.44% improvement in HD95 scores, for small objects like Pancreatic Tumor and Hepatic Vessel, respectively. However, 10.04% increment is observed for Hepatic Vessel with minor improvement in pancreatic tumor in-terms of dice score.

8:15 Graphene-Based Bow-Tie Plasmonic Tweezers for Enhanced Optical Trapping at Terahertz Frequencies

Swapnil Siddiky (Bangladesh University of Engineering and Technology, Bangladesh); Ahmed Zubair (Bangladesh University of Engineering and Technology, Bangladesh & Rice University, USA)

Graphene-based plasmonic tweezers achieve strong localized field confinement by taking advantage of their low-loss properties and unique tunable conductivity. To improve near-field localization and optical trapping performance, we designed an optical nanotweezer structure in the shape of a bow-tie with a gap at the center. Using extensive parametric analysis, we explored structural dimensions, including gap size, length, and width, impact electric field, trapping performance, and localized surface plasmon resonance. Our findings demonstrated that while width primarily affected near-field strength, increasing length caused a redshift in the resonant frequency. We obtained a peak optical force of -1.57 nN/Wμm-2 at 7.3 THz frequency on a nanoparticle by introducing an 8 nm gap between the two bow-tie triangles, which is twofold higher than that of the gapless bow-tie structure. A deep optical potential well (> - 8x103 kBT/Wμm-2) was revealed close to the surface. The field and potential variation along the vertical axis further confirmed stable trapping. These findings highlight the potential of graphene plasmonic tweezers for high- precision dielectric and biological nanoparticle manipulation in lab-on-a-chip applications by demonstrating their strong geometrical tunability.

8:30 Preventive Maintenance Service (PMS) Scheduling: Development of Transformer Monitoring Device Considering Temperature and Vibration

GP Florianne Maghuyop Jabagat (Mindanao State University - Iligan Institute of Technology, Philippines); Marven E Jabian (MSU - Iligan Institute of Technology, Philippines & Mindanao State University - Iligan Institute of Technology, Philippines)

This study presents the development of a distribution transformer monitoring device purposefully designed to measure temperature and vibration, and its evaluation. The system constitutes a monitoring device energized by a solar-powered source scheme and a centralized data receiver. Its two-layered printed circuit board is customized through KiCAD, ensuring efficiency in component layout and optimized signal integrity. The enclosure design is made possible through Fusion 360, ensuring it features a secure mounting, appropriate placement of sensors, protection from the environment, and the inclusion of a solar charging system for autonomous operation. Accommodating the limitation of the NRF24L01 transceiver, the firmware implemented efficient data structures and its protocols for transmission. Sensing, storing, and organizing incoming data from multiple transmitters without collision is executed successfully by the centralized data receiver, aiding serial communication for the Human-Machine Interface (HMI). The garnered results proved the validity of the performance and consistency of the device subjected to changing circumstances. Consequently, the study infers that the monitoring device has the potential to serve as a solution for real-time condition monitoring of transformers, highlighting potential deployment in electrical distribution systems, thus contributing to enhancing maintenance practices and ensuring a reliable system.

8:45 Study of Insulative and Dissipative Material Surface Resistance Across Temperature and Humidity

Jeevan Kanesalingam, See Fung Lee and Hock Guan Ho (Motorola Solutions, Malaysia)

In the explosive atmosphere, and ignition can occur if a static spark comes into contact with explosive gasses or dust. An enclosure surface with a high surface resistance has a higher chance of surface electrostatic charge buildup due to the reduction of transferred charge. A reduction of transferred charge (increased surface resistance) would subsequently cause a higher electrostatic charge buildup on the surface; which increases the likelihood of a spark ignition in the explosive atmosphere. Surface resistance can vary with temperature and humidity (%RH). This paper studies the change of surface resistance of 3 different materials (PVC, ESD mat, PET) across temperature and humidity (%RH). Temperatures and humidity ranging from 15℃ - 70℃ and 30 - 90 % relative humidity (%RH) was used for the study. The paper confirms that % RH and surface resistance have an inverse relationship while the relationship between temperature and surface resistance is also having an inverse relationship.

9:00 Numerical Evaluation and Simulation-Based Performance Analysis of Transition Metal Dichalcogenide (MX₂; M=Mo, W; X=S, Se, Te) Heterojunction Photodetectors

Sourav Podder and Areeb Ahmed Zulkifle (Bangladesh University of Engineering and Technology, Bangladesh); Farseem Mohammedy (Bangladesh University of Engineering & Technology, (BUET)., Bangladesh)

A systematic numerical investigation is conducted on two-dimensional transition metal dichalcogenide (TMD)-based photodetectors comprising a heterostructure of p-CuO/MX₂/n-TiO₂, where MX₂ (M=Mo, W; X=S, Se, Te) denotes a family of layered semiconductors with tunable bandgaps. The device architecture incorporates a wide-bandgap window and back-surface layer to enhance carrier selectivity and suppress interfacial recombination. Key performance metrics, including photogenerated current density, spectral responsivity, and external quantum efficiency, are evaluated under standard solar illumination conditions. Parametric variations in absorber layer thickness and defect density are analyzed to assess their impact on photodetector performance. Among the studied materials, Tungsten Ditelluride (WTe₂) and Molybdenum Diselenide (MoSe₂) exhibit enhanced near-infrared sensitivity, achieving responsivity values up to 0.72 A/W. Furthermore, the spectral responsivity of the photodetectors was analyzed at various temperatures, where different active layer materials exhibited distinct trends. The results highlight the spectral adaptability and design tunability of TMD-based heterojunction photodetectors, positioning them as promising candidates for integration into broadband, high-performance optoelectronic platforms.

9:15 EEG Based Motor Imagery of Hands Classification Using Modified Sub-Band Common Spatial Pattern

Yash Madanlal Kashyap (Indian Institute of Technology Kharagpur, India); Eashita Chowdhury (Indian Institute of Technology, Kharagpur, India); C S Kumar (Indian Institute of Technology Kharagpur, India); Manjunatha Mahadevappa (Old NCC Building & Indian Institute of Technology Kharagpur, India)

Brain-computer interfaces (BCIs) leveraging motor imagery (MI) create intuitive systems that translate brain activity into direct control of computer applications. Motor imagery is a technique that provides simulated solutions during training for motor problems. For this study, Electroencephalography(EEG) data is collected from fourteen healthy subjects with 11 channels for two tasks with 40 repetitions each; the data were sourced from a publicly available database, ‘OpenVibe'. The present work proposes a modified Subband Common Spatial Pattern (mSBCSP) with Linear Discriminant Analysis as a method to classify right and left hand motor imagery movements, which provides a significant accuracy of 91.79% compared to other methods. The mSBCSP features are extracted from six EEG sub-bands after band pass filtering to enhance the discriminatory features. The results suggest that the proposed method offers a reliable and efficient framework for MI classification, which can serve as a foundation for future development of BCI-controlled applications for motor rehabilitation and the multimedia industry.

Thursday, October 30 8:00 - 9:30

Track C6F3 PES 6: Power, Energy & Electrical Systems (PES) 6

Room: F3. 502 Mesilau

Chair: Nur Ashida Salim (Universiti Teknologi MARA, Malaysia)

8:00 Galvanic Corrosion and Thermal Modelling of ACSR/TW Conductor in a Fully Immersed Acidic Industrial Electrolyte

Shahnurriman Abdul Rahman and Izzat Nawawi (Universiti Sains Islam Malaysia, Malaysia); Konstantinos Kopsidas (University of Manchester, United Kingdom (Great Britain)); Shamsul Fahmi Bin Mohd Nor (Universiti Sains Islam Malaysia, Malaysia & USIM, Malaysia); Norhidayu Rameli and Izzuddin Mat Lazim (Universiti Sains Islam Malaysia, Malaysia)

Galvanic corrosion is a critical degradation mechanism affecting the long-term performance of overhead conductors, particularly in industrial environments characterized by high acidic pollutants. This study investigates the coupled effects of galvanic corrosion and thermal behaviour in Aluminium Conductor Steel Reinforced (ACSR) with trapezoidal wires (TW) when fully immersed in an acidic industrial electrolyte. Using COMSOL Multiphysics, a comprehensive electrochemical-thermal simulation was developed to model the degradation and heat transfer characteristics of both pristine and corroded trapezoidal wire conductors, with the codename of Drake/TW. The analysis considers different pH levels (6.5 and 5.0) to reflect varying degrees of industrial acidity under a sustained operating temperature of 75 °C and 100 °C. At 100 °C operating temperature, simulation results reveal that, over a four-year period, aluminium loss of up to 62.4 mm² at pH 6.5 leads to a conductor temperature rise of 10.3 °C and a reduction in ampacity by approximately 85 A. Under more aggressive conditions at pH 5, corrosion accelerates, resulting in a greater temperature increase (16 °C) and ampacity reduction of up to 103 A. These findings highlight the substantial thermal impact of galvanic corrosion and emphasize the necessity of integrating corrosion evolution into ampacity assessments to ensure safe and reliable operation of overhead transmission lines in corrosive industrial environments.

8:15 Comprehensive Analysis of Solar Power Generation: Time-Series Dynamics and Clustering Patterns for Predictive Modeling

Hasanur Zaman Anonto (American International University-Bangladesh, Bangladesh); Md Ismail Hossain (American International University Bangladesh, Bangladesh); Afrin Tanzila Rabbani, Samiul Ahasan Sajid, Abu Shufian and Toriqul Islam (American International University-Bangladesh, Bangladesh); Md Sultan Mahamud (Lamar University, USA)

The growth of the renewable energy target has contributed to increasing the needs of a powerful solar generation system. But those caused by solar power production itself from irradiation, temperature, and the state of the solar panels present tough challenges in terms of prediction, beneficial use and optimization. The objective of this work is to model the solar power generation forecasting accurately using machine learning models like Linear Regression, Decision Tree Regressor and Random Forest Regressor. The work investigates the influence of the environment on the power output, underlining the diurnal variability of irradiation and temperature daily. Here, it describes how the use of time-series modelling and clustering algorithms help identify patterns of solar power behavior distilled from data collected across multiple months and locations. The results show that compared to other models the Random Forest model achieved a boosted accuracy rate of forecasting by capturing non-linear overheads and minimizing the variance. The best-case scenario yields 92.5W peak DC power generation and the worst-case scenario shows 12W minimum peak DC power generation, and solar power generation is heavily affected by temperature and irradiation changes as in daily temperature fluctuations from 20°C to 85°C in addition to combining anomaly detection using Z-Score analysis and Isolation Forest algorithms to identify problems such as degradation of panels and fault of sensors. These findings emphasize the necessity for a near real-time model and complex forecasting models to mitigate issues associated with renewable energy precursors to increase solar energy productivity and efficiency.

8:30 Technical and Economic Assessment of Renewable Energy Integration into Saudi Arabia's Fossil Fuel Based Grid

Zaid S. Al Otaibi (King Abdulaziz City for Science and Technology (KACST), Saudi Arabia); Mazen A. Baabbad and Saad S. Alshahrani (King Abdulaziz City for Science And Technology, Saudi Arabia)

Saudi Arabia faces a pivotal moment in its energy transition, aiming to diversify electricity generation while remaining a top energy exporter. With abundant solar and wind resources, the Kingdom has strong renewable potential but must overcome operational, economic, and infrastructure challenges. Electricity transmission, especially over long distances, involves losses and high costs, with trade-offs between HVAC and HVDC technologies. This study examines these costs alongside utility-scale renewable projects, highlighting solar PV and onshore wind's growing cost-competitiveness but also their intermittency and low capacity factors that limit grid efficiency. Fossil-based dispatchable generation, like Open Cycle and Combined Cycle Gas Turbines, remains crucial for reliability. Using California as a case study, the paper reveals barriers such as curtailment, seasonal variability, and limited storage that challenge full renewable reliance. Given that fossil fuels still generate 80% of Saudi electricity, transitioning to a hybrid system demands region-specific modeling of generation, transmission, and storage. This research offers a framework to support Saudi Arabia's energy diversification and carbon reduction goals sustainably.

8:45 Data-Driven Optimization of Wind-Solar Tower Performance a Machine Learning Approach to Thermal Updraft Prediction

MD. Minhajul Islam (American International University - Bangladesh, Bangladesh); Ahmed Intekhab Rohan (Islamic University of Technology, Gazipur, Bangladesh); Hasanur Zaman Anonto (American International University-Bangladesh, Bangladesh); Md Ismail Hossain (American International University Bangladesh, Bangladesh); Md Mukter Hossain Emon and Md Akteruzzaman (Lamar University, USA); Abu Shufian (American International University-Bangladesh, Bangladesh); Md Siddikur Rahman (Universiti Teknologi PETRONAS, Malaysia)

This paper aims at examining how machine-learning can be applied to achieve optimal wind-solar hybrid towers performance in relation to predicting thermal updraft. Wind-solar towers use solar irradiance and wind speed to generate energy but due to the complex nature of interaction between the various environment variables, wind-solar towers are relatively hard to predict the thermal updraft due to the interaction between the solar irradiance, air temperature, and wind velocity. A complete dataset that covers 90 days is used, and various machine-learning models, such as linear regression, support vector machines, and neural networks, are tested to make a prediction of thermal updraft. The findings show that among all the alternative models, linear regression had a much superior predictability and efficiency in computation. The obtained result showed a close-to-perfect fit, R2 being equal to 0.98 and indicating the extremely high ability of the regression model to make predictions of the updraft velocity under heterogeneous environmental conditions. Sensitivity analysis had demonstrated that solar irradiance and wind speed are the best predictors of thermal updraft thereby offering insights that would be adopted to streamline the hybrid renewable energy system, and energy forecasting with potential to boost power generation especially during days with low solar irradiance.

9:00 Data-Driven Identification of VHIF-Prone Areas Using Waveform Behavior and Unsupervised Learning

Sophia Mae M Gascon (Mindanao State University-Iligan Institute of Technology, Philippines); Rovick Tarife (Mindanao State University- Iligan Institute of Technology, Philippines)

This paper presents a data-driven approach for identifying high-impedance fault (HIF)-prone areas in power distribution networks. The study focuses on staged vegetation-related HIFs (VHIFs) drawn from the publicly available VeHIF dataset. Recognizing the scarcity of real-world HIF data and the absence of precise location labels, the method simulates fault occurrence across the IEEE 14-bus test system by probabilistically assigning VHIF cases to distribution network. High-frequency voltage signals are analyzed to extract discriminative waveform features, including wavelet-based coefficients and power spectral density (PSD) peaks, which are aggregated per line to capture behavioral risk patterns. K-means clustering is then applied to group lines into low-, medium-, and high-risk categories, enabling unsupervised classification of VHIF-prone regions. The results demonstrate that even without location-specific training labels, meaningful distinctions in VHIF risk levels can be inferred from signal behavior. This framework provides a scalable and interpretable diagnostic tool to support proactive fault management and wildfire mitigation strategies in data-limited environments.

Thursday, October 30 8:00 - 9:30

Track C6F4 ECD 6.2: Electronics, Circuits & Devices (ECD) 6.2

Room: F4. 503 Dinawan

Chair: Maizatul Zolkapli (Universiti Teknologi MARA, Malaysia)

8:00 FPGA Implementation of Lorenz System Based RO-PUF

Kota Naga Srinivasarao Batta (NIT Warrangal, India); Hemanth Reddy Kattamanchi and Mahesh Vanga (National Institute of Technology Warangal, India); Suresh Babu Erukala (Assistant Professor, India)

This paper presents the design and implementation of a Lorenz chaotic system-based Ring Oscillator Physically Unclonable Function (RO PUF) on Field Programmable Gate Arrays (FPGAs). PUFs have evolved as an important component of hardware security, providing unique IDs derived from inherent process variations during integrated circuit fabrication. The chaotic dynamics of the Lorenz system, which is recognised for its unpredictability and sensitivity to initial conditions, give a unique technique to creating random and secure challenges for PUF. The present research demonstrates the viability of incorporating chaotic systems into PUF designs to enhance security. The proposed system offers a reliable and adaptable way to generate secure and distinctive IDs that can be applied to a range of embedded systems, IoT, and cybersecurity applications. The results emphasise the potential of chaotic dynamics to considerably enhance the security features of hardware-based cryptographic primitives. By utilising the intrinsic variability of the Lorenz system, the proposed design optimises the security features of the PUF, guaranteeing a high degree of unpredictability and uniqueness. The experimental results indicate that the design is relatively low in hardware overhead, has a reliability of 97.79%, and a uniqueness of 48.91%. Additionally, the design parameters can be adjusted to alter the output response by the design parameters.

8:15 Biasing Schemes & Loading Effects in Analog Selectors Built with Transmission Gate Logic

Md Al Amin Ashik, Toufiq Mohammad Hasan, Jahidul Islam, Tamima Tabassum and Nafisa Noor (North South University, Bangladesh)

Transmission gate logic (TGL) is a popular circuit connection technique for analog selectors. Despite the numerous IC models and existing literature on simulation-based performance analysis for TGL-based selectors, the biasing strategies and loading effect problems are still not well discussed or characterized. In this work, we present these two vital issues with detailed SPICE circuit simulations and measurements conducted on hardware circuits. We have observed that 4-terminal MOS transistors with fixed body bias and face-to-face arrangement of parallelly connected NMOS and PMOS transistors are the only viable biasing schemes for TGL-based selectors. We have monitored considerable loading effects for load resistances lower than approximately 10 kilo-ohms from simulations and characterization of 1:2 and 1:4 demultiplexers and 2:1 and 4:1 multiplexers. We have discussed the underlying reasons behind the loading effect by analyzing the overdrive voltages, operation regions, drain-to-source resistances, and body effects of the NMOS and PMOS transistors at the active channels of selectors.

8:30 Design of a Compact Octagonal Patch Antenna with Defected Ground for Multiband 5G Applications

Md. Omar Faruq, Md. Ehsanur Rahman and Afzal Hossain (Aviation and Aerospace University Bangladesh, Bangladesh); Pran Kanai Saha (Bangladesh University of Engineering and Technology, Dhaka, Bangladesh)

This paper presents a compact octagonal patch antenna featuring two slots on the top and a central slot, designed to achieve multiband operation within the 5G frequency spectrum. A circular defected ground structure is incorporated, and the antenna is fed using a microstrip line. The design and analysis are conducted using CST simulation software, examining the effects of varying the sizes of three circular slots on the patch along with the defected ground. The proposed antenna operates at eight resonant frequencies within the 0-30 GHz range, specifically at 3.3, 3.8, 5.28, 7.8, 13.92, 16.56, 19.89, and 24.81 GHz. Key performance characteristics such as S-parameters, impedance response, and both 2D and 3D radiation patterns are analyzed. The S-parameter obtained from the simulation is experimentally verified, showing good agreement with the simulation results. With a compact size of 40mm × 40mm, this antenna is well-suited for FR1, FR2, and future 5G applications.

8:45 Inductive/Capacitive Coupling Hybrid WPT for Implantable Medical Device: Basic Analysis of Coupler Structure

Miyu Kodama and Dairoku Muramatsu (The University of Electro-Communications, Japan)

Wireless power transfer (WPT) is rapidly expanding beyond consumer and industrial applications and is now being investigated for wirelessly powering implantable medical devices (IMDs). Efficiency losses caused by coil misalignment in inductive links and electrode spacing in capacitive links motivate a millimetre-scale inductive/capacitive hybrid coupler (ICCH-WPT). In this study, we design and evaluate an ICCH-WPT coupler designed for abdominal IMDs such as abdominal neurostimulators. Electromagnetic field simulations using a multilayered human abdominal model show that a 7.5 mm × 7 mm hybrid coupler can deliver 5.85 mW at 1 MHz-66% higher than a capacitive-only design-while complying with human safety guidelines on electromagnetic exposure. The field energy is strongly confined near the coupler, minimizing deep-tissue exposure and indicating excellent biological safety. ICCH-WPT therefore satisfies the 3-5 mW power budget of modern neurostimulators. This makes it a promising solution for next-generation, wirelessly powered implantable devices requiring stable and safe wireless energy links.

9:00 Condition Monitoring for Induction Motors: Vibration and Temperature Data Acquisition for Predictive Maintenance Analysis

Godfrey F Mulit (Mindanao State University - Iligan Institute of Technology, Philippines & College of Engineering, Philippines); Harreez Villaruz (Mindanao State University - Iligan Institute of Technology, Philippines); Marven E Jabian (MSU - Iligan Institute of Technology, Philippines & Mindanao State University - Iligan Institute of Technology, Philippines)

Induction Motors are the backbone and workhorse machines in various industry sectors. Primarily used to provide increased efficiency and productivity in many industrial processes, minimizing equipment downtime and life-cycle improvement are of utmost importance. This study focuses on the design and development of a non-intrusive, microcontroller-based condition monitoring device for the vibration and temperature operating parameters of induction motors.

In this study, hardware and software systems are developed. An accelerometer module and infrared temperature sensors are integrated with ESP32-based development boards, as well as the application of radio frequency transceiver modules for wireless data transmission. Printed circuit boards are designed and fabricated alongside a 3D-printed enclosure to house all of the hardware components. Data acquisition techniques for the vibration and temperature parameters are implemented and transmitted through a combination of ESP-NOW and radio frequency communication protocols. These features allow real-time condition monitoring of induction motors even without physical intervention on the internal components of the machine. The condition monitoring device is tested on a developed vibration generator platform under different operating conditions with the use of a digital PWM control. The prototype device is shown to achieve all the intended functions, as well as provide potential for scalability. This paper recommends future studies to incorporate optimization on power management, integration of different communication protocols, reduction of device footprint, as well as field trials for outdoor, weather-exposed applications.

9:15 A Cost-Effective Approach to PCB Structure and Process for Enhanced High-Speed Signal Integrity

Jeonghyeon Choi and Youbean Kim (Myongji University, Korea (South))

As the use of high-speed signals continues to grow, precisely controlling signal loss has become an increasingly challenging task in the electronics industry. This paper focuses on dielectric loss that occurs in transmission channels during high-speed signaling and proposes a novel printed circuit board (PCB) structure to effectively mitigate such loss. To evaluate the feasibility of the proposed structure, electromagnetic simulations were conducted to analyze the electrical characteristics of the PCB. Furthermore, in order to reduce additional manufacturing costs, a practical PCB fabrication method that does not require high-end equipment was developed, and a prototype was manufactured. Finally, using a vector network analyzer (VNA), the electrical performance and signal quality improvement of the prototype were thoroughly evaluated. The experimental results demonstrate that the proposed structure can effectively reduce dielectric loss in the transmission channel while minimizing the need for expensive low-loss dielectric materials, making it a cost-efficient solution for high-speed PCB applications.

Thursday, October 30 8:00 - 9:30

Track C6F5 CS 6 / CSR 5: Communication Systems (CS) 6 / Control Systems & Robotics (CSR) 5

Room: F5. 504 Madai

Chair: Azwan Mahmud (Multimedia University & Telekom Malaysia, Malaysia)

8:00 Distributed Algorithms for Age Optimal Scheduling in Multi-Hop Wireless Networks

Ramakrishnan K (Indian Institute of Technology Tirupati, India); Teena Mary Treesa (IIITDM Kancheepuram, India); Premkumar Karumbu (Indian Institute of Information Technology Design and Manufacturing Kancheepuram, India)

In this work, we consider a scheduling problem for a multihop network with multiple flows. For each flow, the source nodes generate packets that carry time-sensitive information to the destination, and the goal is to achieve a scheduler that has the minimum expected weighted sum age of information. This problem has significance in real-time systems such as IoT, industrial process control, and remote monitoring, where timely updates are more important than throughput or latency. Centralized age optimal scheduling policies are often impractical for large-scale networks due to the overhead and coordination required. We propose a distributed adaptive gossip-based scheduling algorithm for achieving minimum age. We compare our proposed scheme with state of the art distributed algorithms such as Fresh-CSMA and Greedy Maximal Scheduling, highlighting the trade-offs between performance and overhead. Through extensive simulations, we demonstrate that our algorithm achieves better performance under a wide range of network conditions and traffic loads while maintaining lightweight communication complexity.

8:15 Biometric Parameter Selection Towards Human Identity Recognition Using Wi-Fi CSI Sensing

Antoinette R Anastacio, Joshua Jake M Benitez, Ryan Carlo DJ Enriquez, Miguel Albert P Icuspit, Ken Wesley O Lampa, Azel Monique O Pangan and Josyl Mariela Rocamora Reyes (University of Santo Tomas, Philippines)

Traditional human activity recognition (HAR) systems utilize vision and wearable sensors to sense and identify human movements. When applied for security and surveillance, these HAR systems can also be utilized for human identity recognition (HIR), where the sensing technologies authenticate users. Recently, Wi-Fi sensing using channel state information (CSI) has emerged as a promising alternative technology for HIR applications, wherein the users are typically identified by their gaits as the primary biometric parameter. As there are other possible biometric parameters, this study explores Wi-Fi CSI sensing in HIR where the subjects perform three physical (static) and two behavioral (dynamic) biometric parameters. A CSI dataset involving samples collected from six subjects in a controlled indoor environment was preprocessed using discrete wavelet transform (DWT) and principal component analysis (PCA). Different parameter-specific HIR models were trained and tested and the HIR model based on the ‘draw triangle' behavioral parameter achieved the highest HIR accuracy of 98.61%. Results show that the five investigated gestures are viable biometric parameters alternative to gait.

8:30 Implementation of EGWO and ISCO Algorithms for Minimizing Transmission Power in IoT Clusters with Full Connectivity

Kotte Sowjanya (Kakatiya Institute of Technology and Science, India); Satish Kumar Injeti (National Institute of Technology Warangal, India & NITW, India)

In this day and age of the Internet of Things (IoT), it is of the utmost importance to guarantee a connection that is both stable and efficient in terms of energy consumption among sensor nodes. Wireless technology researchers and developers

I frequently come into a situation in which the energy requirements of sensors and the longevity of their total communication are in conflict with one another. As a consequence of this, a number of researchers are focusing their attention on locating the best possible answer. Improved Grey Wolf Optimization (IGWO) and Improved Sine Cosine Optimization (ISCO) are two algorithms that are developed, implemented, and utilized in this work. The purpose of these algorithms is to reduce the amount of energy that is consumed by a sensor node while simultaneously ensuring that all nodes are completely interconnected. By applying the IGWO and ISCO algorithms, the findings that were obtained suggest that the proposed technique produces superior results in comparison to those that were discovered in the literature. This is particularly true in terms of the amount of energy that is saved and the dependability of connectivity. EGWO and ISCO-based solutions were presented as a means of reducing the amount of power that is consumed by wireless sensor Internet of Things networks, according to the findings of the article.

8:45 Integrated Circuit Binning Count System for Semiconductor Manufacturing

Mark Angelo T Mercado, Oliver A. Medina and Juliet O. Niega (National University, Philippines)

The semiconductor industry in the Philippines accounts for nearly 60% of the country's total exports, playing a critical role in the national economy. However, many production facilities continue to depend on outdated system particularly those operating on legacy Windows NT platforms with decentralized, manual processes for Integrated Circuit (IC) binning. This study presents the design and implementation of an Integrated Circuit Binning Count System (ICBCS) that leverages microcontroller-based hardware to automate and centralize binning data acquisition. The system interfaces with legacy testing equipment to monitor IC classification pulses in real time, display local counts, and wirelessly transmit data to a centralized web-based server for storage, analysis, and reporting. The proposed solution significantly improves traceability, reduces human error, and enhances operational efficiency. Designed as a low-cost, scalable system, ICBCS supports the semiconductor industry's transition toward Industry 4.0 standards. Field testing in an actual manufacturing environment demonstrated its effectiveness, ease of integration, and adaptability to various production line configurations.

9:00 Constrained GA Optimized Super Twisting Sliding Mode Controller for 2-DoF Robotic Arm

Vishal Mehra (Gujarat Technological University & Anand Agricultural University, India); Dipesh Shah (Gujarat Technological University, India); Axaykumar Mehta (Institute of Infrastructure Technology Research and Management, India); Siddharth Mehta (Arizona State University, Tempe, AZ, USA)

This paper presents a constrained optimization method for the Super-Twisting Algorithm using a Genetic Algorithm. The Super-Twisting Algorithm is an important class of higher-order sliding mode strategy commonly employed in designing robust controllers and observers that effectively mitigate the chattering effect observed in conventional sliding mode control. The performance and stability of the dynamical system critically rely on the appropriate selection of gain parameters, so it is necessary to choose the optimal gains for the controller. A novel approach to optimize the control gain parameters using a constrained Genetic Algorithm is proposed to achieve the desired dynamic response. The constraints are derived from a strict Lyapunov function, thereby ensuring the finite-time stability of the closed-loop system through Lyapunov stability criteria. The performance of the proposed optimized gains is evaluated through real-time experiments on a 2-degree-of-freedom serial flexible joint robotic arm. The experiment results show that the proposed technique performs better regarding dynamic response, disturbance rejection, and chattering reduction.

9:15 Data Driven Controller for 6-DOF Quadcopter

Harikrushn Surani (M.Tech, India); Nikita Joshi (IITRAM, Ahmedabad, India); Axaykumar Mehta (Institute of Infrastructure Technology Research and Management, India)

This paper introduces a data-driven Artificial Neural Network (ANN) controller for a 6-DOF quadcopter, leveraging advanced machine learning techniques to address limitations in traditional control systems. The proposed data-driven controller eliminates the need for a mathematical model of the system, relying solely on accurate data for its design and functionality. The controller is trained on real-time data captured via OptiTrack motion capture and onboard sensors, ensuring precise position, orientation, and velocity tracking. Experimental results demonstrate the controller's superior performance in both hovering and trajectory tracking tasks, achieving greater stability, adaptability, and efficiency compared to the PIV controller. The data-driven approach exhibits faster convergence and enhanced robustness in maintaining position and following predefined trajectories under dynamic conditions. Additionally, the system demonstrates strong resilience to environmental disturbances. These findings underscore the potential of neural network-based controllers as a transformative solution for UAV operations, marking a significant advancement in the autonomy, reliability, and intelligence of modern aerial systems.

9:30 A Trust-Based Blockchain Framework for Mitigating Smartphone Theft Incidents

Suresh Babu Erukala (Assistant Professor, India); Banoth Krishna Mohan Naik (National Institution of Technology, Warangal, India); Aswani Devi Aguru (SRM University AP, India); Erukala Sudarshan (Sumathi Reddy Institute of Technology for Women (SRITW), India)

In today's digital age, mobile phones are vital for communication, navigation, and productivity, but their growing role as data repositories makes them prime targets for theft, leading to financial loss, privacy breaches, and operational disruptions. Traditional prevention methods often lack robust data protection and are vulnerable to unauthorized access. To address these challenges, this paper proposes a blockchain based mobile theft detection system using Hyper ledger Fabric, a permissioned blockchain platform. The system assigns each mobile device a unique identifier (UID) upon registration and securely stores metadata, such as location and status, on the blockchain. In case of theft, the owner can report the incident, triggering smart contracts to update the device's status and notify network participants in real time. This decentralized approach ensures that stolen devices are flagged and blocked across participating networks. Additionally, the system supports secure ownership transfer, reducing fraud and enhancing trust in the mobile ecosystem.

Thursday, October 30 8:00 - 9:30

Track C6F6 CCI 6.2: Computing & Computational Intelligence (CCI) 6.2

Room: F6. 505 Sepilok

Chair: P. Susthitha Menon (Universiti Kebangsaan Malaysia, Malaysia & Institute of Microengineering and Nanoelectronics (IMEN), Malaysia)

8:00 EDSGAN: an Edge-Informed Generative Adversarial Network for Enhanced Perceptual Quality Single Image Super-Resolution

Masuma Aktar (National Institute of Technology, Silchar, India); Kuldeep Singh Yadav (NIT Silchar, Assam, India); Rabul Hussain Laskar (NIT SILCHAR, India)

Single Image Super-Resolution (SISR) faces a persistent challenge in reconstructing high-frequency edge details, which are paramount for human perceptual quality. While Generative Adversarial Networks (GANs) have significantly advanced SISR, they often struggle to generate truly sharp and realistic edges, often due to their inherent loss functions. To address this critical limitation, we propose an Edge-Informed Super-Resolution GAN (EDSGAN). EDSGAN employs a dual-path edge-informed discriminator that simultaneously analyzes image content and edge maps, enabling more effective discrimination of realistic versus artifact-prone edge structures. Concurrently, an innovative edge-aware loss guides the generator towards reconstructing perceptually sharper and more accurate edges. Extensive experiments on benchmark datasets demonstrate EDSGAN's superior perceptual quality. Notably, on the Set14 dataset, EDSGAN achieves a remarkable improvement of 9% in LPIPS (from 0.1329 to 0.121) and 8% in PI (from 2.9261 to 2.689) over the ESRGAN baseline. Our method effectively strikes an optimal balance between visual realism and pixel-level accuracy.

8:15 Deep Learning Based Tree Counting Method for Various Plant Density in UAV-Captured Palm Oil Plantation Images

Eisyatul Hannie Binti Mohammad Rushdan (University Putra Malaysia, Malaysia); Syamsiah Mashohor and Khairunniza Bejo (Universiti Putra Malaysia, Malaysia)

Oil palm is a globally significant economic crop, especially in Malaysia and Indonesia, where it contributes substantially to national economies. Accurate monitoring of plantations, particularly tree count and health, is essential for effective management and yield estimation. However, traditional field-based methods are costly and labor-intensive, and while UAVs and very high-resolution satellite imagery offer precision, they are often limited by high cost, limited coverage, and technical constraints such as altitude variation and image mosaicking. This study proposes a scalable and cost-effective pipeline that utilizes UAV imagery implementing two tree-counting approaches Template Matching, serving as a classical baseline, and YOLOv8, a deep learning object detection model chosen for its high accuracy and inference speed. The oil palm tree density was classified into High, Medium, and Low category for easy result observation. Preliminary results demonstrate promising True Positive Rate (TPR), False Negative Rate (FNR), and Estimation Error (EE) values, with YOLOv8 achieving its best performance in medium-density regions (TPR: 109.54%, FNR: 9.50%, EE: 12.91%) and Template Matching showing the weakest performance in low-density areas (TPR: 305.45%, FNR: 205.45%, EE: 205.45%), indicating that the proposed approach offers a practical, real-time, and resource-efficient solution for large-scale oil palm monitoring, supporting sustainable plantation management.

8:30 TigerNav: Development of a Virtual Assistant Using an Autoregressive Model for Indoor Navigation

Ralph Alexander N. San Juan, Ernest John Q. Baetiong, Saranggani J. Bantayao Jr, Marc Justin M Mangali, Carl Kristien P Sumo and Ma. Madecheen S Pangaliman (University of Santo Tomas, Philippines)

Indoor navigation systems play a crucial role in guiding users through complex environments such as airports, shopping malls, hospitals, and university campuses. With the advancement of Artificial Intelligence (AI), researchers have explored dialogue-based approaches to enhance user interaction within these systems. However, existing models face limitations in accurately processing diverse user inputs, which poses challenges in efficiency and usability. To address this, the researchers developed a virtual assistant using a Large Language Model (LLM) to facilitate a dialogue-driven navigation experience. The study was conducted in three phases: (1) generation of datasets, (2) development of models, and (3) virtual assistant performance assessment through a user satisfaction survey. The dataset, consisting of 200,000 entries, was split into 70% for training and 30% for testing, designed to train the navigation model on real-world scenarios. Due to hardware limitations, specifically the 4GB of VRAM on NVIDIA GeForce GTX 1650 and RTX 3050 GPUs, the GPT-2 model was selected as the base model. Despite being an outdated and less capable model for handling complex language tasks, GPT-2 was chosen due to its compatibility with limited hardware resources, such as GPUs with only 4GB of VRAM. Among the training methods tested, the General Purpose Trainer achieved the best performance, with a BERT score of 0.89, a METEOR score of 0.84, and an average perplexity of 2.82, indicating moderate success in understanding and responding to user input. A user satisfaction survey further validated the practicality of the system, with an average rating of 87.44%. Although the model shows promise, its effectiveness in interpreting highly unstructured or unconventional queries could be improved using more advanced LLMs.

8:45 Pestaway: a Pest Management Chatbot with Integrated Speech Capability

Rachel Hannah C. Dela Cruz, Joshua Carlo C Aguarin, Nickolas Chase P Ling, Maveric S. Magsaysay, Jastin Brylle C Villanueva and Ma. Madecheen S Pangaliman (University of Santo Tomas, Philippines)

Farmers faced challenges with pest control, leading to lower crop yields and financial losses due to limited access to accurate information. To address this, the study introduced Pestaway, an AI-powered chatbot for Filipino farmers that uses Large Language Models (LLMs) to provide interactive and speech-enabled solutions. A data set of 80,000 pairs of questions and answers, equally divided between English and Tagalog, was generated using GPT-4 and credible agricultural sources. The models were fine-tuned using different training approaches: Supervised Fine-Tuning (SFT), SFT with Direct Preference Optimization (DPO), and Odds Ratio Preference Optimization (ORPO). Among the evaluated trainers, the ORPO Trainer produced the best results, achieving a METEOR score of 0.3271, a BERTScore of 0.7316, and an average perplexity of 1.5185. Additionally, the metrics word error rate (WER) and character error rate (CER) were used to evaluate the speech recognition capabilities of the chatbot. The WER ranged from 0% to 28.57%, which showed that some transcriptions exceeded the acceptable threshold, while the CER ranged from 0% to 3.43%, which remained low across tests, indicating more accurate transcriptions due to minimal character-level errors. These findings demonstrated that the chatbot was able to recognize voice queries in English and Tagalog with reliable precision. Furthermore, a user satisfaction survey involving farmers, agricultural students, and individuals interested in agricultural practices yielded an average rating of more than 4.1 out of 5, with 97.73% giving positive feedback and 100% recommending the chatbot for use. The participants highlighted the usefulness, precision, and bilingual capability of the chatbot and suggested additional enhancements such as faster responses, visual aids, and a mobile version.

9:00 Enhancing Software Refactoring Prediction Accuracy Through Feature Selection and Data Sampling Strategies

Aditya Kumar Singh Bisht (NIT, Kurukshetra, India); Lov Kumar and Vikram Singh (National Institute of Technology, Kurukshetra, India)

Accurate classification and prediction, especially in critical software engineering tasks like software refactoring, often face significant challenges due to the messy nature of real-world data: high dimensionality, irrelevant noise, and wildly imbalanced categories. This study introduces a robust and practical framework designed to mitigate these issues. We thoroughly examine how advanced feature selection methodologies, including statistical tests, correlation filters, and Principal Component Analysis (PCA), synergize with the Synthetic Minority Over-sampling Technique (SMOTE) for data balancing. Our approach involves systematically applying these preprocessing steps to 30 distinct real-world datasets, followed by evaluation with different and diverse machine learning models. Our findings are clear: SMOTE consistently and significantly boosts the reliability and performance of our models, particularly in identifying crucial minority cases, which is vital for effective refactoring prediction. Moreover, intelligent feature selection proves instrumental in optimizing performance, frequently leading to simpler models without compromising accuracy. Ultimately, Random Forest, Support Vector Machines, and Neural Networks emerged as the consistent top-performing machine learning models. This research offers practical, empirically proven strategies for building highly accurate, efficient, and stable predictive models, essential for modern software engineering and refactoring efforts.

9:15 Illegitimate Data Flow Detection of Data-Driven Applications Using Dependency Graph

Anwesha Kashyap (Indian Institute of Information Technology, Guwahati, India); Angshuman Jana (IIIT Guwahati, India)

Detecting potential data breaches in software systems can be effectively achieved through language-based information flow security analysis. Significant risk to confidential data, potentially leading to security breaches, is posed because of poor coding practices in database applications. To tackle the issue, our paper presents a dependency graph-based approach that identifies potential confidentiality leaks in software code. For a variety of software engineering tasks, including information flow security analysis, debugging, code optimization, enabling code reuse, and enhancing program comprehension, Dependency information plays an essential role. Existing dependency information techniques fail to generate precise results. Through the utilisation of syntax-based dependency analysis, we enhance and improvise on existing techniques by introducing Indirect DD-Dependencies, which can result in indirect information leakage. A precise dependency information, which improves the accuracy of security analysis results, is provided through this refinement. We provide experimental results on benchmark codes to validate our approach. To determine illegal information flows, we use propositional formulae for the security-level truth assignments. Thorough utilisation of such an approach aids in bridging the gap between existing approaches and future applications to handle the same.

Thursday, October 30 8:00 - 9:30

Track C6F7 ETS 6: Engineering Technologies & Society (ETS) 6

Room: F7. 506 Selingan

Chair: Mohd Suffian bin Misaran Status (Universiti Malaysia Sabah, Malaysia)

8:00 Ethical Design Framework for Enhancing Accessibility in Graphic Platforms for Colorblind Users

Michael P Camacho and John Heland Jasper Ortega (FEU Institute of Technology, Philippines)

Color is a foundational element in digital design, yet individuals with color vision deficiency (CVD) often encounter significant barriers when engaging with mainstream graphic design platforms. This study explores the ethical and practical implications of designing for users with CVD, focusing on the experiences of Filipino creatives in both professional and academic settings. It aims to uncover common usability challenges, evaluate the effectiveness of existing accessibility features, and propose improvements that reflect user needs and promote inclusive design practices. Employing a qualitative, exploratory methodology, the study integrates word cloud analysis and the Diffusion of Innovation theory to interpret user feedback and assess attitudes toward accessible tools such as colorblind-friendly palettes and vision simulators. Results show strong support for built-in, customizable accessibility features, with many users expressing openness to adoption if these tools are integrated seamlessly into their workflows. The study contributes to the discourse on digital inclusion by presenting an ethical design framework that promotes accessibility as a standard, not optional, element in graphic design platforms.

8:15 MySecureMap: a Geolocation-Based Recommender System for Safety and Healthy Environment

Preecha Tangworakitthaworn, Poramet Kaewpradub, Patcharapon Hathaichot and Waritthorn Mahasiripanya (Mahidol University, Thailand)

This research presents the MySecureMap system, a geolocation-based recommender system designed for real-time air quality visualization and safety-focused navigation. The system leverages the Air Quality Index (AQI) data provided by both government and private sources. The proposed mechanism has integrated the Long Short-Term Memory (LSTM) neural network's machine learning technique which is capable of predicting the AQI levels and providing the route recommendations. The ultimate goal of the proposed system aims to protect users from the hazardous air pollutants by automatically identifying the risk zones and suggesting the safe routes which are visualized on the map. This paper discusses the proposed approach and the system architecture, the design and the development of the proposed MySecureMap system, and the performance evaluation, highlighting its usability and effectiveness in enhancing the environmental awareness and public health. The performance evaluation results shown that the proposed system met the acceptable accuracy with Mean absolute percent error (MAPE) of 2.39 % when comparing the predicted forecast value with the real AQI data observed for the next 12 hours.

8:30 Historical Ice Velocity Mapping Techniques: Assessing Long-Term Stability of the Prince Harald System

Liang Tang, Gang Qiao, Rongxing Li, Guojun Li and Shi Li (Tongji University, China)

Long-term ice dynamics of the Prince Harald system in Lützow-Holm Bay (LHB), East Antarctica, particularly before 1990, remain poorly investigated, hindering assessments of its historical contribution to regional mass balance. Here, we reconstructed high-resolution ice velocity fields (1973-1989) for its main components - Prince Harald 1, Prince Harald 2, and Prince Harald 3 - leveraging historical Landsat imagery with a robust photogrammetric method, a systematic framework that includes geometric correction, orthorectification, and hierarchical feature matching. Our integrated analysis, combining these velocities with assessments of basal melt, surface elevation change, ice front dynamics, and Passive Shelf Ice (PSI) behavior, reveals a stable state of the system. We find that this stability was characterized by consistent flow velocities, persistent surface thickening, minimal basal melt, and calving events predominantly within the PSI zone. These results indicate an overall trend of stability and net mass accumulation for the Prince Harald system during this period.

8:45 IPBlocks: a Blockchain Ecosystem for Secure IP Registration and Decentralized Marketplace

Sadia Ahmmed, S M Jishanul Islam and Sahid Hossain Mustakim (United International University, Bangladesh); Ridwan Arefin Islam (McGill University, Canada); Subangkar Karmaker Shanto (Purdue University, USA); Salekul Islam (North South University, Bangladesh)

Intellectual properties (IPs) are crucial intangible assets in today's innovator economy. However, their management suffers from bureaucratic inefficiencies and vulnerability to mismanagement. We introduce IPBlocks, a blockchain-based solution that streamlines IP applications, trading, and royalty transfers through a decentralized marketplace. We develop a set of algorithms that allow users to apply for, publish, auction, and transfer IPs. These processes are designed to ensure enhanced security and transparency. The algorithms are implemented through Solidity on the Ethereum platform. These algorithms exist in a smart contract that is deployed on a blockchain network to interface with a user-friendly web application. Large files associated with IPs are stored in a decentralized file storage system. Our performance analysis demonstrates the system's feasibility and scalability for large user bases, testing the performance in local and deployed settings. IPBlocks aims to revolutionize IP ownership and management by reducing processing times and improving royalty distribution. We open-source our codebase to the community to promote collaborative development and transparency.

9:00 Assessing Migraine Severity in University Students Using Low-Channel Wearable EEG

Lee Fan Fan, Huei Qhi Teoh, Hou Kit Mun and Kiruthika Selvakumar (Universiti Tunku Abdul Rahman, Malaysia)

Migraine is a prevalent neurological disorder characterized by episodic headaches and altered cortical activity, often impacting quality of life. Although advanced neuroimaging has enhanced understanding of migraine-related brain changes, these methods are often costly and inaccessible for routine use. This study investigates the feasibility of using a low-channel, wearable electroencephalography (EEG) device to assess migraine severity in university students. Fifty-three participants were stratified by migraine-related disability using the Migraine Disability Assessment (MIDAS). Resting-state EEG data were recorded with the Muse 2 headband and processed via EEGLAB in MATLAB. Power spectral density (PSD) analysis focused on alpha (8-12 Hz) and beta (13-30 Hz) bands at frontal and temporal sites. Participants also completed the Migraine-Specific Quality of Life Questionnaire (MSQv2.1). Although no statistically significant differences were found across MIDAS groups, higher disability levels showed a trend toward reduced alpha and beta power, suggesting cortical hypoactivation consistent with prior migraine research. Quality of life scores did not significantly differ among groups, indicating that migraine severity may not directly correspond to self-perceived well-being. These findings align with previous studies and support the potential utility of consumer-grade wearable EEG devices for non-invasive assessment of migraine severity.

9:15 Characterization of a Solar Rooftop Greenhouse Drying System for Food Applications

Lew Andrew Tria (University of the Philippines - Diliman, Philippines); Kyle Lemuel S Juliano (University of the Philippines Diliman, Philippines)

In the Philippines, especially in urban areas like Quezon City, food waste has become a significant concern. Agricultural innovations such as solar greenhouse dryers have been adapted to address this issue. On the other hand, installation of grid-tied photovoltaic systems in building rooftops in urban areas have been increasing. Merging these two concepts to design an urban solar rooftop greenhouse dryer is proposed to address the two issues of energy production and food wastage mitigation. This work investigated the drying capability of an urban rooftop solar greenhouse drying system and its effect on the output power of solar modules. Results showed that the proposed system is technically viable achieving an average temperature and humidity reduction of 4.25°C and 1.75%, respectively, resulting to average crops weight loss of 90.85%, relatively similar to a regular greenhouse dryer. In addition, energy generation of solar modules in the proposed setup decreased by 9.6% only with respect to a usual grid-tie system.

Thursday, October 30 8:00 - 9:30

Track C6F8 CCI 6.3: Computing & Computational Intelligence (CCI) 6.3

Room: F8. 507 Monsopiad

Chair: Mohamad Yusoff Alias (Multimedia University, Malaysia)

8:00 Length-of-Stay Prediction with Data Fusion and Masked Language Modeling

Hieu Ngo (Ho Chi Minh City University of Technology, VNUHCMC, Vietnam); Chau Vo (HCMUT, Vietnam)

Length of Stay (LOS) prediction plays a pivotal role in efficient healthcare resource management, optimization of care quality, and reduction of treatment costs. One of the key challenges in this domain is the complexity of processing unstructured long clinical notes containing sensitive information. Moreover, limitations persist in effectively processing long texts and integrating multiple information sources. This paper therefore proposes a method integrating two medical data sources: structured demographic and admission data, unstructured long clinical notes. Both can be then transformed into a unified text input. This data combination simultaneously leverages quantitative information from tabular data (demographic and initial admission information) and rich supplementary information from clinical notes (medical history, detailed symptoms, etc.), providing a more comprehensive picture of patient condition and improving prediction performance. Additionally, the paper proposes applying masked language modeling to enhance the ModernBERT model to create more effective general representations of entire patient texts compared to traditional approaches that rely solely on BERT. Evaluated on MIMIC-III data, our resulting Ad-ModernBERT model can yield better LOS predictions consistently in many experiments.

8:15 Real Time Sign Language Recognition Using MediaPipe and Random Forest Classifier

Ajay Kumar Naik G, Imon Kumar Biswas and Venkata Rama Rao Chevula (National Institute of Technology Warangal, India)

Deaf individuals typically use Sign Language (SL), which involves the use of hand and finger motions, to engage in communication with others. However, when interacting with hearing individuals who may not be familiar with SL, there is often a notable challenge in translating these gestures. To address this communication barrier, various research efforts have focused on employing computer vision techniques and sensor data for recognizing hand movements. This paper presents the development of a Sign Language Recognition (SLR) system utilizing a computer vision-based approach. Here, the data is represented in the form of coordinates extracted using Google MediaPipe from individual frames of video. The extracted features through this representation are fed to the Classifier. Here, the random forest machine learning algorithm is used as a classifier. The experimental results reveal that the Sign Language Recognition (SLR) system using Random Forest Classifier (RFC) achieved an accuracy of 99.777%. This performance is superior to that of other considered machine learning algorithms.

8:30 Disease Classification and Report Generation from Chest X-Ray Images Using SigLIP and LLaVA

Kamalnath M S, Harsh Kumar and Jiji Victor Charangatt (Shiv Nadar University Chennai, India)

In real-world scenarios, radiologists often face the challenge of interpreting numerous medical images, emphasizing the need for scalable and effective automated medical image report generation systems. The goal of Artificial Intelligence (AI)-based report generation is to produce clinically accurate and coherent descriptions from medical images, which can help alleviate the workload involved in traditional radiology reporting. In this paper, we present a novel methodology for multi-label disease classification and automated report generation from chest X-ray images. As the first step, our method utilizes the Sigmoid loss for Language-Image Pre-training (SigLIP) model which utilizes the sigmoid loss to predict relevant medical labels based on image features. These predicted labels guide the report generation process where we employ the Large Language and Vision Assistant (LLaVA) model for generating comprehensive radiology reports, taking advantage of its vision-language capabilities to produce more contextual and descriptive outputs. We assess our unified multi-label classification and report generation framework on the MIMIC-CXR dataset using established evaluation metrics. Experimental results demonstrate that our approach exceeds the performance of many existing state-of-the-art methods on multiple evaluation criteria.

8:45 Powerlist-Based High-Level Programming Model for Parallelism

Jeya Ganesh M R and Anshu S Anand (Indian Institute of Information Technology Allahabad, India)

Designing efficient parallel programs is a non-trivial task that requires one to have an in-depth knowledge of the underlying parallel architecture, memory hierarchy and programming model besides other aspects. This may require considerable efforts, time, and cost. To improve the programmers' productivity, there is a need for parallel abstractions that can simplify the programmers job of first specifying the computation and then optimizing it for performance. Powerlist [1] is one such potential parallel abstraction that is able to exploit both parallelism and recursion at the same time, resulting in succinct representations of computations. In this work, we propose the use of Powerlist as a Domain Specific Language (DSL) and implement it as a Source-to-Source compiler that translates the high-level powerlist specification into sequential and parallel C++ programs. Several new powerlist specifications were also devised for computations and in this process, the powerlist notation was also enriched. The proposed framework considerably improves the programmers' productivity by providing a high-level abstraction resulting in succinct and intuitive specification of computations, absolving them of the task of exploiting parallelism and tuning for performance, which is taken care of by the implementation.

9:00 AI Detection and Watermarking: an Advanced Deep Learning Framework for Real-Time Content Authentication and Digital Fraud Prevention

Aneesh Narayan Bandaru (IIIT Naya Raipur, India); Karthikeya Prachodhan Mudumba (IIIT Naya raipur, India); Mallikharjuna Rao K (IIIT Naya Raipur, India)

The emergence of large language models (LLMs) has transformed the landscape of digital content generation, enabling the creation of human-like text at scale. However, this capability has introduced profound challenges in verifying content authenticity and combating misinformation. As AI-generated content becomes increasingly indistinguishable from human-authored text, existing detection tools struggle to maintain accuracy, especially when confronted with adversarially paraphrased or subtly modified outputs. In response to these challenges, this study proposes a dual-layered framework that combines a CNN-LSTM hybrid deep learning model for AI content detection with robust watermarking mechanisms for authentication. The detection model is trained on a rich dataset comprising human and AI-generated sentences across diverse linguistic patterns. Simultaneously, watermarking techniques, token-level, logit-wise, and frequency-based, are applied to embed statistical fingerprints in AI-generated text to support traceability. Evaluations demonstrate high detection accuracy, reduced false positives, and watermark resilience even under adversarial transformations. The proposed approach contributes toward building a reliable and scalable solution for ethical AI content regulation and digital traceability.

9:15 gptPromptFuzz: LLM Prompt Engineering-Based Seed Generation for Effective Fuzzing

Darshan Lohiya (National Institute of Technology Warangal, India); Vivek Yelleti (SRM University AP, India); Sangharatna Godboley (National Institute of Technology Warangal, India); Pisipati Radha Krishna (National Institute of Technology, Warangal, India)

Fuzz testing is one of the popular techniques for evaluating software reliability. Its effectiveness largely depends on the quality and diversity of the initial seed inputs. Traditionally, these seeds are generated randomly, which may limit the effectiveness of the fuzzing process. However, generating seeds based on an analysis of the target code can significantly improve the performance of these tools. To address this, we proposed a Large Language Model (LLM)-based seed generation approach for effective fuzzing and named it gptPromptFuzz. In our approach, initially, one meta-prompt is designed in accordance with the objective of diverse seed generation. To further enhance diversity, we construct ten additional prompts that are semantically equivalent to the meta-prompt. Each of these prompts is independently processed by LLM to produce unique seeds. The experimental results demonstrated that the proposed gptPromptFuzz outperformed random AFL in generating seeds effectively, with reduced execution time, in all 45 benchmark C programs. Further, a larger number of paths are obtained in 41 out of 45 programs.

Thursday, October 30 11:30 - 1:00

Track C7F1 CCI 7.1: Computing & Computational Intelligence (CCI) 7.1

Room: F1. Sipadan I

Chair: Sanjaya Kumar Panda (National Institute of Technology Warangal & NITW Techsammelan Private Limited, India)

11:30 Adaptive Feature Aggregation Enhanced by Using DenseNet for Robust Breast Cancer Histopathology Image Classification

Zaka Ur Rehman (Malaysia); Mohammad Faizal Ahmad Fauzi, Wan Noorshahida Mohd-Isa and Meriem Touhami (Multimedia University, Malaysia); Arbab Sufyan Wadood (Multimedia University, Malaysia & BUITEMS, Pakistan); Muhammad Kashif Jabbar (Shenzen University, China)

Accurate classification of breast cancer from histopathology images is critical for early diagnosis and effective treatment planning. While deep learning techniques-particularly convolutional neural networks (CNNs)-have achieved substantial success in medical image analysis, existing models often struggle with issues such as overfitting, channel redundancy, and insufficient focus on clinically salient features. To address these limitations, this paper introduces a deep learning framework that integrates an Adaptive Feature Aggregation (AFA) block into the DenseNet121 architecture. The proposed AFA module learns to emphasize important feature channels by modeling inter-channel dependencies through a lightweight attention mechanism, thereby improving the network's ability to distinguish between benign and malignant tissue patterns. Extensive experiments were conducted on the BreakHis 400x histopathology dataset. The proposed model achieved 98% accuracy, 98% F1-score, and an AUC of 0.99, outperforming the baseline DenseNet model. Evaluation metrics such as precision, recall, ROC-AUC, and confusion matrix analysis confirm the robustness and effectiveness of the proposed method for breast cancer classification.

11:45 Enhancing Mental Health Disorder Classification: a Decision Tree-Based Approach with Optimized Feature Engineering, Data Balancing, and Hyperparameter Tuning

Md. Arifur Rahman Akib, Jannatul Ferdous and Fariah Mahzabeen (North South University, Bangladesh); Riasat Khan (North South University, Bangladesh & New Mexico State University, USA)

Recent advancements in natural language processing (NLP) have employed transformer-based models to classify mental health conditions using social media data. Nevertheless, these models often face challenges with complex feature dependencies, require substantial computational power, and may not always yield optimal results. In contrast, conventional machine learning models can attain similar or even better performance by utilizing effective feature engineering, balancing data, and optimizing hyperparameters. This study addresses an important gap by demonstrating that a properly optimized Decision Tree model can greatly improve classification accuracy, surpassing deep learning methods such as BERT and BiLSTM. By utilizing Term Frequency-Inverse Document Frequency (TF-IDF), extracting N-gram features, and applying the Synthetic Minority Over-sampling Technique (SMOTE) for balancing classes, we improve the accuracy of predictions. Our Decision Tree model shows a 16% increase in accuracy compared to transformer-based models, demonstrating that a well-tuned conventional machine learning method can compete with or exceed deep learning methods which have more computational cost.

12:00 M-MedNeRF: 3D Modeling and Novel View Synthesis from Single-View X-Rays Using Mamba-Accelerated Neural Radiance Fields

Lohith Saradhi Kandukuri and Jiji Victor Charangatt (Shiv Nadar University Chennai, India)

Reconstructing 3D medical images from single-view X-rays offers a low-risk, low-cost alternative to traditional 3D reconstruction methods that use multiple CT or MRI scans. But current methods often face limitations in computational efficiency and visual fidelity. In this paper, we extend the state-of-the-art GAN-based novel view generation model, MedNeRF, by proposing M-MedNeRF, which integrates the Mamba architecture-a novel state-space model optimized for long-range sequence modeling with linear time complexity -into the volumetric rendering pipeline. The generator of the model, samples points in 3D space to train a Deep Neural Network to predict view for a given Camera position, which is passed on to a discriminator to evaluate the generator's performance. The proposed M-MedNeRF model captures spatial dependencies more effectively, modeling ray sequences as a whole rather than independently. We demonstrate that this architecture outperforms the baseline in SSIM and LPIPS metrics, with notable qualitative improvements in anatomical reconstruction. These results highlight the potential of fast and efficient sequence modeling in advancing single-view 3D medical image reconstruction.

12:15 Visualization of the Contributions of Frequency-Domain Features to Person Identification in Motor Imagery EEG

Yuki Arai, Tadanori Fukami and Chako Takahashi (Yamagata University, Japan)

When classifying a person's state or emotions from brain activity, individual differences in electroencephalography (EEG) signals pose a key issue. If those features of motor imagery EEG that contribute to improved accuracy in person identification can be identified, they may provide insights into the nature of individual variability in EEG data. In this study, we used the frequency-domain feature attribution method proposed by Tachikawa et al. to calculate and visualize the contribution of EEG frequency bands to person identification performance. We trained a person identification classifier on motor imagery EEG datasets, which achieved approximately 80% accuracy. For this classifier, we calculated the contributions of frequency-domain features. We observed that both the amplitude and phase exhibited highly contributive frequency bands in the low-frequency range. Furthermore, in the case of amplitude, the high-contribution bands were distributed over a broader frequency range, suggesting that individual differences may be reflected differently in EEG amplitude and phase information.

12:30 Explainable AI for Breast Cancer Diagnosis Using EfficientNetB3 with Attention Mechanism

Md Serajun Nabi, Mohammad Faizal Ahmad Fauzi and Hezerul Abdul Karim (Multimedia University, Malaysia); Tong Boon Tang (Universiti Teknologi PETRONAS, Malaysia); Normy Abdul Razak (Universiti Tenaga Nasional, Malaysia); Hasanul Bannah (Multimedia University, Malaysia)

Accurate classification of HER2 immunohistochemistry (IHC) scores is essential for determining effective breast cancer treatment, yet it remains challenging due to subjective manual interpretation, especially for borderline scores (1+ and 2+). This study proposes an interpretable deep learning framework that combines EfficientNetB3 with a Convolutional Block Attention Module (CBAM) to strengthen feature extraction and attention to regions of interest that are diagnostically significant. To facilitate clinical trust, explainable AI (XAI) is performed using Gradient-weighted Class Activation Mapping (Grad-CAM). Evaluated on a HER2-IHC-40x-WSI dataset of 10,997 image patches distributed over four HER2 classes (0, 1+, 2+, 3+), the proposed model achieved an overall accuracy of 0.96% and a macro-averaged F1-score of 0.93%, demonstrating strong performance, particularly in borderline cases. The system also demonstrates robustness across varied samples, highlighting its generalization capability. These results highlight the potential of applying attention mechanisms with explainable AI for stable and interpretable HER2 IHC scoring in digital pathology.

12:45 BitRelation: Exploring Bit-Level Dependencies in Neural Cryptanalysis

Yue-Tian Goi, Shu-Min Leong and Raphael C.W. Phan (Monash University, Malaysia Campus, Malaysia); Ana Sălăgean (Loughborough, United Kingdom (Great Britain)); Shangqi Lai (CSIRO Data61, Australia); Wei Chuen Yau (Xiamen University Malaysia, Malaysia)

This paper applies Explainable Artificial Intelligence (XAI) to improve the interpretability of neural differential cryptanalysis on the SPECK cipher. We use Local Interpretable Model-agnostic Explanations (LIME) to analyse and visualise feature importance in neural distinguishers, giving signed contributions and absolute rankings. Signed contributions show whether, and how strongly, specific bit positions influence the model's decision, while absolute rankings reflect their importance regardless of sign. To study interactions beyond single bits, we introduce a Systematic Masking Approach to reveal relations among bits by testing if chosen combinations of masked bits alter classification accuracy. On Gohr's 8-round SPECK32/64 distinguisher, masking up to four-bit combinations shows that decisions involve multi-bit interactions rather than isolated single-bit effects. Although LIME highlights strong single-bit signals, masking reveals interaction patterns consistent with differential cryptanalysis. These findings clarify model behaviour in neural cryptanalysis and show XAI's value for exposing and visualising interaction structure in ciphertext features and decisions.

Thursday, October 30 11:30 - 1:00

Track C7F2 ECD 7.1: Electronics, Circuits & Devices (ECD) 7.1

Room: F2. 501 Kadamaian

Chair: Ismail Saad (Universiti Malaysia Sabah, Malaysia)

11:30 Development of a Strap-Based Active Back-Support Exoskeleton for Static Postural Sway Correction

Jun Han Lee and Yu Zheng Chong (Universiti Tunku Abdul Rahman, Malaysia); Siow Cheng Chan (University Tunku Abdul Rahman, Malaysia)

Postural sway control has traditionally been targeted through lower-limb or ankle-based interventions, especially critical for individuals with chronic low back pain (CLBP), older adults, and those with neurological disorders. This paper introduces a strap-based soft active back-support exoskeleton that applies corrective torque directly across the trunk and shoulders using pneumatic actuation. Trunk sway is detected via an inertial measurement unit (IMU) mounted on the sternum via nylon straps, with corrective action triggered through a lightweight ESP32-based threshold control algorithm. Fifteen healthy adults were evaluated across four static stance conditions, with the Tandem Stance Eyes Closed (TSEC) posture representing the greatest challenge to balance. When the exosuit was active, the centre of pressure (CoP) pathlength decreased by 51.5%, while RMS sEMG activity was reduced by 51.2% in the rectus abdominis, 38.3% in the external oblique, and 41.8% in the erector spinae. These results highlight the feasibility of a trunk-focused active exosuit for improving static balance control, offering a complementary alternative to conventional ankle or limb-based corrective strategies. These findings suggest the exosuit's potential as a practical tool for assisting static balance.

11:45 An FPGA Based Accelerated Perception Sub-System

Sreehari N, Naghulram S P, Sahil Ahamed S, Shreyas Srinivas A and Yamuna B (Amrita Vishwa Vidyapeetham, India); Karthi Balasubramanian (Amrita Vishwa Vidyapeetham & Amrita School of Engineering Coimbatore, India)

This paper outlines an FPGA-accelerated perception sub-system for real-time object detection employing the You Only Look Once (YOLO) v5 model. The system uses the Deep Learning Processing Unit (DPU) B4096 implemented on the Xilinx Kria KV260 platform for efficient deep learning inference. The implementation shows a 7.5× speedup over traditional CPU solutions, achieving up to 59.94 Frames Per Second (FPS) for the YOLOv5 Nano model and 17.4 FPS for the YOLOv5 Large model. The system further shows high detection accuracy with a mean Average Precision (mAP) of 0.76 and an average Intersection over Union (IoU) of 0.72 for the intelligent traffic perception system. The implementation incorporates optimized data pipelines, quantized models, and high throughput inference methodologies within the Kria Docker environment, utilizing OpenCV, Xilinx Runtime(XRT), and Vitis AI Runtime (VART). The results highlight that an effective hardware-software co-design approach, combining high throughput, optimal resource utilization, and reliable inference accuracy, significantly enhances the performance of FPGA-based AI (Artificial intelligence) workloads in applications such as surveillance, industrial automation, and production line safety.

12:00 A Novel 5-Bit Ascon s-Box with Multiplexer for FPGA Implementation

Muhammad Hafiz Abu Hassan (Universiti Malaysia Perlis (UniMAP), Malaysia); Rizalafande Che Ismail and Siti Zarina Md Naziri (Universiti Malaysia Perlis, Malaysia)

The explosive growth of the Internet of Things (IoT) has ushered in an era of smart, interconnected devices yet with it comes an urgent need for robust and efficient security. IoT devices often operate under severe constraints, including limited processing power, memory, and energy resources making traditional cryptographic solutions impractical. This has fueled the rise of lightweight cryptography (LWC), a new frontier of algorithms tailored to deliver strong security without compromising performance. Among the standout candidates, Ascon has emerged as the primary recommendation for lightweight authenticated encryption in the final round of the prestigious CAESAR competition (Competition for Authenticated Encryption: Security, Applicability, and Robustness). At the heart of Ascon lies a complex permutation process involving round constants, substitution boxes (S-boxes), and a linear diffusion layer components that demand optimized hardware design. This paper introduces novel S-box implementation techniques for FPGA platforms, pushing the boundaries of existing 5-bit hardware designs. Our approach not only improves efficiency but also secure, high-performance cryptographic systems in the age of pervasive IoT.

12:15 An Energy-Efficient 8T SRAM Cell with Self-Regulating Intramural Loop for Leakage Suppression

Shaik Lal John Basha (VIT-AP University, Amaravati, Andhra Pradesh, India); Avishkar Kant (VIT-AP University Amaravati Andhra Pradesh, India); Atul Shankar Mani Tripathi (VIT-AP University, India)

In contemporary VLSI design, memory blocks have emerged as dominant contributors to both silicon area and overall power consumption. As a result, the development of energy constrained memory architectures is critical to meet the demands of energy-efficient design. Among the available memory technologies, Static Random-Access Memory (SRAM) continues to be the preferred choice for System-on-Chip (SoC) implementations, primarily due to its high-speed operation and compatibility with traditional CMOS integration. However, aggressive scaling of MOSFET devices introduces significant challenges, particularly in the form of elevated leakage currents. These arise from factors such as reduced channel lengths, thinner gate oxides, and lower threshold voltages. These factors adversely impact the reliability and energy efficiency of contemporary systems. To address these challenges, this paper proposes a 8-transistor (8T) memory architecture that incorporates a novel Intramural Loop mechanism. This innovative approach introduces an internal, self adaptive and regulating feedback structure dynamically adjusts the biasing of internal nodes based on stored logic state. The technique operates autonomously, without requiring additional control lines or incurring area overhead, and is specifically tailored to suppress subthreshold, gate, and junction leakage currents. According to simulation studies using 45nm CMOS technology, the proposed configuration achieves a leakage power reduction of up to 99.84% at subthreshold voltages. On average, the cell demonstrates more than 90% leakage reduction across a wide voltage range. These results affirm the potential of the proposed memory architecture for ultra-low power applications such as wearable electronics, biomedical implants, and Internet of Things (IoT) devices.

12:30 Impedance Matching Technique for Dual-Band Radio Frequency Energy Harvesting Unit Utilizing 900MHz (UHF) and 2.45GHz (Wi-Fi) Frequency Band

Jefferson A. Hora (MSU-Iligan Institute of Technology, Philippines); Xi Zhu (University of Technology Sydney, Australia); Marjie Anne Thezza S. Teleron (Mindanao State University - Iligan Institute of Technology, Philippines)

This study presents a dual-band impedance matching technique designed for wireless power transfer (WPT) applications operating at 900 MHz (UHF/GSM) and 2.45 GHz (Wi-Fi/ISM) frequencies. The proposed method employs a bridged-T coil (BTC) network integrated with a single-stub tuning approach to simultaneously match the input impedance at both frequency bands, ensuring efficient power delivery. Fabricated using 65nm CMOS technology, the rectifier circuit demonstrates promising performance metrics. At an input power of -15 dBm, the power conversion efficiency (PCE) reaches 58.01% at 900 MHz and 50.52% at 2.45 GHz. Under higher input power of -3 dBm, the peak PCE improves to 68.57% and 57.44% for 900 MHz and 2.45 GHz, respectively. The design also achieves a dynamic range of 19.04 dB at 2.45 GHz, indicating robust performance across varying input levels. Furthermore, excellent return loss (S11) values are observed, with -34.48 dB and -13.27 dB at 900 MHz, and -11.28 dB and -16.27 dB at 2.45 GHz for GSM and Wi-Fi bands, respectively.

12:45 Multiplexed Biomarker Detection with Dual-Gated Organic Electrochemical Transistors: Toward Prostate Cancer Diagnosis

Ke Meng (The University of Electronic Science and Technology of China, China); Ruyou Zhang (The University of Electronic Science and Technology of China, Malaysia); Jia Zhu and Yuan Lin (University of Electronic Science and Technology of China, China)

Organic electrochemical transistor (OECT) is an ideal sensing platform due to its advantages of effective signal amplification, low working voltage, and ultra-high sensitivity, which has shown great potential in disease diagnosis and monitoring. This study developed an OECT-based aptamer sensor with a dual-gate configuration for real-time biomarker monitoring in biofluids. This innovative biosensor not only has high sensitivity but also the capability of simultaneously detecting two key biomarkers relevant to prostate cancer: prostate-specific antigen (PSA) and vascular endothelial growth factor (VEGF). The dual-gate design effectively promotes device miniaturization and provides multi-analyte detection capability, significantly enhancing the diagnostic accuracy for prostate cancer. The OECT-based aptamer sensor with the dual-gate configuration exhibits an ultra-low detection limit (as low as 100 fg/mL) and a wide linear range (100 fg/mL to 1 μg/mL) towards biomarkers. This work lays a solid foundation for the development of next-generation portable and multifunctional diagnostic tools towards prostate cancer.

Thursday, October 30 11:30 - 1:00

Track C7F3 PES 7: Power, Energy & Electrical Systems (PES) 7

Room: F3. 502 Mesilau

Chair: Chai Chang Yii (Universiti Malaysia Sabah, Malaysia)

11:30 Multi-Timescale Hierarchical Coordination of Market-Based Day-Ahead Dispatch: Practical Applications and Prospects in CSG

Xin Yin (South China University of Technology, China); Haoyong Chen, Prof. Chen (South China University of Technology, China & Universiti Tunku Abdul Rahman, Malaysia); Yiping Chen, He Huang and Yuefeng Lu (China Southern Power Grid, China); Xin Zeng (South China University of Technology, China)

A high proportion of grid-connected renewable energy in power systems represents an economically viable and reliable pathway toward achieving clean and low-carbon power systems. However, the safe and efficient integration of unstable and uncontrollable renewable energy sources remains a significant challenge for power grid operators. Moreover, indiscriminate investment in flexible resources may result in suboptimal resource utilization. Drawing on operational experiences from the power dispatching and controlling center of China Southern Grid (PDCC-CSG), this paper demonstrates how PDCC-CSG mobilizes flexible resources through dispatch mechanisms such as hierarchical coordination, multi-timescale coordination, and "power market plus dispatch" coordination approaches to enable the efficient utilization and consumption of renewable energy in scenarios where new energy accounts for more than 20% of the total power generation. Furthermore, in order to cope with the scenarios with an even higher share of renewable energy, the future application of advanced cooperative and intelligent dispatch technologies is envisioned.

11:45 Development of a Single Phase Shunt Active Power Filter Using Synchronous Reference Frame and Self-Charging Algorithms

Muhammad Ammirrul Atiqi Mohd Zainuri (Universiti Kebangsaan Malaysia, Malaysia); Yushaizad Yusof (Jabatan Kejuruteraan Elektrik, Elektronik & Sistem, Fakulti Kejuruteraan & Alam Bina, UKM, Malaysia); Ahmad Asrul Ibrahim, Nor Azwan Mohamed Kamari, Mohd Hairi Mohd Zaman and Mohd Asyraf Zulkifley (Universiti Kebangsaan Malaysia, Malaysia)

The increasing use of non-linear loads in modern electrical power systems has led to significant power quality concerns, primarily due to the generation of harmonic currents. This study explores the effectiveness of a single-phase Shunt Active Power Filter (SAPF) in mitigating these harmonics. The primary objective is to design a SAPF using advanced harmonic extraction methods and DC-Link capacitor voltage control algorithms to maintain Total Harmonic Distortion (THD) below 5%, in compliance with IEEE standard 519-1992. The system's performance is assessed through simulations in MATLAB/Simulink, under both steady-state and dynamic conditions. A comparative analysis is conducted between the proposed method and the conventional Proportional-Integral (PI) controller. The study highlights the benefits of using the synchronous reference frame (SRF) technique combined with a self-charging mechanism and fuzzy logic controller (FLC). Results show that this approach delivers improved harmonic mitigation, achieving lower THD, faster response time, and reduced overshoot and undershoot compared to traditional methods. These findings suggest that the proposed SAPF configuration offers a promising solution for enhancing power quality in systems affected by non-linear loads, making it suitable for modern industrial and residential applications requiring reliable and efficient harmonic compensation.

12:00 Geospatial Analysis of Biomass Energy Systems: Site Suitability, Energy Yield, and Validation

King Harold A Recto (Ateneo de Manila University, Philippines)

This study investigates the potential of biomass energy as a renewable power source through geospatial analysis and system evaluation. Using Geographic Information Systems (GIS), it assesses the suitability of sites for biomass energy development based on resource availability, land use, and proximity to existing energy infrastructure. The research includes an evaluation of the current energy supply landscape, quantification of annual biomass energy potential from agricultural sources (rice, corn, sugarcane, and coconut), and estimation of projected energy generation. A cost-benefit and sustainability analysis was also performed to examine the environmental, social, and economic implications of deploying biomass energy systems. The validation process ensured data reliability and alignment with practical deployment considerations. Results indicate strong potential for integrating biomass energy into regional power systems, particularly in areas with abundant agricultural waste. However, high capital costs remain a key challenge. The study recommends further investigation into the optimal operating point ("Q-point") for biomass energy within a hybrid renewable energy framework to enhance sustainability, accessibility, and energy security.

12:15 Performance of Transformer Insulation Paper in the Presence of Multi Walled Carbon Nanotube (MWCNT) in Palm Oil Methyl Ester (POME)

Nurul Izzati Hashim (Universiti Malaysia Sarawak, Malaysia); Shirley Anak Rufus (Universiti Malaysia Sarawak (UNIMAS) & Universiti Teknologi Malaysia (UTM), Malaysia); Nazreen Junaidi (Universiti Malaysia Sarawak, Malaysia); Sharifah Masniah Wan Masra (Universiti Malaysia Sarawak (UNIMAS), Malaysia); Yanuar Arief (UNIMAS, Malaysia); Nur Eryshazana Amirulzaki Hassan (Universiti Malaysia Sarawak, Malaysia)

The reliability of power transformers depends greatly on their insulation systems, which traditionally use Mineral Oil (MO) and Kraft paper. Due to environmental concerns with MO, Palm Oil Methyl Ester (POME) has emerged as a sustainable alternative, though it requires performance enhancement for high-voltage use. This study investigates the effect of adding 0.02 g/L Multi-Walled Carbon Nanotubes (MWCNTs) to POME on the mechanical and dielectric properties of Kraft paper, under both unaged and thermally aged conditions. Tensile strength (TS) was evaluated in both machine (MD) and cross directions (CD), while dielectric strength was assessed using AC Breakdown Voltage (ACBDV) tests per IEC standards. Tensile testing confirmed the anisotropic behavior of Kraft paper, with MWCNT addition improving strength by 13 % in the cross direction and showing only a 1.4 % decrease after aging. In the machine direction, strength remained high with minimal changes. All samples met IEC 60641-3-2 standards. AC breakdown voltage improved by 5.8 % in unaged and 3.0 % in aged MWCNT-treated samples, while aging effects were less severe compared to pure POME samples.

12:30 Customer-Centric Power Reliability Assessment of Selected Cebu Distribution Utilities via Real-Time Localized Intelligent Power Monitoring System

Wilen Melsedec O. Narvios, Jayson C Jueco, Rafran P de Villa, Ferdinand F. Batayola, Gilbert Silagpo and Maria Gemel B Palconit (Cebu Technological University, Philippines)

There is restricted access to reliable data from distribution utilities due to privacy concerns, and inadequate infrastructure results in frequent outages and hinders analysis of power reliability issues in regional areas in the Philippines. The paper evaluated power distribution reliability by calculating the Customer Average Interruption Duration Index (CAIDI) using data from a real-time intelligent monitoring system across multiple sites served by local utilities. The intelligent monitoring system utilized an ETL model to collect and manage data via cloud infrastructure and integrate AI models to detect anomalies in the system. VECO and CEBECO I exceeded DOE CAIDI limits with values of 185 and 140 minutes, respectively, while CEBECO II, III, and MECO demonstrated strong reliability with zero interruptions in key areas. To address these gaps, the paper recommends deploying reclosers, advanced outage management systems, and integrating distributed energy resources to reduce outage durations by up to 60% and enhance grid resilience.

12:45 Solar PV Power Generation Forecasting Employing Feedforward Artificial Neural Network for Virtual Power Player Setup

Muhammad Haiqal Mohd Aminuddin, Madihah Md Rasid and Syed Norazizul Syed Nasir (Universiti Teknologi Malaysia, Malaysia)

Integrating renewable energy-based distributed generation (RE-DG) into modern electrical grids is reshaping power system operations. However, this transition brings notable challenges for grid operators, especially in managing variable generation and maintaining reliable supply. A major concern is the need for accurate forecasting to plan generation schedules effectively and avoid excessive reliance on standby sources. This work focuses on improving PV output prediction to support distributed generation scheduling in a Virtual Power Player (VPP) setup. Therefore, this study developed an Artificial Neural Network (ANN) model trained on historical irradiance and weather data from the Solcast Historical Time Series Service. The model was tested on a 39 MWp PV plant located at 1°32'45.6"N, 103°40'12.5"E. The forecasting results show that the approach can deliver high prediction accuracy with 8.3766×8.376×〖10〗^(-6)at epoch 7 mean square error (MSE), and 0.256% range on the scatter plot beyond 30MW. This model may be employed to help operators allocate renewable resources more effectively and reduce backup generation needs across the distribution network.

Thursday, October 30 11:30 - 1:00

Track C7F4 ECD 7.2: Electronics, Circuits & Devices (ECD) 7.2

Room: F4. 503 Dinawan

Chair: Somesh Kumar (ABV IIITM Gwalior India, India)

11:30 Electroencephalogram-Based Feature Classification During Swallowing Using Deep Learning

Rui Takahashi, Shuya Shida and Kyoko Yamazaki (Toyo University, Japan); Motoki Arakawa (Biomedical Engineering, Japan); Kaoru Miyano and Yutaka Suzuki (Toyo University, Japan)

Food texture, which includes properties such as viscosity and elasticity, considerably affects swallowing ease and sensory perception. This study aims to objectively evaluate the physiological responses when swallowing jelly drinks with varying physical properties by analyzing electroencephalogram (EEG) signals. EEG data were collected while participants swallowed four different commercially available jelly drinks. Spectrograms obtained after preprocessing and time-frequency conversion using the short-time Fourier transform were input into the EfficientNetB7 deep learning model for classifying the jelly types based on the EEG patterns. This model achieved high classification accuracy on the training data; however, its performance on validation data was notably lower, suggesting potential overfitting. Jelly1 and Jelly3 were frequently misclassified because of their similar textures, while Jelly4 showed relatively higher classification accuracy, which can be attributed to its distinct physical characteristics. These findings suggest that EEG signals recorded during swallowing contain texture-related neural signatures, and deep learning models can be used to partially capture these differences. This study contributes to the development of neurophysiological methods for food texture evaluation and lays the foundation for applications in dysphagia assessment and food engineering.

11:45 Core-Shell Nanostructures for Dynamic Color Control in Electrochromic Plasmonic Nanopixels

Kawshik Nath (Bangladesh University of Engineering and Technology, Bangladesh & Chittagon University of Engineering and Technology, Bangladesh); Md. Shariful Islam (Bangladesh University of Engineering and Technology & Bangladesh Telecommunications Company Limited, Bangladesh); Bibekananda Nath (Bangladesh University of Engineering and Technology, Bangladesh & Chittagong University of Engineering and Technology, Bangladesh); Ahmed Zubair (Bangladesh University of Engineering and Technology, Bangladesh & Rice University, USA)

Electrochromic nanoparticle on mirror (eNPoM) facilitates voltage-controlled color changes through the adjustment of optical resonances at nanoscale. This study focused on designing eNPOMs integrating plasmonic cores made of Au, AZO, GZO, and ITO with an electrochromic shell made of PANI for three different configurations: cylindrical core-shell, cylindrical core-shell structure with hollow center, and pyramidal core-shell designs. We utilized finite-difference time-domain (FDTD) solver to investigate the scattering cross section and electric field distributions in oxidized, semi-oxidized, and reduced states. Moreover, chromaticity coordinates for different redox states were quantified through the CIE 1931 diagram and color differences were numerically assessed according to the CIEDE2000 standard. Remarkably, the structures comprised of GZO and Au showed CIEDE2000 color differences exceeding 50, whereas the ITO-based configuration demonstrated >56 chromatic contrast with distinct plasmonic resonances. The electric field distribution observed in this study indicated strong field confinement in oxidized states. Electron delocalization was through reduction, aligned with our calculated spectral trends. Moreover, a comparative analysis of the calculated results with WO3 based eNPoM was performed. However, PANI-based systems demonstrated relatively higher color contrast and modulation depth. Our findings will significantly enhance the development of tunable eNPOM platforms such as high-resolution nano-displays, adaptive optics, and responsive metasurfaces.

12:00 Development of a Generator Condition Monitoring Device for Predictive Maintenance Applications Considering Temperature and Vibration Analysis

Vann Anthony Viray (Mindanao State University - Iligan Institute of Technology, Philippines); Marven E Jabian (MSU - Iligan Institute of Technology, Philippines & Mindanao State University - Iligan Institute of Technology, Philippines)

A condition monitoring device was developed to support predictive maintenance strategies for industrial generators by acquiring vibration and temperature data-two common indicators of mechanical and thermal faults. The system integrated a digital accelerometer and a non-contact infrared sensor, with an ESP32 microcontroller serving as the main processing unit to monitor real-time operational parameters. Data was collected at fixed intervals and transmitted wirelessly using an NRF24L01 module. Additional features included battery monitoring, LED indicators, and an alarm system. All components were integrated onto a custom-printed circuit board and enclosed within a 3D-printed housing. The device was designed to operate non-intrusively, powered by either an external source or a rechargeable battery, with power-saving capabilities via light sleep mode for extended deployment. Initial tests demonstrated accurate sensor readings and effective wireless transmission, validating the device's capability for fault detection. Although currently limited to data acquisition and transmission, the system is capable of future integration with data logging, analytics, or machine learning models for advanced predictive maintenance. This solution offers a cost-effective and practical approach to real-time generator condition monitoring.

12:15 Development of a High-SNR, Feature-Based Machine Learning Model for Accurate Multiclass Lung Sound Classification

Jose Antonio J. Loren, Andre Joaquin D. Adiz, Al Fred C. Picana, Arvy C. Santos, J.R. S. Templanza, Seigfred V. Prado and Wally Enrico M Ingco (University of Santo Tomas, Philippines)

Lung diseases are one of the leading causes of global morbidity and mortality, accounting for more than four million deaths annually according to the World Health Organization. Although machine learning has shown promise in automating lung sound classification, most existing models are either focused on binary lung sound classification or have a narrow subset of abnormal sounds, hindering their diagnostic utility. This study addresses these limitations by developing a high-SNR, multiclass, and accurate classification model capable of identifying normal lung sounds and five abnormal types: wheezes, crackles, stridor, rhonchi, and pleural rubs. Various feature extraction techniques were compared, including the standard Mel-Frequency Cepstral Coefficients (MFCC) model, an enhanced-MFCC (eMFCC) model, and a hybrid Discrete Wavelet Transform-Short-Time Fourier Transform (DWT-STFT). The input-output Signal-to-Noise Ratio (SNR) analysis confirmed a significant improvement in signal quality, with median SNR increasing from 16 dB (MFCC) to 35 dB (eMFCC), validating the effectiveness of the enhancement techniques. Classification was performed using Support Vector Machine (SVM) and K-Nearest Neighbors (KNN), with the highest accuracy of 97.69% and 98.34%, respectively, achieved using eMFCC mean features. The results demonstrate the robustness of the proposed high-SNR feature-based model for accurate multiclass lung sound classification.

12:30 Multilayer All-Oxide Polarization-Independent Narrowband Emitter: A Step Towards the Future Thermophotovoltaic Applications

Bibekananda Nath (Bangladesh University of Engineering and Technology, Bangladesh & Chittagong University of Engineering and Technology, Bangladesh); Kawshik Nath (Bangladesh University of Engineering and Technology, Bangladesh & Chittagon University of Engineering and Technology, Bangladesh); Ahmed Zubair (Bangladesh University of Engineering and Technology, Bangladesh & Rice University, USA)

The emitter is an indispensable part of a thermophotovoltaic (TPV) energy conversion system. However, conventional metal-dielectric emitters suffer greatly from the oxidation of metal layers at high temperatures. Emitters based on all-oxide structures can be a possible solution to this problem. Here, we present a polarization and incident angle-insensitive multilayer emitter structure based on MgO/ITO composite layers for a conventional TPV system operating at 1450 to 1500 K temperature. The emission mechanism of the proposed structure was assessed using the finite-difference time-domain (FDTD) method, and the structural dimensions were optimized using a brute-force design approach. The optical simulation of the optimized structure provides a peak emission of around 98.8% at the wavelength of 1928 nm, which coincides perfectly with the spectral response of the In₀.₇₄Ga₀.₂₆As cell and the blackbody radiation of the 1450 to 1500 K heat sources. Moreover, our designed structure was polarization-independent and insensitive to the incident angle of radiation up to 70◦ for both TM and TE polarized light. This study will have an immense impact on high-temperature applications, such as thermophotovoltaic systems, photodetectors, and sensors.

12:45 Design and Comparison of Enhanced P&O and FOCV-Based MPPT for Indoor Light Energy Harvesting in 65nm CMOS Process

Rochelle M Sabarillo, Luisa Mae M Mamburao, Quezza Phola S Patulin and Winzil Khaye V Pitogo (Mindanao State University - Iligan Institute of Technology, Philippines)

This paper presents a comparative study of two maximum power point tracking (MPPT) algorithms Enhanced Perturb and Observe (P&O) and Fractional Open-Circuit Voltage (FOCV)-targeted for indoor photovoltaic (PV) energy harvesting applications. Both designs were implemented using the 65nm CMOS process in Cadence Virtuoso and integrated with a boost converter to ensure efficient energy transfer under low-light conditions. The evaluation focuses on key performance metrics including output voltage, settling time, ripple voltage, boost accuracy, and power consumption. Simulation results show that both algorithms achieve comparable performance in terms of output voltage stability and ripple control, with post-simulation ripple voltages of 0.622% for Enhanced P&O and 0.641% for FOCV, and boost accuracies above 99% for both. However, Enhanced P&O clearly outperforms FOCV in terms of settling time, achieving 370 µs compared to 810 µs, making it more suitable for applications requiring dynamic adaptation and fast-tracking response. On the other hand, while FOCV demonstrates slightly higher power consumption and slower response, it offers lower circuit complexity and simplified control implementation due to the absence of current sensing and a feedback loop. These trade-offs highlight the strengths of each technique depending on application-specific constraints.

Thursday, October 30 11:30 - 1:00

Track C7F5 ETS 7.1: Engineering Technologies & Society (ETS) 7.1

Room: F5. 504 Madai

Chair: Heng Jin Tham (Universiti Malaysia Sabah, Malaysia)

11:30 Sustainability in the Urban Context: Evaluating Metro Manila Residents' Intention to Accept and Adopt Vertical Farming

Shantelle Magsino, Vanessa Mae Malabuyoc and Ritchie Ybañez (National University, Philippines); Ma. Kathleen Duran (National University, Philippines & Mapua University, Philippines); Ferly Ann Revilloza (National University, Philippines)

Rapid urbanization in Metro Manila has intensified challenges in food security, environmental sustainability, and resource efficiency. This study investigates the intention of Metro Manila residents to accept and adopt vertical farming as a viable urban agricultural solution. Anchored on the Theory of Planned Behavior, Sustainable Development Theory, and Diffusion of Innovation Theory, the research examines how economic, societal, and environmental factors influence behavioral intention. A structured questionnaire was administered to 100 residents using stratified random sampling. Data were analyzed using Partial Least Squares-Structural Equation Modeling (PLS-SEM) to identify the relationships among variables. Results revealed that economic and environmental factors significantly affect residents' adoption intention, while societal factors and attitudes toward sustainability integration showed limited influence. The study also highlights the role of perceived behavioral control and subjective norms in shaping adoption behavior. Findings suggest that enhancing public awareness, policy support, and cost-accessibility could facilitate the integration of vertical farming into urban sustainability initiatives. This research provides evidence-based insights for policymakers, urban planners, and agricultural innovators aiming to promote vertical farming in densely populated cities like Metro Manila.

11:45 Informatics Analysis of Glycosylation Pathway by Network Flow Algorithm and Machine Learning

Taisei Matsuo and Kento Totsuka (Soka University, Japan); Akira Togayachi and Kiyohiko Angata (Glycan and Life System Integration Center (GaLSIC), Japan); Shinomiya Norihiko (Soka University, Japan)

Glycans, often termed the "third life chain," are fundamental biomolecules exhibiting unparalleled structural complexity, crucial for diverse biological processes including cancer and cellular communication. Their intricate, branched architectures and non-template driven biosynthesis pose significant challenges for traditional biochemical analysis, necessitating advanced computational approaches. This research addresses the urgent need for in silico modeling of glycan dynamics by focusing on directed glycosylation pathways, which represent the sequential biosynthesis of glycans. Leveraging insights from recent advancements in graph neural networks for glycomics, this study pioneers the application of informatics to glycosylation pathway analysis. Our primary objective is to develop a robust, informatics-based tool that enables researchers without specialized biological expertise to accurately analyze glycan data. Specifically, we propose constructing directed graphs from biochemical glycosylation pathway data and applying the Edmonds Karp algorithm to quantify glycan production and Node2Vec to compare structural properties of pathways. This framework will facilitate the comparison of glycosylation maps across different cancer stages, an area with rich biological data but limited computational evaluation methods.

12:00 Investigation of the Mechanical Properties and Thermal Conductivity of Lightweight Concrete Hollow Blocks with Crushed Coconut Shell as Partial Replacement of Fine Aggregates

Brian Ivan Atienza, John Aeron Humphrey Padilla and Rosendo Jr De Guzman (Philippines); Joseph Carlo Labampa, Bryan De Guzman and Kaycee T. Alcantara (National University, Philippines)

This study examines the use of crushed coconut shells (CCS) as a partial substitute for fine aggregates in lightweight concrete hollow blocks (CHBs). The research evaluates how CCS affects key properties, including compressive strength, thermal performance, and water absorption. Three replacement levels were tested: 5%, 10%, and 15% CCS. Results showed that CCS reduced the density of CHBs by 3.5% to 5.4% compared to conventional lightweight concrete. However, compressive strength decreased by 23.9% to 33% as CCS content increased, making the blocks suitable only for non-load-bearing applications. On the other hand, CCS improved thermal insulation, with lower thermal conductivity observed at higher replacement levels. Water absorption rates increased with CCS content due to its porous structure, but stability improved at 10% to 15% replacement. The findings suggest that 10% CCS replacement offers the best balance between thermal benefits and structural performance. This makes CCS a viable sustainable material for non-structural construction, particularly in tropical climates where thermal efficiency is important. Further research should focus on optimizing mix designs, assessing long-term durability, and exploring large-scale applications to promote wider adoption in the construction industry.

12:15 Implementation of Virtual Reality Based Home Automation System

Harinatha Reddy Chennam and Raghuram Reddy Gajula (G Pulla Reddy Engineering College, India); Vuyyuru Lakshmi (JNTU, India); Pradeep Kumar Allagadda, Bramhananda Reddy Teegala and Siva Reddy Y V (G Pulla Reddy Engineering College, India)

In today's fast growing economies, Home Automation has become a crucial part. There is a rapid change in it's technology and various automating strategies are implemented. The, and New ways of producing super sensor systems is growing up as the conceptual understanding for automation has been changed. Home automation is the only way to manage everything. In Indian houses almost all the people make mistakes while using normal electrical circuit combination. They make mistake of not switching off the electrical gadgets when not in use. They even forget when go out or completely forget about it. The result of this is the wastage of energy when it is not in use. In order to avoid these drawbacks, automation techniques can be implemented. This paper presents how to fill the gap between the company and client in imagination of Home Automation system before the installation in real world. In this paper a virtual home automation system that reflects the real world is presented. Client can experience his desired home automation system even before the installation in real world. The Virtual Home Automation is developed using Unreal Engine Software

12:30 SpeechPal: Specialized Speech Aid Device for Therapists Assisting Children with Repaired Cleft Lip and Palate

Jafeth C Estocado, Jan Carlo A Magpantay, Joyce Ann P Precilla and Prince Earl M Salva (FAITH Colleges, Philippines); Marco A Burdeos (First Asia Institute of Technology and Humanities, Philippines)

Children with repaired cleft lip and palate (CLAP) often face challenges in speech clarity and communication, necessitating tailored therapeutic support. In response, the researchers designed and implemented SpeechPal, an innovative assistive technology intended to aid speech therapists in providing effective therapy. The system integrates three key features: text-to-speech and speech-to-text conversion, nasal airflow detection, and augmentative and alternative communication (AAC) functionality. The project implementation involved Intel Core i7-9700 (9th Gen) with MSI GeForce GTX 1050 Ti Dual Fan OC, Ypa 4016 Headset Microphone, HXV710 Nasal Air Flow Sensor, ANMITE 14" HDR IPS FHD Portable LED Gaming Touch Monitor, HuBERT Model, Google Speech Recognition, and PyQt6-Based Graphical User Interface for AAC. The results showed that the researchers successfully implemented the system and achieved the objectives of the study. This research highlights the potential of SpeechPal to address speech therapy challenges for children with repaired CLAP, offering a reliable, efficient, and userfriendly solution that promotes inclusivity and improved communication.

12:45 Early Flexural and Thermal Behavior of Recycled PET Fiber-Reinforced Fiber Cement Boards for Sustainable Applications

Ahl Paz, Jeremiah Lara and Chester Cortez (Philippines); Jomar Llanto and Kaycee T. Alcantara (National University, Philippines); Ma. Kathleen Duran (National University, Philippines & Mapua University, Philippines)

This study examines the flexural and thermal performance of fiber cement boards (FCBs) reinforced with recycled polyethylene terephthalate (PET) fibers as a sustainable alternative to conventional materials. Mixes with 5%, 10%, 15%, and 20% PET content by weight were prepared and evaluated against a control. Flexural strength was measured after seven days using a three-point bending setup, while thermal behavior was assessed under simulated tropical conditions using a temperature-controlled environment. The 15% PET mix showed the highest flexural strength, although it remained below the commercial board benchmark, indicating the need for further optimization. The 5% PET mix demonstrated the most consistent thermal insulation, likely due to improved pore structure at lower fiber content. While PET fiber inclusion enhanced post-crack behavior and thermal resistance, early-age mechanical performance remained limited and did not meet industry thresholds. Nonetheless, incorporating recycled PET supports sustainable construction by reducing plastic waste and encouraging circular material utilization. Future research should include cost analysis, hybrid fiber development, and field-based durability evaluation.

Thursday, October 30 11:30 - 1:00

Track C7F6 CCI 7.2: Computing & Computational Intelligence (CCI) 7.2

Room: F6. 505 Sepilok

Chair: Pei Yee Chin (Universiti Malaysia Sabah, Malaysia)

11:30 iTRAC: Intelligent Tracking and Real-Time Analysis on Cloud Using RFID and MQTT

Dr. Jayanthi Ganapathy, Sr (Sri Ramachandra Institute of Higher Education and Research, India); Nachiappan N (National University of SIngapore, Singapore); Sreevijnya M (Sri Ramachandra Institute of Higher Education and Research, India & Agilisium Consulting, India); Purushothaman Ramachandran (Sri Ramachandra Institute of Higher Education and Research, India)

This research explores real-time monitoring of diverse assets using AIoT, leveraging edge-cloud collaboration for enhanced responsiveness and intelligence. RFID sensors are employed to validate edge computing functionality in tracking and anomaly detection. RFID tag reads are processed on the edge using Python scripts, which collect temporal data such as tag frequency and signal strength with precise timestamps to identify irregularities. A compound AI system integrates an LSTM model for data encoding and normalization, and an Isolation Forest for outlier detection on critical features. These models are containerized using Docker to enable consistent deployment across edge devices and cloud platforms. Processed RFID data is transmitted from a Raspberry Pi to a private OpenStack cloud via MQTT using QoS level 1, ensuring reliable, low-latency anomaly reporting. A lightweight dashboard interface enables real-time visualization and monitoring of anomalies. The system is scalable, secure, and adaptable for applications in logistics, healthcare, and industrial asset tracking.

11:45 Unlocking Battery Health: Real-Time State of Health Estimation Using Deep Learning on Partial Charging Data Segments

Hasanur Zaman Anonto (American International University-Bangladesh, Bangladesh); Md Ismail Hossain (American International University Bangladesh, Bangladesh); Isha Das (Chittagong University of Engineering and Technology, Bangladesh); Afrin Tanzila Rabbani and Samiul Ahasan Sajid (American International University-Bangladesh, Bangladesh); Md Aktar Hossain and Minul Khan Rahat (Lamar University, USA); Abu Shufian (American International University-Bangladesh, Bangladesh)

Partially and randomly based charging information on electric vehicles and energy storage systems is essential in the proper selection of charging strategies to guarantee effectiveness and safety, and this depends heavily on the accurate online estimation of battery state of health (SOH). This paper presents a comprehensive end-to-end evaluation protocol that uniformly samples a variety of different aspects of partial charging and a comparison of three kinds of neural network architecture feed-forward (FNN), convolutional (CNN), and long short-term memory (LSTM)) under direct and transfer learning conditions. The latency and error rates of inference on each of these models are profiled to determine viability to be deployed on-board. Explainability methods detect key charging intervals that work on the predictions. Observations demonstrate that rather than just lightweight implementation, FNN with moderate accuracy, when compared to CNN, leads to enhanced performance through the extraction of local patterns and the best precision when compared to LSTM which models sequences. All the architectures are always improved with transfer learning, with average errors roughly decreasing by 0.05-0.1% point and error bands getting narrower. As far as model compression is concerned, it is posited that the application of efficient LSTM variants would accommodate embedded hardware constraints. The development of this work will offer usable information on how to pick and tune deep learning models to perform well, maintain real-time SOH monitoring in realistic charging situations.

12:00 A Single-Shot Multi-Box Detector with MobileViT Backbone for Metallic Surface Defect Detection

Adelson Lok Thien Chee, Saaveethya Sivakumar, King Hann Lim, Ing Ming Chew and Chye Ing Lim (Curtin University Malaysia, Malaysia); Siew Eng Fui (Press Metal, Malaysia)

Defect detection is an essential step to ensure the quality of manufactured metal products. Industrially, detecting metallic surface defects can be challenging due to a resource constrained environment in terms of hardware computational availability. In this study, we explore a lightweight defect detection model, specifically the single-shot multi-box detector variant called SSDLite. In this study, the SSDLite is paired with varying backbone base networks, MobileViT, MobileNetv2 and MobileNetv3. Transfer learning is employed to enhance SSDLite learning by enabling a relation between previous tasks and the targeted task, which is a domain-specific task. The same implementation details are applied across all the SSDLite models (MobileViT, MobileNetv2 and MobileNetv3) being trained on the PASCAL VOCdataset, and then the prior knowledge in the form of pre-trained weights is used to fine-tune the model on a domain specific metallic surface defect dataset. The metallic surface defect in this study is based on a hot-rolled steel strip surface defect dataset called the NEU-DET dataset. This study lays the groundwork for future investigations into alternative backbone architectures with SSD for enhanced detection accuracy and efficiency in real-world manufacturing applications.

12:15 ContractIQ: A Multimodal RAG-Based Agentic System for Intelligent Contract Understanding

Abhay A Rao, Abhay Raghavendra Revankar, Nikita Nair, Shreya Mittal, Uma D and Ujjwal Mohan Kumar (PES University, India)

When organizations in industries like finance, health-care, real estate, and technology deal with intricate, high-risk contracts, legal contract analysis cannot be avoided. Accurate and proper analysis assists in the interpretation of minute details, evaluation of risks, and adherence in contracts. Smart contractual automation is needed since manual checking is cumbersome and susceptible to errors. By applying Retrieval-Augmented Generation (RAG), an agentic eleven-coordinated agent, and Chain-of-Thought (CoT) prompting to interpret legal analysis, the study proposes a legal contract analysis system based on Google's Gemini large language model (LLM). The system includes Clause extraction, definition detection, risk analysis, compliance checks, QnA and summarization. Gemini can be finely adapted to be used in the legal field through LoRA finetuning. Testing achieves strong performance: ROUGE-1 of 0.42, ROUGE-2 of0.38, ROUGEL of 0.40, F1 score of 0.70, and BLEU of 0.45, indicating high-quality legal text generation. This solution significantly reduces manual labor but with enhanced legal precision and compliance.

12:30 Ganoderma Detection in Oil Palm Plantations Using UAV Hyperspectral Imaging and AI

Chee Seng Kwang and Siti Fatimah Abdul Razak (Multimedia University, Malaysia); Sumendra Yogarayan (Multimedia University (MMU), Malaysia); Abdul Mateen Montree Bin Muhammad and Ai Ling Choo (iRadar Sdn Bhd, Malaysia); Shahrul Azman Bakar (FGV R&D Sdn Bhd, Malaysia); Haryati Abidin (University Putra Malaysia & FGV R&D, Malaysia)

The imperative for early and accurate detection of Ganoderma Boninense infections in oil palms is paramount to mitigating the devastating impact of basal stem rot. This disease poses a significant threat to palm oil production and the economic stability of affected regions. Conventional detection methods rely heavily on visual inspection or destructive laboratory analysis, which are time-consuming, labour-intensive, and cost-inefficient for large plantations. To address these limitations, this paper proposes a novel method for Ganoderma detection using frame-based hyperspectral imaging captured by an unmanned aerial vehicle (UAV). The approach incorporates feature-based band registration to correct spectral misalignments, followed by dimensionality reduction using principal component analysis (PCA). Support vector machines (SVMs) were evaluated for classification alongside a fine-tuned ResNet50 model. The results demonstrated that the TF-ResNet50 model achieved an accuracy of 84%, with a sensitivity of 74% for early infected trees and a specificity of 86% for healthy trees, underscoring the potential of UAV-based hyperspectral imaging for scalable, non-destructive disease monitoring in oil palm plantations.

12:45 Automated Nuclei Segmentation in PR-IHC Breast Cancer Images Using the Cellpose Deep Learning Model

Hasanul Bannah and Mohammad Faizal Ahmad Fauzi (Multimedia University, Malaysia); Sarina Mansor (MMU, Malaysia); Md. Shoukhin Khan, Wan Siti Halimatul Munirah Wan Ahmad and Md Serajun Nabi (Multimedia University, Malaysia); Seow-Fan Chiew, Phaik Leng Cheah and Lai Meng Looi (University Malaya Medical Center, Malaysia)

In digital pathology, precise nuclei segmentation in immunohistochemical-stained tissue sections is essential for clinical decision-making and subsequent quantification of biomarkers. This task is particularly important for the analysis of hormone receptors in breast cancer, where the status of the progesterone receptor (PR) plays a key role in determining the response to treatment. However, because of differences in nuclear morphology, staining intensity, and overlapping structures, nucleus segmentation in PR-IHC images is still difficult. In order to separate PR-expressing nuclei from high-resolution breast cancer histopathology images, we present an automated instance segmentation framework in this work that is based on the Cellpose deep learning model. supported by an entirely novel ground truth (GT) dataset produced by a hybrid pipeline. In order to create the GT, 250 high-resolution PR-IHC images with trustworthy binary nuclei masks were combined with automated segmentation (StarDist), extensive manual corrections, and multi-round pathological validation. On the test set, our Cellpose-based approach consistently performs properly, achieving an average F1 score of 0.8535, precision of 0.8882, recall of 0.8215, and IoU of 0.7445. Strong segmentation of extracted and overlapping nuclei is confirmed by visual results. This study offers a useful resource for future research in hormone receptor quantification and computational pathology, as well as the first automated segmentation benchmark for the PR-IHC dataset.

Thursday, October 30 11:30 - 1:00

Track C7F7 ETS 7.2: Engineering Technologies & Society (ETS) 7.2

Room: F7. 506 Selingan

Chair: Siti Nurfadilah Binti Jaini Status (Universiti Malaysia Sabah (UMS), Malaysia)

11:30 Surface EMG - Based Quantitative Assessment of Muscle Coordination and Complexity in Post-Anterior Cruciate Ligament Injury Individuals

Arunthathi S (Anna University, India); S Saranya (Sri Sivasubramaniya Nadar College of Engineering, India); Rakshana R (SSN College of Engineering, India)

Surface Electromyogram (sEMG) signals can be used as inevitable tool to assess the muscle coordination and complex activation muscles, particularly strength and order of muscle recruitment during walking. The muscle coordination feature like Co-Activation Index (CAI) is an important parameter to examine the contribution of specific antagonist and agonist muscle pairs. In the proposed study, features which exhibits complex behavior of muscles like kurtosis, Correlation Dimension (CD), Sample Entropy (SE) and Skewness were used to compare the variations among post Anterior Cruciate Ligament (ACL) injury and normal subjects. The dataset consists of surface EMG signals obtained from Medial Hamstrings (MH), Biceps Femoris (BF), Vastus Lateralis (VL), Gastrocnemius Medialis (GM), Gastrocnemius Lateralis (GL) from Control Groups (CG) and individuals undergoing post-ACL injury Reconstruction (PAIR) between 18 and 30 years of age. CAI was calculated between VL & BF muscle pairs. CAI (p=0.84) and kurtosis measures fail to show the significant differences when comparing PAIR with CG while other features like CD (PGM=0.04), SE (PMH=0.04) and Skewness (PGL=0.03) provide the significant difference. Therefore, the proposed study contributes to the understanding of the dynamics of EMG features mentioned and its importance in quantitative assessment of post-ACL injury reconstruction.

11:45 Future Trip Profile Nomination (FTPN): A Framework for Proactive EV Routing

Farhaan P Alawiya and Abdul Aziz G. Mabaning (Mindanao State University - Iligan Institute of Technology, Philippines)

Future Trip Profile Nomination (FTPN) is proposed as a proactive framework for electric vehicle (EV) routing and traffic coordination. Built upon the broader concept of Future Behavior Nomination (FBN), FTPN allows EV users to voluntarily submit anticipated trip information-including origin, destination, departure time, and optional waypoints. These submissions are aggregated to support optimized routing decisions that minimize traffic congestion and ensure sufficient battery charge levels, taking into account each vehicle's range and the availability of charging infrastructure. Foundational models are introduced to integrate these behavioral nominations with conventional forecast data, incorporating time-dependent accuracy and varying user participation rates. A simulation-based validation demonstrates that the proposed models can significantly improve trip prediction accuracy by fusing user nominations with conventional forecasts. By shifting from reactive, demand-driven routing to a behavior-informed paradigm, FTPN supports the development of Proactive Vehicle Traffic Management (PVTM) systems. This framework offers a scalable and intelligent approach to mobility planning that aligns with United Nations (UN) Sustainable Development Goals (SDG), particularly SDG 11 on sustainable cities and communities, and SDG 13 on climate action through improved energy efficiency and reduced transportation emissions.

12:00 A Method for Generating Panoramic Borehole Images via 3D Analysis Using Gaussian Splatting

Kyuhei Honda (National Institute of Technology, Oita College, Japan)

This study investigates a method for converting borehole camera images, used for evaluating the integrity of geological formations and rock masses, into three-dimensional point cloud data for high-precision analysis of borehole wall geometry. The procedure is as follows. First, a camera-equipped probe is moved vertically within the borehole to acquire continuous video images. Then, the borehole wall is reconstructed in 3D using a technique called 3D Gaussian Splatting. This technique enables rapid and high-quality representation and shape estimation of the 3D point cloud. The wall surface irregularities are represented by the point cloud, while wall textures are derived based on Gaussian distributions. The acquired 3D data often contains noise, such as points located away from the actual borehole wall. To address this, the borehole wall is approximated as a cylinder, and noise is removed based on the estimated cylinder axis and radius. Unfolded panoramic images are generated using the cylinder axis and the color information associated with each point. This approach significantly enhances the quantitative assessment and operational efficiency of borehole investigations, contributing to preventive maintenance against infrastructure deterioration and geological hazards.

12:15 Projection Net: A CNN Framework for Segmention of Teeth from Panoramic X-Ray Images

Nagaraj Yamanakkanavar (Central University of Karnataka & CUK Karnataka, India); Sibasankar Padhy (Indian Institute of Information Technology, India); D Chaitra (Indian Institute of Information Technology, Dharwad, India); Sameena Begum (Central University of Karnataka, India); Santosh Uppinal (ESIC Hospital, India)

The segmentation of X-rays and computed tomography (CT) images is crucial for identifying and separating tooth characteristics. This process plays a vital role in various clinical applications such as dental diagnostics, treatment planning, and surgical procedures. Moreover, a detailed analysis of tooth structures from segmented X-ray images enables accurate diagnosis of specific oral health conditions. Among these, deep learning techniques have gained significant attention due to their ability to produce effective results on large datasets. Consequently, deep learning is increasingly favored over traditional machine learning approaches. In this paper, we aim to explore current deep learning-based segmentation algorithms used for quantitative tooth analysis in diagnosing oral health issues. The proposed projection module, integrated with group convolutions, enhances feature aggregation, leading to improved accuracy while also reducing complexity (number of trainable parameters). An evaluation of the proposed method was carried out using images from the State University of Southwestern Bahia's diagnostic imaging center (UFBA-UESC). In this facility, a dental dataset is available with 1500 images that can be used to segment teeth based on panoramic X-rays. As a result of applying the proposed method to the UFBA-UESC dataset, a mean accuracy of 0.97, precision of 0.92, recall of 0.91, and F1-score of 0.92 were achieved, surpassing the performance of existing methods for segmenting teeth images.

12:30 LIFE-MAP: an Indigenous Biosignature Detection Protocol Using Multi-Biomolecule Based Analysis

Md. Ehsanur Rahman, Tyseer Ninad, Md Anwarul Islam Aion, Md Rafin Haque and Tasin Ahmed (Aviation and Aerospace University Bangladesh, Bangladesh); Md. Samin Rahman (American International University-Bangladesh (AIUB), Bangladesh)

This paper introduces LIFE-MAP (Life Investigation Framework for Extraterrestrial Mapping and Analysis Protocol), a modular system developed to accurately identify biosignatures in extraterrestrial environments. Combining advanced mechanical, electronic, and sensor technologies, LIFE-MAP integrates biomolecular assays, microbial volatile organic compound (mVOC) analysis, and environmental monitoring to classify samples as Extant, Extinct, or NPL (No Presence of Life). With specialized sensors designed to detect proteins, carbohydrates, chlorophyll, and microbial byproducts like ethanol and formaldehyde. The system provides precise astrobiological detection using multi-biomolecule based soil analysis and CNN based rock analysis. The modular design of LIFE-MAP enables seamless integration with robotic platforms, including Mars rovers. This system marks a significant advancement in life-detection technologies, drawing inspiration from exploration missions to extreme environments where life adapts to harsh conditions, including nuclear disaster sites. The system's ability to assess all parameters positions it as a vital tool for future missions, offering a comprehensive and efficient approach to detect life across a range of extreme environments.

12:45 Characterization of Physiological, Psychological, and Physical Stress Responses from Wearable Technology

Joyce Anne A Bernardino (University of Santo Tomas, Philippines & BRAIN Lab, Philippines); Danna Francheska F. De Regla, Florenz TJ DG Galvez, Sean Archie D Gregorio, Anne Margarita C. Yu Ekey, Wally Enrico M Ingco and Seigfred V. Prado (University of Santo Tomas, Philippines)

Stress affects health and well-being, with heart rate (HR) and heart rate variability (HRV) serving as the key indicators closely related to the autonomic nervous system (ANS), which are parameters controlling stress responses. Albeit most studies focused on one to two parameters, this study explored the interplay of three, namely, physiological, psychological, and physical stress responses from wearable technology data. With the employment of the Gaussian Mixture Model (GMM), Uniform Manifold Approximation and Projection (UMAP) Manifold Learning, and statistical analysis, the research aimed to uncover hidden patterns of stress responses, assessing stress severity and binary classifications. Significant overlaps in moderate stress levels were observed between the mild and severe levels, confirming challenges in distinguishing stress levels. This led to the adoption of binary classifications, enhancing robustness and simplifying the outputs. Statistical validation confirmed strong correlations between UMAP components and stress severity. Physiological markers, including HR, electrodermal activity (EDA), and accelerometer data (ACC), consistently exhibited high correlations with severe stress levels. While skin temperature (TEMP) and interbeat intervals (IBI) contributed to moderate stress differentiation. Finally, mild stress has a notable connection to blood volume pulse (BVP), IBI, and HR.

Thursday, October 30 11:30 - 1:00

Track C7F8 CCI 7.3: Computing & Computational Intelligence (CCI) 7.3

Room: F8. 507 Monsopiad

Chair: Kit Guan Lim (Universiti Malaysia Sabah, Malaysia)

11:30 Element Based User Interaction with Design Semantics of Mobile Apps and Usability Assessment

Gundala Shanmukhi Rama, Sangharatna Godboley and Ravichandra Sadam (National Institute of Technology Warangal, India)

Designing interactive UI templates is a challenging task. Designers often struggle to determine the best design choices, and even experienced professionals spend significant time evaluating layouts. Moreover, assessing whether a design is good or bad for users remains difficult. This study aims to develop a framework for predicting and scoring the placement of interactive UI elements. By providing usability scores for element placement, our goal is to help trace the users interaction and make it more efficient and assist designers in optimizing their layouts. We employ the YOLO model to detect interactive elements in UI screenshots and assess their placement on a usability scale. The model predicts element positions and usability scores, enabling designers to refine their layouts based on data-driven insights. The model evaluates UI designs by identifying interactive elements and assigning usability scores. Designers can use these scores to assess the effectiveness of their layouts and make informed improvements without direct position suggestions. Our approach enhances UI usability, helping designers create more effective interfaces aligned with current design trends.

11:45 Design and Development of Deep Learning Framework for Glaucoma Detection via Retinal Scan Analysis

Siddharth Ranganatha (R V College of Engineering, Bengaluru, India); Sujatha Badiger (RV College of Engineering, India)

A deep learning framework has been developed to automatically detect glaucoma through the analysis and segmentation of retinal scans, leveraging the U-Net architecture for precise identification of key features. The U-Net's distinctive design facilitates the effective extraction and segmentation of anatomical structures critical for glaucoma diagnosis, including the Cup-to-Disc Ratio (CDR), Rim-to-Disc Ratio (RDR), Disc Damage Likelihood Scale (DDLS), and the Inferior-Superior-Nasal-Temporal (ISNT) rule. This framework's robustness is further improved by its advanced preprocessing capabilities, which ensure accurate identification of biomarkers through techniques such as noise reduction, contrast enhancement, and data augmentation. These preprocessing steps are crucial in enhancing image quality and enabling the model to focus on relevant features. Evaluations conducted on retinal image datasets have validated the framework's effectiveness in distinguishing anatomical structures pertinent to glaucoma diagnosis. This capability supports early risk assessment and contributes to the development of scalable, cost-effective tools for glaucoma detection in clinical settings. By facilitating early intervention, this framework holds promise for improving clinical outcomes and advancing the accessibility of glaucoma screening.

12:00 A Stacking-Based Multi-View Class-Level Refactoring Prediction Framework

Hardik Hardik (NIT Kurukshetra, India); Lov Kumar and Vikram Singh (National Institute of Technology, Kurukshetra, India)

Refactoring prediction plays a crucial role in software maintenance by identifying structural improvements in code without altering external behavior. However, class imbalance and the inherent complexity of real-world codebases pose significant challenges for traditional detection approaches. In this study, we present a multi-view stacking-based framework that integrates structural features from object-oriented (CK) metrics with semantic representations derived from CodeBERT embeddings. To address data imbalance, SMOTE is applied exclusively to the combined feature representation. Each feature modality is processed by dedicated base classifiers, and their outputs are aggregated via a meta-learner in a stacking ensemble. The proposed method is evaluated on two open-source Java projects-ANTLR4 and Titan-using 22 diverse classifiers within a stacking pipeline. Results show that combining inputs with SMOTE significantly improves classification performance. For example, LightGBM and Extra Trees achieved AUC scores of 0.92 and 0.91, respectively, on ANTLR4, while Gradient Boosting and LightGBM exceeded 0.90 AUC on Titan. The stacking approach consistently outperformed single-view baselines, with notable improvements in F1-score (up to 15%) and F-measure (over 12%) across configurations. These findings validate the effectiveness of multi-view stacking with class balancing for robust and scalable refactoring prediction.

12:15 Analysis of Speech Features in Identifying Client's Change Talk in Motivational Interviewing

Shareef Babu Kalluri (University of Petroleum and Energy Studies, India); Deepu Vijayasenan (NITK, India)

Motivational Interviewing (MI) is a frequently used and effective psychotherapy approach for treating behavioral problems. MI is a collaborative interaction for understanding the client's own reasoning for a change in behavior. In this study, we analyzed an MI corpus in the nutrition and fitness domains, in which counselor and client utterances were categorized using Motivational Interviewing Skill Code (MISC). During the interaction when the client expresses the need or willingness to change is the Change Talk (CT). We aimed to analyze the speech features and proposed a BiLSTM multimodal neural network model for detecting the change talk or not a change talk. Our approach using speech and language information in detecting the change talk is par with other multimodal approaches (language and facial information) with an F1-score of 0.573 for CT. The proposed set of speech features shows the statistical significance in identifying the change talk or not a change talk.

12:30 Bridging the Gap Between Natural Language and CLI: An Intelligent Assistant Approach

Arya Ajay Gupta, Uchit N M, Lakshmi Kamath, Mahima NR and Uma D (PES University, India)

There is a large gap between natural language and command-line interfaces (CLI) because of the syntactic stiffness, non-tolerance to errors, and steep learning curve for terminal interaction. This paper introduces an AI-driven command-line assistant that interprets natural language queries into executable Unix shell commands based on a Retrieval-Augmented Generation (RAG) framework coupled with a multi-agent architecture. The platform utilizes the Gemini Large Language Model (LLM) to interpret user intent and return human-readable responses, and FAISS-based vector indexing to allow for speedy, semantically close command retrieval. The Command Optimizer module suggests sequences based on functional compatibility, usage patterns, and graph-based relationships between commands. The multi-agent pipeline breaks down user queries into dedicated stages allowing for modular reasoning and successful task execution. Experimental assessment proves contextual precision, response appropriateness, and flexibility improvements. This research validates the value of agent-based RAG models in covering the gap between natural language interaction and CLI-based system management.

12:45 Integrating Generative AI Tools with Design Thinking: a Facilitator's Manual Approach for Rapid Innovation

Ahmad Nizar Harun (Mimos Berhad & Universiti Teknologi Malaysia, Malaysia); Azman Bin Hussin, Saidatul Farrah Binti Muhammad Johar, Fatin Khairunnisa Binti Mohd Adha and Tuan Amera Binti Tuan Kamaluddin (MIMOS, Malaysia)

This paper examines a novel approach to integrating generative Artificial Intelligence (AI) tools within Design Thinking (DT) workshops, as outlined in a comprehensive facilitator's manual. The manual provides a step-by-step guide to enhance various stages of the DT process, from ideation to implementation planning with Key Performance Indicators (KPIs), within a condensed 1-day workshop format. This paper highlights the benefits of leveraging Large Language Models (LLMs) and other AI tools for accelerating idea generation, streamlining data analysis, enabling rapid prototyping, and incorporating triangulation methods for robust outcomes. It also addresses the inherent challenges and limitations of AI, emphasizing the crucial role of human facilitators in ensuring ethical, accurate, and culturally sensitive innovation. Through a hybrid human-AI facilitation framework, the manual introduces pragmatic heuristics and modular templates to guide participants from problem scoping to validated solution narratives. Ultimately, this work offers a practical contribution to educators, consultants, and innovation leaders seeking to embed responsible generative AI practices into human-centered design workflows.

Thursday, October 30 1:30 - 3:30

Interactive Session C.1

Room: Interactive Area 1, Foyer of Sipadan

Chairs: Md Pauzi Abdullah (UTM, Malaysia), Fatanah Mohamad Suhaimi (Universiti Sains Malaysia, Malaysia)

#1 Enhanced Mechanism for Neighbor Discovery Protocol Table Exhaustion Attacks in IPv6 Networks

Navaneethan CArjuman (CoE for Advanced Cloud, Faculty of Computing and Informatics, Multimedia University, Cyberjaya, Malaysia); Muhammad Ukasyah Bin Md Yusof and Zar Chi Hlaing (Faculty of Computing and Informatics, Multimedia University, Malaysia); Sami Hassan Omer Salih (Al Jouf University, Sudan & Sudan University of Science and Technology, Saudi Arabia)

The adoption of Internet Protocol version 6 (IPv6) has increased due to the requirements for a larger address space and better suitability for modern networks, such as Internet of Things (IoT) networks, but it also introduces new security challenges. The Neighbor Discovery Protocol (NDP) that supports features like the router discovery and address resolution, is not secured due to its stateless and trust-based feature. The most notable one is the NDP Table Exhaustion attack that consists of overwhelming the network with forged Neighbor Solicitation (NS) and Neighbor Advertisement (NA) packets, inducing neighbor cache overload and denial-of-service (DoS). Current defenses, such as Secure Neighbor Discovery (SEND) or machine learning-based detection models, have significant computational overheads and cannot be adapted to resource constrained networks, creating a gap in the overall security of IPv6. To address this problem, this research paper suggests a lightweight, flow-based detection framework that integrates an entropy-based analysis with rule-based thresholds to detect NS and NA flooding attacks. The system gathers traffic into flows, entropy checks on key fields such as source Internet Protocol (IP) and network prefix, and alerts on anomalies when the traffic moves out of the normal operation range. It had the average precision of 0.91, average recall of 0.88, and F1-score of 0.87, but with minimal overhead and highly deployable in both enterprise and IoT networks.

#2 Design of Low-Power Active Inductor-Based Digitally Controlled Oscillator for All-Digital PLL in Zero-Energy IoT Devices

Xi Zhu (University of Technology Sydney, Australia); Jefferson A. Hora (MSU-Iligan Institute of Technology, Philippines); Ricster franuel Sibala (Center for Integrated Circuits Design (CICD) MSU-IIT, Philippines); Haniah L Mohammad (Mindanao State University - Iligan Institute of Technology, Philippines)

This paper presents the design and performance evaluation of a low-power, high-frequency Digitally Controlled Oscillator (DCO) incorporating an active inductor, implemented using 22nm Fully Depleted Silicon-On-Insulator (FDSOI) technology. Tailored for energy-constrained Internet of Things (IoT) devices-particularly those relying on RF energy harvesting-the proposed DCO achieves a frequency tuning range from 44.78 GHz to 45.35 GHz, while maintaining a total power consumption of less than 12 mW. The integration of an active inductor replaces conventional bulky passive inductors, enabling significant area reduction and improved compatibility with standard CMOS processes. This makes the design highly suitable for compact and scalable on-chip RF systems. Although the design exhibits a moderate phase noise of -78.96 dBc/Hz at 1 MHz offset, it offers a well-balanced trade-off between power efficiency, frequency performance, and spectral purity. Overall, the proposed DCO architecture demonstrates a viable solution for next-generation, low-power mmWave transceivers in dense IoT networks and other wireless sensor applications.

#3 Machine Learning-Based Analysis Method for Brain Activity Measurement Data Under Pleasant and Unpleasant Stimuli

Takumi Ishimoto, Rui Takahashi, Shuya Shida and Yutaka Suzuki (Toyo University, Japan)

In this study, we investigated the feasibility of automatic emotion classification by applying the machine learning algorithm Random Forest to time-series brain activity data measured using functional near-infrared spectroscopy (fNIRS). fNIRS is a noninvasive neuroimaging technique capable of monitoring cerebral hemodynamics by detecting changes in oxygenated and deoxygenated hemoglobin concentrations in the cortical surface. It offers practical advantages such as portability, safety, and ease of use, making it suitable for real-world emotion research.

Fifteen healthy male participants were presented with emotionally valenced visual stimuli, consisting of pleasant, unpleasant, and neutral images that had been pre-classified based on previous subjective ratings. During the task, changes in blood flow in the prefrontal cortex were recorded using 22 fNIRS channels. After preprocessing the fNIRS signals to reduce drift and physiological noise, we applied statistical analyses and machine learning techniques to evaluate emotional responses.

Specifically, we conducted paired t-tests to assess significant differences in hemoglobin levels before and during stimulus presentation, and used the feature importance metric from Random Forest to identify which brain regions contributed most to emotion classification. The classification model achieved a remarkably high accuracy of 99.48% using all channels and maintained strong performance (96.04%) even when restricted to the top five most informative channels.

These results indicate that the integration of fNIRS and machine learning is a promising approach for developing objective, reliable, and noninvasive methods for emotion recognition, with potential applications in fields such as affective computing, mental health assessment, and brain-computer interfaces.

#4 Design of Low-Power Digitally Controlled Oscillator for All-Digital PLL Receiver in Zero-Energy IoT Devices

Jacob Anthony M Agito (Mindanao State University Iligan Institute of Technology, Philippines); Xi Zhu (University of Technology Sydney, Australia); Jefferson A. Hora (MSU-Iligan Institute of Technology, Philippines); Haniah L Mohammad (Mindanao State University - Iligan Institute of Technology, Philippines)

The proliferation of Internet of Things (IoT) devices in the era of sixth-generation (6G) wireless communication demands ultra-low power and high-frequency circuit solutions to support sustainable, near-zero energy operation. As IoT devices become more pervasive in applications such as smart environments, industrial automation, and wearable electronics, there is a growing need for compact, energy-efficient frequency generators capable of operating in the millimeter-wave spectrum. This study presents the design and analysis of a Digitally Controlled Oscillator (DCO) operating at 45 GHz, conforming to the IEEE 802.11aj standard, and implemented using GlobalFoundries' 22nm Fully Depleted Silicon-On-Insulator (FDSOI) technology. The oscillator core adopts a complementary Colpitts topology, chosen for its efficiency and suitability at high frequencies. Frequency tunability is achieved through a digitally controlled switched capacitor array, implemented with low-leakage transmission gates. Operating at a low supply voltage of 0.8V, the proposed DCO achieves a power consumption of only 5.959 mW and demonstrates excellent spectral purity, with a phase noise of -99.03 dBc/Hz at 1 MHz offset, making it ideal for ultra-low-power mmWave transceivers in dense IoT networks.

#5 Crowdsourced Geospatial Cellular Data and Fuzzy Rule-Based Inference for Optimizing Network Selection and Radio Coverage Mapping in Maritime Operating Zones

Maria Gemel B Palconit, Jonathan Maglasang, Jayson C Jueco, Mary Nathaline Sevilla, Rau Lance Cunanan and Gabriel B Valenzuela (Cebu Technological University, Philippines)

Reliable and adaptive wireless communication is critical for Maritime Autonomous Vehicles (MAVs) operating in hybrid land-sea environments. This study proposes an intelligent framework that integrates crowdsourced cellular geodata with fuzzy logic inference to optimize mobile network selection and radio technology mapping across maritime zones. Using publicly available OpenCellID data, k-means clustering was applied to stratify cell tower distributions and determine radio-type availability in key land and offshore locations. Then a fuzzy inference system (FIS) was developed to assess the preference of the mobile network operator and predict the optimal radio technologies, GSM, UMTS, or LTE, based on signal strength, tower proximity, sample density, and location priority. Visualization techniques, including geospatial graphs, surface response maps, and signal overlays, were used to validate the FIS outputs. The results show that Smart (MNC 3) is the top choice of operators due to its strong coverage of LTE at ground stations and reliable UMTS access in maritime areas. This radio mapping is an important reference point for creating adaptable data routing strategies, which are crucial to maintaining efficient low-power communications with MAVs that can handle delays. This approach contributes to a scalable, data-driven methodology to enhance wireless communication reliability in maritime Internet of Things (IoT) systems.

#6 Soft-Actor Critic Deep Reinforcement Learning for Reconfigurable Intelligent Surface in Vehicle-to-Everything Network

Reng Yi Kueh (Curtin University, Malaysia); Choo Wee Raymond Chiong (Curtin University Malaysia, Malaysia); Lenin Gopal (University of Southampton Malaysia, Malaysia); Filbert H. Juwono (Xi'an Jiaotong - Liverpool University, China); King Hann Lim (Curtin University Malaysia, Malaysia); Huo-Chong Ling (RMIT University Vietnam, Vietnam)

With the introduction of 5G technologies that promise lower latency, more reliable, and higher network capacity, many researchers have proposed to integrate various 5G technologies such as millimeter waves, multi-radio access technology, and reconfigurable intelligent surfaces (RIS) with vehicle-to-everything (V2X). RIS is first introduced as an arranged array of reflectors that can be independently and dynamically reconfigured to adjust the signal propagation direction in a favorable way. With RIS, the overall performance of the wireless communication system can be improved. However, it is unclear whether RIS can be directly applied in areas of high mobility, specifically within vehicular traffic. This paper presents an RIS-aided network model that can be utilized to enhance the V2X communication networks with multiple vehicles (V2V) and infrastructure (V2I) links. A Soft-Actor Critic Deep Reinforcement Learning (SAC-DRL) approach is proposed to optimize the network model to achieve the stringent quality-of-service (QoS) requirements. Subsequently, numerical simulations were performed to validate the performance of the proposed SAC-DRL method.

#7 Enhanced-ZNN-Based Fixed-Time Model-Free Adaptive Control of Robotic Manipulator

Jin Siang Yap, Ibrahim Bako Abdulhamid and Muhammad Nasiruddin Mahyuddin (Universiti Sains Malaysia, Malaysia); Siang Kok Chia (Sandisk, Malaysia)

This paper presents an enhanced Zeroing Neural Network (ZNN) that is incorporated in the adaptive update law of a fixed-time model-free adaptive control scheme which improves the tracking performance of robotic manipulator. Although there are many research related to precise trajectory tracking control of robot manipulator using conventional model-based methods, it often requires high-fidelity model of the robot system, which is essential for accurately capturing its nonlinear dynamics. However, acquiring such precise models can be challenging due to parameter uncertainties, external disturbances, and unmodeled dynamics, that will lead to potential degradation in control performance or even instability when assumptions are inaccurate. To overcome this limitation, model-free adaptive control (MFAC) is gaining prominence because it directly estimates the unknown robot dynamics from the input and feedback data without the necessity for prior knowledge of robot parameters. The proposed control scheme is an extension of our previous work, which improves the tracking performance compared to the prior approach. A rigorous Lyapunov stability analysis, complemented by extensive simulations, was conducted to validate its effectiveness and demonstrates superiority in achieving precise and stable control.

#8 Fixed-Time Consensus Tracking for Nonlinear Second-Order Multi-Agent Systems with Communication Delays

Ibrahim Bako Abdulhamid, Jin Siang Yap and Muhammad Nasiruddin Mahyuddin (Universiti Sains Malaysia, Malaysia)

This study proposes a novel fixed-time tracking control methodology tailored for second-order multi-agent systems (MASs) operating over directed communication networks and experiencing communication delays. Recent studies have explored fixed-time consensus in MASs with nonlinear dynamics and communication delays. The control design employs a Lyapunov-Krasovskii (L-K) functional framework, integrating Hölder's inequality and a Lipschitz-like condition to ensure system stability. Notably, this approach eliminates the dependence on Linear Matrix Inequalities (LMIs), which are prevalent in traditional control strategies but often lead to increased computational complexity. By circumventing LMIs, the proposed method enhances computational efficiency, making it suitable for large-scale MAS applications. The control protocol guarantees that all agents achieve consensus tracking of the leader's trajectory within a predetermined fixed time, regardless of initial conditions. Theoretical analyses are substantiated through numerical simulations, demonstrating the effectiveness and robustness of the proposed scheme in achieving rapid convergence and maintaining stability in the presence of communication delays.

#9 Learning Alphabets, Seeing Worlds: Enhancing Early Childhood Education Using AR-Powered Handwritten Alphabet Recognition

Sajeena S and Ravanam Durga Venkata Satya Avinash (National Institute of Technology Calicut, India); Chinthakommadinne Vinay Kumar and Konduru Dhanush (NIT Calicut, India); Santosh Kumar Behera (National Institute of Technology Calicut, India); Ajaya Dash (IIIT Bhubaneswar, India)

The purpose of this work is to create an augmented reality (AR) application for supporting primary education that accelerates early literacy through interactive and immersive experiences. Since the early days of AR research, fiducial markers have acted as the backbone of AR applications, on top of which virtual computer-generated 3D objects are superimposed. The popular fiducial markers are printed characters or symbols surrounded by a thick black border in order to make the marker detection and identification task easier. During the testing of an AR alphabet learning kit with printed markers, we found that the learning engagement would have been significantly higher if the printed fiducial markers had been swapped out for the children's handwritten characters on a plane surface. The idea presented in this article is to build a dynamic AR learning space where 3D models corresponding to handwritten alphabet letters are projected, turning conventional learning into a visually engaging experience. Also, to enhance the experience further, when a letter is chosen or touched by a child, the system automatically projects a related 3D object in AR, such as an apple for the letter ‘A' or a ball for ‘B'. This spatial and visual correspondence of letters with actual objects aids in strengthening recognition, memory, and understanding abilities among young learners. To mitigate this, we have trained a custom dataset of handwritten alphabets for object detection and identification. After successful identification of the alphabet, the corresponding 3D objects are superimposed over the alphabet with respect to the pose in an AR setting. The application exploits AR technology through software like Unity and AR development kits to incorporate 3D content into the real world via mobile or tablet devices. The AR application covers a wide range of learning styles and keeps young learners actively engaged through the integration of visual, auditory, and tactile interaction. This work not only fills the chasm between physical interaction and visual learning but also conforms to current pedagogic practices that favor experiential learning. This is a beneficial teaching aid for parents and teachers by providing a fun yet practical means of promoting early literacy skills in children. The end goal is to revolutionize early learning by making education both fun and educational, hence encouraging curiosity and motivation in the minds of children.

#10 Computational and Physiological Perspective of Gender Based Pulmonary Fibrosis Severity

Anjana K and Selvaganesan, N (Indian Institute of Space Science and Technology, India)

Pulmonary fibrosis is a progressive interstitial lung disorder characterized by thickening of alveolar capillary membrane, resulting in reduced gas exchange. Although these disease has been extensively examined, the impact of gender on the severity and course of pulmonary fibrosis remains inadequately investigated. This work offers a computational simulation of alveolar membrane thickening to assess its effect on pulmonary gas exchange, emphasizing gender specific physiological variations. The alveolar compliance was decreased from 30% to 47% by varying alveolar membrane thickness in order to assess the severity of pulmonary fibrosis. In mild pulmonary fibrosis, the results show that gender does not substantially influence gas exchange parameters, such as oxygen saturation levels. However, in the case of severe pulmonary fibrosis the oxygen saturation of the male patient is reduced to below 88% indicating the necessity for oxygen therapy. The simulation results suggest that pulmonary fibrosis results in earlier and more severe gas exchange limits in males compared to females at equal membrane thickening percentages due to anatomical and functional differences.

#11 Computer Vision-Guided Humans and Animals Detection in Aquatic Disaster Zones

Raunit Maurya, Sajeena S and Santosh Kumar Behera (National Institute of Technology Calicut, India); Ajaya Dash (IIIT Bhubaneswar, India)

In geographically diverse countries like India, where floods occur frequently, recurring natural disasters pose a significant challenge to timely and effective disaster response. This work addresses the critical need for surveillance of flood-affected regions, with a focus on detecting the presence of humans and animals in submerged areas. Leveraging advancements in computer vision and artificial intelligence, the proposed system aims to enhance rescue operations through rapid and accurate life detection in aquatic disaster zones. By integrating object detection and semantic segmentation techniques, the solution provides a practical and scalable approach for early identification and improved situational awareness, thereby enabling prompt and effective action by emergency responders. This article at first addresses a dataset creation due to the lack of annotated dataset availability involving animals in waterlogged environments. The dataset is created using real-world videos and AI-generated images simulating various flood conditions. Further, an animal detection system is proposed based on a merged vision pipeline, where the detection is achieved by YOLOv8 supported by semantic segmentation using U-Net. The results of YOLOv8 and U-Net are combined to create the robust context-aware interpretation such as whether the detected object could be mapped to a flood area located in or near submerged regions. The proposed merged pipeline can achieve the purpose with mAP@0.5 as 0.81 and recall as 0.61. The method is useful to reduce irrelevant object detection and provide a meaningful output for use in rescue planning.

#12 Overcoming Data Scarcity in Load Forecasting Using Time Series Transfer Learning

Jayson C Jueco, Wilen Melsedec O. Narvios, Ferdinand F. Batayola, Rafran P de Villa, Cheryll Ann C Villamor, Gilbert Silagpo and Maria Gemel B Palconit (Cebu Technological University, Philippines)

Data scarcity introduces variabilities that compromise forecasting accuracy and reliability. In energy systems, precise load forecasting is vital for grid optimization, resource management, and outage prevention, supporting operational and economic stability. However, most models perform poorly under short time series, non-stationary patterns, and abrupt demand shifts. This paper employs Gramian Angular Field (GAF) to encode load data as images and develops a hybrid Autoencoder-Stacked LSTM network. The Autoencoder extracts latent temporal features from load snapshots, while the Stacked LSTM forecasts day-ahead hourly demand. A ten-year hourly demand dataset from the Philippine grid is aggregated, with missing values zero-filled and outliers preserved. The Autoencoder compresses snapshots into a latent space for feature representation, and transfer learning enables LSTM models trained on data-rich sub-grids to forecast in data-scarce regions. Performance, evaluated via MAE, MSE, MAPE, and RMSE, remains consistent across sub-grids. ANOVA results $(F < 0.002, p > 0.94)$ confirm no significant performance differences, validating the model's robustness and generalizability. Future work focuses on integrating attention, multimodal data, and real-time deployment to enhance forecasting accuracy and grid adaptability.

#13 Multi-Criteria Prioritization and Clustering of Stochastic on-Road Vehicle CO2 Emissions Based on Road Slope, Speed, and Acceleration Using K-Means, PCA, and Fuzzy AHP

Maria Gemel B Palconit and Jayson C Jueco (Cebu Technological University, Philippines)

The carbon emissions from public utility vehicles (PUVs) in the Philippines are projected to contribute up to 80% of vehicle kilometers traveled and become a major source of emissions by 2035 without intervention. Recognizing the stochastic and condition-dependent nature of vehicular emissions, the research aims to identify and prioritize the factors influencing (CO_2) emissions using advanced analytical techniques. Emissions data were clustered using K-Means to identify distinct operational states, while Principal Component Analysis (PCA) reduced dimensionality and revealed key influencing factors. The Fuzzy Analytic Hierarchy Process (FAHP) was then applied to prioritize these factors, considering environmental, technical, and economic implications. Results showed a positive relationship between road slope and (CO_2) emissions (r = 0.3111) and an opposite relationship with respect to speed (r = -0.3078), while acceleration had a minor positive effect (r = 0.1330). FAHP assigned the highest weight to (CO_2) emissions (0.3589), followed by slope (0.3121) and speed (0.2972). Cluster analysis highlighted Cluster 0 as the most emission-intensive operational state, with an average (CO_2) level of 31394.25 g/km, moderate speed, and uphill road conditions. The integration of PCA and FAHP revealed that (CO_2) emissions and slope together accounted for over 67% of the emission profile importance. These insights inform the development of emission control strategies, eco-driving guidelines, and data-driven transport policies.

#14 Evaluating Factors Affecting Headed Reinforcement Performance: a Case for External Beam-Column Joints for Seismic Resistant and Sustainable Structures

Hana Astrid R. Canseco-Tuñacao (Cebu Technological University, Philippines)

Approaches in studying the seismic performance of structural members are through the conduct of rigorous and costly experimental work with highly mechanistic models to explain behaviors. For the case of exterior beam-column joints with headed reinforcement, previous studies have identified factors that contribute to the performance of the member, and these have been included in code provisions. From a design perspective, there is missing information on the prioritization of factors. This study conducted a statistical approach using multiple linear regression, correlation, and dominance analysis to evaluate the significance and contribution of five key factors based on an adopted database. These factors are (1) Axial load applied (Axial), (2) ratio of the joint transverse reinforcement supplied parallel to the applied shear load to the required (ρ), (3) minimum between clear bar spacing between bars in a layer and spacing of bars between layer (c12), (4) clear cover to the bar (c3), and (5) column moment ratio (Mtest/2MNC). Results show that for specimens with good performance (Category I), c12, ρ, and c13 are significant and dominant factors due to the combined action of bonding along the reinforcing bar and confinement. For specimens with poor performance (Category II), c12 and Axial are significant and dominant factors. Axial confinement improves performance through bonding by delaying the development of cracks in the joint core. Addressing the failure of beam-column joints improves their seismic performance, resulting in the structure's overall durability and longevity.

#15 Hybrid Renewable System Optimization with Backup Integration and Sensitivity Analysis

Mohammad Reza Maghami (Asia Pacific University of Technology & Innovation, Malaysia); Jacqueline Lukose and Ka Fei Thang (Asia Pacific University of Technology and Innovation, Malaysia); Arthur G.O. Mutambara (University of Johannesburg, South Africa)

As solar and wind energy gain global traction for their sustainability, their intermittent nature and reliance on weather conditions pose serious reliability challenges. Additionally, the high upfront costs of renewable energy systems hinder their broader deployment, particularly in off-grid and rural areas. Many existing hybrid energy systems (HES) struggle to balance affordability with consistent power delivery, especially under fluctuating load and supply conditions. This underscores the need for optimized HES designs that can reduce power outages while keeping costs manageable. This study introduces an innovative hybrid energy system that combines solar and wind power with battery storage, enhanced by Proton Exchange Membrane Fuel Cells (PEMFCs) and diesel generators as backup sources. To improve energy capture from renewables, a Maximum Power Point (MPP) tracking technique is employed, boosting overall system efficiency. The core advancement lies in integrating backup solutions and power optimization within a unified, multi-objective framework. Four operational scenarios are analyzed: a basic HES, HES with PEMFC backup, HES with diesel generator backup, and HES with MPP tracking. The system is tested in a rural community in South Khorasan Province, Iran. Optimization results show a reduced Net Present Cost (NPC) of $118,800 and a minimal Loss of Power Supply (LPS) of 50 kW. Sensitivity analysis highlights the impact of battery state-of-charge violations, unmet energy demand, and excess generation, reinforcing the importance of adaptive and resilient system design.

#16 PlantEase: an Arduino-Powered Solution for Automating Seed Crop Cultivation and Planting

Joan Katherine N Romasanta (National University, Philippines & NU Dasmarinas, Philippines)

In the Philippines, traditional farming methods are still used. Modern techniques like drip irrigation, controlled-release fertilizers, and digital tools are helping farmers improve efficiency, reduce waste, and increase profits. This research focuses on developing a device to automate crop cultivation and harvesting, offering an alternative to traditional practices and addressing the need for innovative solutions in agriculture like the Philippines' farming context. The system uses an LCD to show the soil moisture levels in real time, two servo motors for automated soil cultivation, and a micro servo for regulated seed dispensing. Furthermore, an ultrasonic sensor tracks the height and development of plants, offering useful information for improving crop management. All servos are synchronized by an infrared (IR) controller, which guarantees precise and efficient planting task execution. The project was evaluated by experts and attain the score of 3.88 which is described as Excellent. This means that the project is a flexible and scalable solution for to decrease human labor, improve planting accuracy, and promote sustainable agricultural methods.

#17 A Weighted Vision Transformer-Based Multi-Task Learning Framework for Predicting ADAS-Cog Scores

Nur Amirah Abd Hamid (Universiti Brunei Darussalam, Brunei Darussalam); Ibrahim Shapiai (Malaysia-Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Malaysia); Daphne Teck Ching Lai (Universiti Brunei Darussalam, Brunei Darussalam)

Prognostic modeling is essential for forecasting future clinical scores and enabling early detection of Alzheimer's disease (AD). While most existing methods focus on predicting the ADAS-Cog global score, they often overlook the predictive value of its 13 sub-scores, which reflect distinct cognitive domains. Some sub-scores may exert greater influence on determining global scores. Assigning higher loss weights to these clinically meaningful sub-scores can guide the model to focus on more relevant cognitive domains, enhancing both predictive accuracy and interpretability. In this study, we propose a weighted Vision Transformer (ViT)-based multi-task learning (MTL) framework to jointly predict the ADAS-Cog global score using baseline MRI scans and its 13 sub-scores at Month 24. Our framework integrates ViT as a feature extractor and systematically investigates the impact of sub-score-specific loss weighting on model performance. Results show that our proposed weighting strategies are group-dependent: strong weighting improves performance for MCI subjects with more heterogeneous MRI patterns, while moderate weighting is more effective for CN subjects with lower variability. Our findings suggest that uniform weighting underutilizes key sub-scores and limits generalization. The proposed framework offers a flexible, interpretable approach to AD prognosis using end-to-end MRI-based learning. (Github repo link will be provided after review)

Thursday, October 30 2:30 - 4:00

Track C8F1 CCI 8.1: Computing & Computational Intelligence (CCI) 8.1

Room: F1. Sipadan I

Chair: Min Keng Tan (Universiti Malaysia Sabah & Modelling, Simulation & Computing Laboratory, Malaysia)

2:30 RainGAN-Kathmandu: a Generative Adversarial Framework for Synthetic Rainfall Augmentation in Urban Road Scene Datasets

Nitesh Kumar Shah (Indian Institute of Information Technology, Allahabad, India); Gadde Jahnavi (IIIT Allahabad, India); Navjot Singh (Indian Institute of Information Technology Allahabad, India); Chandra Prakash Maurya (IIIT Allahabad, India); Satish Singh (IIT Allahabad, India)

Urban roads face major challenges during rainfall, impacting visibility and road texture. Rain degrades the performance of vision-based systems in traffic safety and monitoring. Real-world rainy road datasets are scarce and often lack diversity. This limits the training of models for image translation under rainy conditions. Effective rainy image synthesis is essential for advancing robust autonomous driving systems. This paper introduces a novel methodology to bridge this gap by generating synthetic datasets simulating adverse weather conditions, particularly rainfall, using Generative Adversarial Networks (GANs). We introduce a newly curated dataset of Kathmandu road scenes to provide diverse, real-world clear-weather imagery. Leveraging these datasets, we employ advanced deep learning techniques to synthesize high-quality rainy road scenes. Generated outputs are evaluated using Fréchet Inception Distance (FID) and Learned Perceptual Image Patch Similarity (LPIPS) metrics to ensure realism and perceptual fidelity. The paper outlines the data creation pipeline, model implementation, evaluation strategy, and broader implications, offering a robust framework for weather-affected scene synthesis and road safety research.

2:45 GTCGAN: an Unsupervised Approach for Single Image Deraining

Debesh Kumar Shandilya (IIIT Allahabad, India); Navjot Singh (Indian Institute of Information Technology Allahabad, India)

Single image deraining is a challenging task even today due to the unavailability of real paired datasets. Though many supervised models have been developed, they are trained on paired synthetic datasets, which cannot map real rainy conditions beyond a certain extent, which limits the capability of the supervised models to perform equally well on the real test datasets. The proposed model, Guided TransCycleGAN (GTCGAN), is based on CycleGAN, which uses an unpaired real dataset for training. Further, our model uses both CNN and attention block to exploit local and global relationships in the image. Apart from this, our model uses two extra discriminators to guide generators to generate clean images for the corresponding rainy images. GTCGAN's performance is enhanced by a multi-objective loss function combining edge loss for visual quality and Structural Similarity Index Measure loss for structural integrity. Our model outperformed the state-of-the-art in terms of average PSNR and average SSIM on three publicly available datasets.

3:00 Functional Connectivity-Driven Diagnosis of ADHD and ASD via Transfer Learning on Rs-fMRI Data

Khushi Jain, Akanksha Upadhyay and Vaishali Chawla (University of Delhi, India); Navjot Singh (Indian Institute of Information Technology Allahabad, India); Bharti Rana (University of Delhi, India)

Neurodevelopmental conditions such as Autism Spectrum Disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD) alter functional connectivity (FC) among the brain regions. ADHD and ASD affect 5-6% and 1-2% of the population, respectively and hence need immediate attention to develop automated differential diagnosis. This work proposes an automated diagnostic system for ADHD and ASD by investigating FC among brain regions. We used publicly available resting-state functional Magnetic Resonance Imaging (rs-fMRI) data for ADHD, ASD and age-matched healthy (HC) subjects. The rs-fMRI data helps to analyse the temporal activity in the brain, and computing FC using time-series data helps to capture the temporal correlation among regions. We used five diverse FC measures and performed three classification tasks: ASD vs. ADHD, ASD vs. HC and ADHD vs. HC. Further, we fine-tuned the pre-trained VGG19 model to develop a decision model. The best accuracy of 99.09% for ASD vs. ADHD, 78.18% for ADHD vs. HC and 65.46% for ASD vs. HC demonstrates the efficacy of the proposed method. The disrupted regions are also identified using SHapley Additive exPlanations (SHAP), an explainable artificial intelligence method, that are in accordance with the literature. The work demonstrates the capability of deep learning in analysing neuroimaging data for clinical and research applications.

3:15 Multi-Scale Temporal Attention Convolutional Neural Network for Sleep Stage Classification Using Single-Channel EEG

Aikendrajit Ningthoujam and Shaik Rafi Ahamed (Indian Institute of Technology Guwahati, India)

Accurate and efficient classification of sleep stages is essential for advancing widespread sleep monitoring solutions. This study presents a novel multi-scale temporal attention Convolutional Neural Network (CNN) tailored for single-channel EEG (Fpz-Cz) sleep stage analysis. Our architecture innovatively combines temporal attention mechanisms across multiple scales within a convolutional neural network framework. By emphasizing temporal relationships in the EEG signal across varied receptive fields, the model effectively captures discriminative features without the computational overhead of channel-wise operations common in multi-channel systems. This multi-scale strategy enables the network to autonomously identify and prioritize significant patterns, ranging from short-term transients to longer rhythmic structures. The proposed design strikes an optimal balance between performance and computational efficiency compared to existing deep learning approaches. Evaluated on the Sleep-EDF-20 dataset, our model achieves a competitive accuracy of 76.46% and a Cohen's kappa κ of 0.69, with approximately 141.5k parameters. This research highlights the potential of multi-scale temporal attention in CNNs for accurate and computationally efficient single-channel EEG sleep stage classification, paving the way for future advancements and practical applications.

3:30 Life Data Analysis (LDA) Using Weibull Distribution Method for Determination of Reliability for HPSV and LED Street Light

Ir. Ts. Nor Diana Ruszaini (TNB Research Sdn Bhd, Malaysia); Khairuddin Abdullah and Asnawi Mohd Busrah (TNB Research, Malaysia)

This study applies Life Data Analysis (LDA), particularly Weibull distribution modeling, to evaluate and compare the reliability, failure patterns, and operational lifespans of High-Pressure Sodium Vapor (HPSV) and Light Emitting Diode (LED) streetlights using real field data. Although LED streetlight technology gained international traction around 2012, its long-term performance was not well documented at the time. In Malaysia, Tenaga Nasional Berhad (TNB) began widespread deployment of LED streetlights in 2016. This study utilizes actual operational and failure data from thousands of units to conduct a statistically rigorous analysis. LDA is a methodical reliability engineering approach that uses time-to-failure data to predict product life and identify failure trends. The Weibull distribution, a versatile tool in LDA, was employed to model failure behaviors of both HPSV and LED lights. In addition to failure modeling, the Mean Time to Failure (MTTF) was calculated for both lighting technologies, serving as a fundamental indicator of reliability. The results demonstrated that LED streetlights possess a significantly higher MTTF compared to HPSV units, confirming their longer lifespan and superior durability in outdoor conditions. These findings reinforce the reliability advantages of LED systems and align with their design intent of long-term, low-maintenance operation.

3:45 S-LIME: an Explainable SVM-Based Framework for Human Activity Recognition Using LIME

Shaharier Kabir (American International University-Bangladesh, Bangladesh); Muhammad Masud Karim (Bangladesh University of Engineering and Technology, Bangladesh)

Human Activity Recognition (HAR) has emerged as a pivotal application in healthcare, assistive technology, and smart environments, leveraging wearable sensor data to classify human actions. While Support Vector Machines (SVMs) are widely used for HAR due to their robustness in handling high-dimensional data, their lack of interpretability restricts their deployment in real-world applications requiring transparency and trust. To bridge this gap, we propose S-LIME (SVM-LIME), an explainable AI (XAI) framework that integrates Local Interpretable Model-Agnostic Explanations (LIME) with SVMs to enhance model interpretability in HAR systems. S-LIME enables domain experts, medical practitioners, and end-users to understand the feature contributions that drive SVM-based activity classifications, making AI-driven HAR more transparent and accountable. We evaluate S-LIME on benchmark HAR datasets, demonstrating its ability to generate human-interpretable insights while maintaining high classification accuracy. The framework provides instance-level feature explanations, highlighting the key sensor signals influencing activity classification. Additionally, we introduce LIME-based feature ranking, which identifies and optimises critical sensor signals, further improving model performance. Our findings show that S-LIME achieves state-of-the-art accuracy while providing an interpretable decision-making process, paving the way for reliable, transparent, and ethically sound HAR models.

4:00 Predicting Fall Risk in Parkinson's Patients Through Sequential Gait Analysis and Machine Learning

Gaurav Sharma, G (Bennett University, India); Nehvanshika Nehvanshika, Niharika Anand and Gaurisha Singh (IIIT Lucknow, India)

Falls pose a critical threat for patients with Parkinson's disease, as difficulties in walking and balance greatly increase the likelihood of falling. This study aims to improve fall risk prediction accuracy and computational efficiency using wearable sensor data and advanced learning models. We analyze real-world gait data collected via inertial measurement units and implement both classical machine learning (ML) techniques and deep learning frameworks aimed at recognizing those susceptible to falls. Feature extraction strategies focusing on gait speed, stride variability, and symmetry measures were applied to enhance model performance and reduce dimensionality. Among classical approaches, a Random Forest classifier optimized with Recursive Feature Elimination (RFE) and XGBoost achieved a balanced accuracy of 77%, with sensitivity and specificity of 70% and 84%, respectively. A long-short-term memory (LSTM) model that uses temporal stride sequences significantly outperformed traditional models, achieving balanced precision of 95.97%, sensitivity of 93.29% and specificity of 98.65%. Comparative analysis with baseline literature demonstrated substantial performance gains, highlighting the ability of time-sensitive deep learning methods to predict the risk of falls in PD. The findings underscore the feasibility of deploying robust, real-time predictive systems in clinical and home-monitoring environments, with future work directed towards multimodal integration and scalable deployment.

Thursday, October 30 2:30 - 4:00

Track C8F6 CCI 8.2: Computing & Computational Intelligence (CCI) 8.2

Room: F6. 505 Sepilok

Chair: Pei Yee Chin (Universiti Malaysia Sabah, Malaysia)

2:30 Performance Analysis of Multimodal Fusion Techniques for Predicting Student Engagement States

Deepika Suranjini Silva and Nadeeshani Wickramage (Sri Lanka Institute of Information Technology, Sri Lanka); Jayakody Arachchilage Don Chaminda Anuradha Jayakody (Curtin University Technology, Sri Lanka & Sri Lanka Institute of Information Technology, Sri Lanka); Pradeep Abeygunawardhana (Sri Lankan Institute of Information Technology (SLIIT), Sri Lanka)

Effective monitoring of student engagement is vital for enhancing learning outcomes, especially in digital and hybrid classroom settings where traditional physical cues such as eye contact and posture are often limited. This study investigates the performance of machine learning models combined with multimodal data fusion techniques to predict student engagement levels using the Emotional Monitoring Dataset. The dataset integrates behavioral (e.g. facial expressions), physiological (e.g. heart rate, skin conductance), and environmental (e.g. ambient noise, lighting) signals, all labeled with discrete engagement states: Highly Engaged, Moderately Engaged, and Disengaged. Three widely used machine learning models, Random Forest (RF), XGBoost, and Multilayer Perceptron (MLP) were evaluated using both early and late fusion strategies. Results demonstrate that the late fusion approach consistently outperformed early fusion across all models, with XGBoost achieving the highest accuracy (R² = 0.99999) and lowest error (MSE = 3.88e−07), indicating near-perfect prediction. Random Forest also yielded strong results, while MLP showed competitive performance under the late fusion configuration. These findings highlight the effectiveness of decision-level integration in handling multimodal data and support the use of ensemble and deep learning approaches for developing intelligent, real-time attention monitoring systems in education. The proposed methodology offers promising applications in personalized learning and emotion-aware educational technologies.

2:45 Real-Time Road Damage Classification and Severity Detection System

Victor Sebastian D. Bondoc, Efren Jr. D. Pastores, Julius Nikolai D. Bernardo, Gary Clyde T. Rabe, Renato T. Panis Jr and Jevon A. Silvano (Technological University of the Philippines, Philippines); Immanuel Jose C Valencia and Ira Valenzuela Estropia (De La Salle University, Philippines); Ryan Reyes (Technological University of the Philippines, Philippines); Lean Karlo S. Tolentino (Technological University of the Philippines, Philippines & Mapua University, Philippines); Jessica S. Velasco (Technological University of the Philippines, Philippines); Mark P Melegrito (Technological University of the Philippines Manila, Philippines)

In the Philippines, road pavement damage poses significant threats to road safety, often leading to accidents, vehicular damage, and increased maintenance costs. Typically, actual road surveying is conducted when inspecting road pavement damage, a traditional method that is time-consuming when generating reports about the surveyed road pavement. Requiring labor, expenses, and subjective evaluations, resulting in inaccuracies and inefficiencies in assessing road conditions, which delays maintenance and repair. The main objective of this study is to develop a real-time road damage classification and severity detection using YOLOv8. Using a moving vehicle, a high-resolution camera is mounted, capturing images of road pavement damages in real-time, processed by the NVIDIA Jetson Nano, YOLOv8 as the deep-learning model classifying road pavement damage classes: potholes, cracks, alligator cracks, pumping, and depression, determining it identified severity levels based on the standards and guidelines set by the Department of Public Works and Highways (DPWH). A VK-162 GPS module is incorporated to geotag each detected road damage by recording its latitude and longitude coordinates in real-time for accurate location mapping and detailed reporting of road conditions. The system was evaluated based on its accuracy of detection and the tagged location. The findings of the study suggest that the system is a viable, low-cost, and scalable assessment tool to conduct preliminary assessments of road conditions, providing significant opportunities to reduce manual labor while improving the speed and efficiency of data collection and asset and infrastructure maintenance planning.

3:00 Predicting Hourly Electricity Demand Using Fuzzy Logic: Integrating Environmental Factors for Accurate Forecasting

Ahmed Intekhab Rohan (Islamic University of Technology, Gazipur, Bangladesh); Md Ismail Hossain (American International University Bangladesh, Bangladesh); Hasanur Zaman Anonto and Abu Shufian (American International University-Bangladesh, Bangladesh); Mohammad Shah Paran (Lamar University, USA); Toriqul Islam (American International University-Bangladesh, Bangladesh); Md Shakhawat Hossain (Lamar University, USA)

This study investigates the distribution of electricity demand is distributed over temperature, humidity, wind speed and seasonal variations. Using data covering the timeframe of five years (2021-2025), the objective behind this research is to understand the interdependent nature of the electric demand and different external conditions of the environment. Preliminary analysis indicates that the demand for electricity varies based on the time of the day, the season and the day of the week. Peak demand is at noon in winter at 27,000 units and declines to 22,000 units during post-midnight. Electric demand positively correlated with temperature and humidity, where a notable increase in demand was detected when temperature increased from 5°C to 8°C and when humidity increased from 60% to 66% The wind speed variation of the demand shows that the demand is less than 2m/s and higher than 5m/s. The impact of the seasonal change is also studied in this paper and the results show that the demand is higher in the winter and summer due to the heating and cooling need respectively while the demand is lower in the spring and autumn. The findings deliver specific intelligence on optimizing how energy management strategies can best be employed, specifically for ensuring that demand drops are anticipated ahead of time around peak seasons and extreme weather.

3:15 MapReduce and K-Means Clustering Method for Long Text Summarization on Large Language Model

Moh. Rosy haqqy Aminy (Sepuluh Nopember Institute of Technology, Indonesia); Diana Purwitasari, Dwi Sunaryono, Ilham Gurat Adillion, Dini Adni Navastara and Bilqis Amaliah (Institut Teknologi Sepuluh Nopember, Indonesia); Hilmil Pradana (Sepuluh November Institute of Technology, Indonesia); Yoga Yustiawan (Pusan National University, Korea (South))

The rapid growth of digital information has resulted in an explosion in the volume of online text documents, ranging from news articles, and financial reports, to scientific papers. This condition triggers the need for an automated summarization system that can present important information in a concise and easy-to-understand manner. The application of Large Language Model (LLM) based summarization models on long documents still faces limited input length challenges. This paper proposes a long text summarization method based on LLM by combining MapReduce and K-means Clustering. Qwen2.5-7B model Instruct is used with Low-Rank Adaptation-based instruction fine-tuning technique. Long documents are divided into chunks, then converted into embedding and clustered using the K-Means algorithm. The clustering results are used to select the most relevant information representation, which is then summarized at the Map stage. The partial summaries are then merged and re-summarized at the Reduce stage to produce one final summary. This approach is capable of processing documents that exceed the token limit of LLM. To evaluate the summarization results, the ROUGE (Recall-Oriented Understudy for Gisting Evaluation) metric is used. An experimental evaluation was conducted on banking sector documents in the BRI dataset and showed that the MapReduce and Clustering method significantly improved performance over the direct truncation approach. Our approach achieves ROUGE-1, ROUGE-2, and ROUGE-L scores of 0.416, 0.118, and 0.219 respectively compared to direct truncation scores of 0.320, 0.090, and 0.168.

3:30 Fuzzy Rule-Based and Multinomial Logistic Regression-Based Models on Selecting Alternative Routes for Maysan Road, Valenzuela

John Lemar M Tirao (Mapua University, Philippines); Jocelyn Buluran (Mapúa University, Philippines); Jordan Velasco (Pamantasan ng Lungsod ng Valenzuela, Philippines)

Maysan Road, a critical national highway in Valenzuela, National Capital Region, Philippines, has long suffered from traffic congestion due to its corridor role in linking MacArthur Highway and General Luis Street, necessitating an effective route management solution. With the rapid increase in vehicular volume brought by urban and economic expansions, this research aimed to propose a traffic management scheme using a fuzzy rule-based model to recommend proper alternative route selection by integrating physical and traffic characteristics of the road network with road user perceptions. A three-phase methodology was employed: (1) data gathering of route conditions and road user perceptions survey, (2) development of fuzzy-rule-based (FRB) system and multinomial logistic regression (MNL) models for route selection, and (3) validation through statistical testing to determine the most efficient route. Parameters such as weather, socio-economic activities, time of the day, day of the week, and level of service were used as inputs in the models. The FRB model tended to emphasize Route 3 under congested traffic and good weather conditions. The MNL was fitted and confirmed the statistical adequacy of all input variables, with varying significance across routes (McFadden's pseudo R-squared value of 0.2623); model validation also showed Route 3 has the highest average predicted probability (21.09%) followed closely by Routes 4 and 1. This research concludes that the hybrid modeling approach that combines statistical and rule-based inference provides an interpretable framework for localized traffic management.

3:45 An Advanced Hybrid Strategy for Detecting Fraud in Mobile Money Services

Kaung Wai Thar and Thinn Thinn Wai (University of Information Technology, Myanmar)

Mobile payment systems are growing in popularity as more people use smartphones, which also attracts fraudsters. As a result, mobile money fraud is increasing, especially in developing countries. However, security concerns in mobile money services have gained attention because weak security has kept many customers away. Fraud is not a new problem, but it still costs the global economy billions of dollars each year. Financial transaction data, including mobile money transactions, are mostly labeled as legitimate, making fraud detection difficult. Researchers have developed various fraud detection methods using various machine learning techniques like LGBM, random forests, SVM, deep learning, neural networks, XGBoost, and logistic regression have been tested to identify fraudulent transactions through data preparation, feature engineering, and model building. However, machine learning models trained on imbalanced data tend to be biased toward legitimate transactions, making them unreliable. This study aims to use machine learning classifiers to detect fraud in mobile money transfers. The data comes from real-time transactions that mimic common fraud schemes. This research explores the development and evaluation of ML models for fraud detection and proposes a solution using a hybrid ML approach with effective hyperparameter tuning, and the Synthetic Minority Over-sampling Technique (SMOTE). The results show that the proposed approach improves accuracy by addressing data imbalance. The findings contribute to the development of better fraud detection systems for mobile money services.