BOOKS
Edited Book: Human Brain inspired Lifelong Learning, World Scientific, 2024.
Monograph: S. Suresh, N. Sundararajan and R. Savitha, Supervised Learning with Complex-valued Neural Networks, Studies in Computational Intelligence (Springer-Verlag, Berlin), ISBN: 978-3-642-29490-7, 2013.
Book Chapter: R. Savitha, S. Suresh and N. Sundararajan, A Projection based Sequential Learning Algorithm for a Fully complex-valued Relaxation Network, Complex-valued neural networks: Advances and applications, IEEE Press CI Book Series, 2012.
M. M. Anwar, M. Pratama, S. Ramasamy, L. Liu, H. Habibullah, and R. Kowalczyk, “Vision and language synergy for rehearsal free continual learning,” in International Conference of Learning Representations (ICLR), 2025.
J. Hu et al., “Prompt-based spatio-temporal Graph Transfer Learning,” in ACM Conference on Information and Knowledge Management (CIKM), 2024, pp. 890–899.
M. M. Anwar, M. Pratama, R. Savitha, L. Liu, and R. Kowalczyk, “Unsupervised few-shot continual learning for remote sensing image scene classification,” IEEE Transactions on Geoscience and Remote Sensing, 2024.
W. Weng, M. Pratama, J. Zhang, C. Chen, E. Yapp, and R. Savitha, “Cross-domain continual learning via CLAMP,” Information Sciences, vol. 676, p. 120813, 2024.
X. Li, S. Ramasamy, A. Ambikapathi, S. Sundaram, and Fayek, Haytham M, “Towards human brain inspired lifelong learning,” World Scientific, 2024.
Z. Qiao, X. H. Pham, S. Ramasamy, X. Jiang, E. Kayacan, and A. Sarabakha, “Continual learning for robust gate detection under dynamic lighting in autonomous drone racing,” in IEEE International Joint Conference on Neural Networks (IJCNN), 2024, pp. 1–8.
P. R. Singh, S. Gopalakrishnan, S. Ramasamy, and A. Ambikapathi, “Task-agnostic inference using Base–Child classifiers,” in Human Brain Inspired Lifelong Learning, World Scientific Publishing, 2024, pp. 123–161.
S. Gopalakrishnan, P. R. Singh, Fayek, Haytham M, S. Ramasamy, and A. Ambikapathi, “Flashcards for knowledge capture and replay,” in Towards Human Brain Inspired Lifelong Learning, World Scientific Publishing, 2024, pp. 163–196.
S. Ramasamy, A. Ambikapathi, and K. Rajaraman, “Growing RBM on the fly for unsupervised representation toward classification and regression,” in Towards Human Brain Inspired Lifelong Learning, World Scientific Publishing, 2024, pp. 25–50.
C. Sze and S. Ramasamy, “Architectural Adaptation and Regularization of Attention Networks for Incremental Knowledge Tracing,” in International Conference on Learning Analytics & Knowledge (LAK), 2024, pp. 757–762.
M. M. Anwar, M. Pratama, S. Ramasamy, L. Liu, H. Habibullah, and R. Kowalczyk, “PIP: Prototypes-injected prompt for federated class incremental learning,” in ACM Conference on Information and Knowledge Management (CIKM), 2024, pp. 1670–1679.
Z. Qiao et al., “Class-incremental Learning for Time series: Benchmark and Evaluation,” in ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2024, pp. 5613–5624.
M. M. Anwar et al., “Dynamic long-term time-series forecasting via meta transformer networks,” IEEE Transactions on Artificial Intelligence, vol. 5, Art. no. 8, 2024.
Q. Pham et al., “CompeteSMoE–Effective Training of Sparse Mixture of Experts via Competition,” arXiv preprint arXiv:2402.02526, 2024.
G. Do et al., “HyperRouter: Towards efficient training and inference of sparse mixture of experts,” arXiv preprint arXiv:2312.07035, 2023.
Qiao, S. Ramasamy, and Suganthan, Ponnuthurai Nagaratnam, “Online Continual Learning for Control of Mobile Robots,” IEEE International Joint Conference on Neural Networks (IJCNN), 2023.
Z. Qiao, M. Hu, X. Jiang, Suganthan, Ponnuthurai Nagaratnam, and R. Savitha, “Class-incremental Learning on Multivariate Time Series via shape-aligned Temporal Distillation,” in IEEE Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023, pp. 1–5.
M. Paknezhad et al., “Improving transparency and representational generalizability through parallel continual learning,” Neural Networks, vol. 161, pp. 449–465, 2023.
M. Paknezhad et al., “PaRT: Parallel learning towards robust and transparent AI,” arXiv preprint arXiv:2201.09534, 2022.
L. Guimeng, G. Yang, C. Wong, Suganathan, Ponnuthurai Nagartnam, and R. Savitha, “Unsupervised Generative Variational Continual Learning,” in IEEE International Conference on Image Processing (ICIP), 2022, pp. 4028–4032.
Y. Guo, J. Wen, C. Sze, and S. Ramasamy, “Bayesian Continual Imputation and Prediction for Irregularly Sampled Time Series Data,” in IEEE Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022, pp. 4493–4497.
C. Sze, G. Yang, A. Ambikapathi, and R. Savitha, “Online continual learning using enhanced random vector functional link networks,” in IEEE Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022, pp. 1905–1909.
C. Sze, G. Yang, N. F. Chen, and R. Savitha, “Incremental Context Aware Attentive Knowledge Tracing,” in IEEE Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022, pp. 3993–3997.
G. Yang, C. Sze, and R. Savitha, “Robust continual learning through a comprehensively progressive Bayesian neural network,” arXiv preprint arXiv:2202.13369, 2022.
S. Gopalakrishnan, P. R. Singh, H. Fayek, S. Ramasamy, and A. Ambikapathi, “Knowledge Capture and Replay for Continual Learning,” in 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2022, pp. 10–18.
P. R. Singh, S. Gopalakrishnan, Q. ZhongZheng, Suganthan, Ponnuthurai N, S. Ramasamy, and A. Ambikapathi, “Task-agnostic continual learning using base-child classifiers,” in IEEE Conference on Image Processing, 2021, pp. 794–798.
M. Gupta, A. Ambikapathi, and S. Ramasamy, “Hebbnet: A simplified hebbian learning framework to do biologically plausible learning,” in IEEE Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021, pp. 3115–3119.
R. Savitha, A. Ambikapathi, and K. Rajaraman, “Online RBM: growing restricted Boltzmann machine on the fly for unsupervised representation,” Applied Soft Computing, vol. 92, p. 106278, 2020.
M. Pratama, A. Ashfahani, Y. S. Ong, S. Ramasamy, and E. Lughofer, “Autonomous deep learning: Incremental learning of denoising autoencoder for evolving data streams,” arXiv preprint arXiv:1809.09081, 2018.
K. K. Nagalingam, R. Savitha and A. Mamun, “Regional ocean wave height prediction using sequential learning neural networks,” Ocean Engineering, vol. 129, pp. 605–612, 2017
N. K. Vuong, G. S. Babu, G. L. Lim, T. Tan, and S. Ramasamy, “Anomaly Detection and Breakdown Diagnosis for Condition Monitoring of Marine Engines,” in IEEE Conference on Artificial Intelligence, 2024, pp. 200–205.
C. Shengkai, W. Yu, R. Savitha, and C. Wong, “Hierarchical optimization for operationally-constrained resource planning,” in IEEE Conference on Artificial Intelligence, 2024, pp. 674–679.
A. Jeyasothy, S. Ramasamy, and S. Sundaram, “A Gradient Descent Algorithm for SNN with time-varying Weights for Reliable Multiclass Interpretation,” Applied Soft Computing, vol. 161, no. 8, p. 111747, 2024.
W. Zhang, F. Liu, C. M. Nguyen, Y. Zhong, S. Ramasamy, and C.-S. Foo, “Training neural networks with classification rules for incorporating domain knowledge,” Knowledge-Based Systems, vol. 294, p. 111716, 2024.
J. Han, A. Chong, J. Lim, S. Ramasamy, N. H. Wong, and F. Biljecki, “Microclimate spatio-temporal prediction using deep learning and land use data,” Building and Environment, vol. 253, p. 111358, 2024.
M. M. Anwar et al., “Dynamic long-term time-series forecasting via meta transformer networks,” IEEE Transactions on Artificial Intelligence, vol. 5, Art. no. 8, 2024.
M. R. Sarkar et al., “GATE: A guided approach for time series ensemble forecasting,” Expert Systems with Applications, vol. 235, p. 121177, 2024.
A. Jeyasothy, S. Suresh, S. Ramasamy, and N. Sundararajan, “Development of a novel transformation of spiking neural classifier to an interpretable classifier,” IEEE Transactions on Cybernetics, vol. 54, Art. no. 1, 2022.
Y. Zhou, M. Wang, M. Gupta, A. Ambikapathi, Suganthan, Ponnuthurai Nagaratnam, and S. Ramasamy, “Investigating robustness of biological vs. backprop based learning,” in IEEE Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022, pp. 3533–3537.
T. Pranavan, T. Sim, A. Ambikapathi, and S. Ramasamy, “Contrastive predictive coding for anomaly detection in multi-variate time series data,” arXiv preprint arXiv:2202.03639, 2022.
S. Suresh, S. Ramasamy, Suganthan, Ponnuthurai N, and C. Sze, “Incremental knowledge tracing from multiple schools,” arXiv preprint arXiv:2201.06941, 2022.
A. Garg, W. Zhang, J. Samaran, R. Savitha, and C.-S. Foo, “An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time Series,” IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 6, Art. no. 6, 2021.
R. Ward, C. Sze, A. Chong, R. Choudhary, and S. Ramasamy, “A study on the transferability of computational models of building electricity load patterns across climatic zones,” Energy and Buildings, vol. 237, p. 110826, 2021.
Y. Guo, Z. Liu, S. Ramasamy, and P. Krishnaswamy, “Uncertainty characterization for predictive analytics with clinical time series data,” Explainable AI in Healthcare and Medicine: Building a Culture of Transparency and Accountability, 2021.
Y. Guo, Z. Liu, P. Krishnswamy, and S. Ramasamy, “Bayesian Recurrent Framework for Missing Data Imputation and Prediction with Clinical Time Series,” NeurIPS ML4H, arXiv preprint arXiv:1911.07572, 2019.
A. Jeyasothy, S. Sundaram, S. Ramasamy, and N. Sundararajan, “A novel method for extracting interpretable knowledge from a spiking neural classifier with time-varying synaptic weights,” arXiv preprint arXiv:1904.11367, 2019.
A. Jeyasothy, S. Ramasamy, and S. Sundaram, “Efficient single input-output layer spiking neural classifier with time-varying weight model,” arXiv preprint arXiv:1904.10400, 2019.
N, Krishna Kumar, R. Savitha and A. Mamun, “A study on dynamic positioning system robustness with wave loads predictions from deep belief network,” in IEEE Symposium Series on Computational Intelligence (SSCI), 2018, pp. 1520–1527.
S. Ramasamy, Y. Xue, R. Phoon, R. Han, N. Low, and C. S. Lim, “Predictive maintenance of the aircraft engine bleed air system component,” in Prognostics and Health Management (PHM), 2018, pp. 1–7.
K. N. Krishna, R. Savitha, and A. Mamun, “Ocean wave characteristics prediction and its load estimation on marine structures: A transfer learning approach,” Marine Structures, vol. 61, pp. 202–219, 2018.
K. N. Krishna, R. Savitha, and A. Mamun, “Ocean wave height prediction using ensemble of extreme learning machine,” Neurocomputing, vol. 277, pp. 12–20, 2018.
A. Ashutosh, R. Savitha, and S. Suresh, “Comprehensive study of features for subject-independent emotion recognition,” in IEEE Joint Conference on Neural Networks, 2017, pp. 3114–3121.
R. Savitha, K. Y. Chan, P. P. San, S. H. Ling, and S. Suresh, “A Hybrid Deep Boltzmann Functional Link Network for Classification Problems,” in EEE Symposium Series on Computational Intelligence (SSCI), 2016, pp. 1–6.
K. K. Nagalingam, R. Savitha and A. Mamun, “Fully complex-valued radial basis function networks for prediction of wind force and moment co-efficients on marine structures,” in International Conference on Cognitive Computing and Information Processing (CCIP), 2016, pp. 1–6.
R. Savitha and A. Mamun, “An ensemble of Extreme Learning Machine for prediction of wind force and moment coefficients in marine vessels,” in International Conference on Cognitive Computing and Information Processing (CCIP), 2016, pp. 2901–2908.
V. Sachnev, S. Ramasamy, S. Sundaram, H. J. Kim, and H. J. Hwang, “A cognitive ensemble of extreme learning machines for steganalysis based on risk-sensitive hinge loss function,” Cognitive Computation, vol. 7, pp. 103–110, 2015.
KK Nagalingam and R. Savitha, “Compact online recurrent network for time series prediction,” 2014.
D. Shirin, R. Savitha, and S. Suresh, “A basis coupled evolving spiking neural network with afferent input neurons,” in IEEE Joint Conference on Neural Networks, 2013, pp. 1–8.
S. Parameswaran, Y. Fang, C. Gautam, S. Ramasamy, and X. Li, “Learning to Identify seen, Unseen and Unknown in the Open world: a Practical Setting for zero-shot Learning,” in EEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 6868–6878.
Gautam et al., “Class Name Guided out-of-scope Intent Classification,” in Empirical Methods in Natural Language Processing (EMNLP), 2024, pp. 9100–9112.
A. Das et al., “Decoupled training for semi-supervised medical image segmentation with worst-case-aware learning,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2024, pp. 45–55.
C. Gautam, A. Kane, S. Ramasamy, and S. Sundaram, “Unsupervised out-of-distribution Detection Using Few in-Distribution Samples,” in IEEE Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023, pp. 1–5.
C. Gautam, S. Parameswaran, V. Verma, S. Sundaram, and S. Ramasamy, “Refinement matters: Textual Description Needs to Be Refined for zero-shot Learning,” in Empirical Methods in Natural Language Processing (EMNLP), 2022, pp. 6127–6140.
M. Sivachitra, R. Savitha, S. Suresh, and S. Vijayachitra, “A fully complex-valued fast learning classifier (FC-FLC) for real-valued classification problems,” Neurocomputing, vol. 149, pp. 198–206, 2015.
K. Subramanian, R. Savitha, and S. Suresh, “A complex-valued neuro-fuzzy inference system and its learning mechanism,” Neurocomputing, vol. 123, pp. 110–120, 2014.
M. Elangeeran, S. Ramasamy, and K. Arumugam, “A novel method for benign and malignant characterization of mammographic microcalcifications employing waveatom features and circular complex valued—extreme learning machine,” in IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), 2014, pp. 1–6.
R. Savitha, S. Suresh, and H. J. Kim, “A meta-cognitive learning algorithm for an extreme learning machine classifier,” Cognitive Computation, vol. 6, pp. 253–263, 2014.
R. Savitha, S. Suresh, and N. Sundararajan, “Projection-based fast learning fully complex-valued relaxation neural network,” IEEE Transactions on Neural Networks and Learning Systems, vol. 24, Art. no. 4, 2013.
S. Suresh, N. Sundararajan, R. Savitha, “Supervised learning with complex-valued neural networks,” Springer Berlin, 2013.
S. Suresh, K. Subramanian, and R. Savitha, “A complex-valued neuro-fuzzy inference system and its learning mechanism,” 2013.
K. Subramanian, R. Savitha, and S. Suresh, “Zero-error density maximization based learning algorithm for a neuro-fuzzy inference system,” in IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2013, pp. 1–7.
R. Savitha, S. Suresh, and N. Sundarara, “Meta-cognitive complex-valued relaxation network and its sequential learning algorithm,” Complex-Valued Neural Networks: Advances and Applications, John Wiley & Sons, Inc., 2013.
S. Suresh, N. Sundararajan, R. Savitha, “Fully complex-valued relaxation networks,” Supervised Learning with Complex-valued Neural Networks, Springer Berlin Heidelberg, 2013.
S. Suresh, N. Sundararajan, R. Savitha, “A fully complex-valued radial basis function network and its learning algorithm,” Supervised Learning with Complex-valued Neural Networks, Springer Berlin Heidelberg, 2013.
S. Suresh, N. Sundararajan, R. Savitha, “Performance study on complex-valued function approximation problems,” Supervised Learning with Complex-valued Neural Networks, Springer Berlin Heidelberg, 2013.
S. Suresh, N. Sundararajan, R. Savitha, “Fully complex-valued multi layer perceptron networks,” Supervised Learning with Complex-valued Neural Networks, Springer Berlin Heidelberg, 2013.
S. Suresh, N. Sundararajan, R. Savitha, “Circular complex-valued extreme learning machine classifier,” Supervised Learning with Complex-valued Neural Networks, Springer Berlin Heidelberg, 2013.
S. Suresh, N. Sundararajan, R. Savitha, “Complex-valued self-regulatory resource allocation network (CSRAN),” Supervised Learning with Complex-valued Neural Networks, Springer Berlin Heidelberg, 2013.
S. Suresh, N. Sundararajan, R. Savitha, “Performance study on real-valued classification problems,” Supervised Learning with Complex-valued Neural Networks, Springer Berlin Heidelberg, 2013.
R. Savitha, S. Suresh, and N. Sundararajan, “Fast learning complex-valued classifiers for real-valued classification problems,” International Journal of Machine Learning and Cybernetics, vol. 4, Art. no. 5, 2013.
R. Savitha, S. Suresh, and N. Sundararajan, “A meta-cognitive learning algorithm for a fully complex-valued relaxation network,” Neural Networks, vol. 32, pp. 209–218, 2012.
R. Savitha, S. Suresh, and N. Sundararajan, “Metacognitive learning in a fully complex-valued radial basis function neural network,” Neural Computation, vol. 24, Art. no. 5, 2012.
K. Subramanian, R. Savitha, S. Suresh, and B. Mahanand, “Complex-valued neuro-fuzzy inference system based classifier,” in Springer Berlin Heidelberg, 2012, pp. 348–355.
K. Subramanian, R. Savitha, and S. Suresh, “Complex-valued neuro-fuzzy inference system for wind prediction,” in IEEE International Joint Conference on Neural Networks (IJCNN), 2012, pp. 1–7.
G. Sateesh Babu, R. Savitha, and S. Suresh, “A projection based learning in meta-cognitive radial basis function network for classification problems,” in IEEE, 2012, pp. 1–8.
B. R. Venkatesh, S. Suresh, and R. Savitha, “Human action recognition using a fast learning fully complex-valued classifier,” Neurocomputing, vol. 89, pp. 202–212, 2012.
R. Savitha, S. Suresh, and N. Sundararajan, “Fast learning circular complex-valued extreme learning machine (CC-ELM) for real-valued classification problems,” Information Sciences, vol. 187, pp. 277–290, 2012.
G. Vani, R. Savitha, and N. Sundararajan, “Classification of abnormalities in digitized mammograms using extreme learning machine,” in IEEE International Conference on Control Automation Robotics & Vision, 2010, pp. 2114–2117.
R. Savitha, S. Suresh, and N. Sundararajan, “A fast learning complex-valued neural classifier for real-valued classification problems,” in IEEE Joint Conference on Neural Networks, 2011, pp. 2243–2249.
S. Suresh, R. Savitha, and N. Sundararajan, “A fast learning fully complex-valued relaxation network (FCRN),” in IEEE Joint Conference on Neural Networks, 2011, pp. 1372–1377.
S. Suresh, R. Savitha, and N. Sundararajan, “A sequential learning algorithm for complex-valued self-regulating resource allocation network-CSRAN,” IEEE Transactions on Neural Networks, vol. 22, Art. no. 7, 2011.
M. F. Amin, R. Savitha, M. I. Amin, and K. Murase, “Complex-valued functional link network design by orthogonal least squares method for function approximation problems,” in IEEE Joint Conference on Neural Networks, 2011, pp. 1489–1496.
R. Savitha, S. Suresh, N. Sundararajan, and H. J. Kim, “Fast learning fully complex-valued classifiers for real-valued classification problems,” in Supervised Learning with Complex-valued Neural Networks, Springer Berlin Heidelberg, 2011, pp. 602–609.
S. Suresh, N. Sundararajan, R. Savitha, and H. Kim, “A fully complex-valued radial basis function classifier for real-valued classification problems,” Supervised Learning with Complex-valued Neural Networks, 2011.
R. Savitha, S. Suresh, and N. Sundararajan, “A self-regulated learning in fully complex-valued radial basis function networks,” in IEEE Joint Conference on Neural Networks, 2010, pp. 1–8.
R. Savitha, S. Vigneswaran, S. Suresh, and N. Sundararajan, “Adaptive beamforming using complex-valued radial basis function neural networks,” in IEEE Region 10 Conference, 2009, pp. 1–6.
R. Savitha, S. Suresh, and N. Sundararajan, “Complex-valued function approximation using a fully complex-valued RBF (FC-RBF) learning algorithm,” in IEEE Joint Conference on Neural Networks, 2009, pp. 2819–2825.
R. Savitha, S. Suresh, N. Sundararajan, and P. Saratchandran, “A new learning algorithm with logarithmic performance index for complex-valued neural networks,” Neurocomputing, vol. 72, Art. no. 16-18, 2009.
R. Savitha, S. Suresh, and N. Sundararajan, “A fully complex-valued radial basis function network and its learning algorithm,” International Journal of Neural Systems, vol. 19, Art. no. 04, 2009.
R. Savitha, S. Suresh, N. Sundararajan, and P. Saratchandran, “Complex-valued function approximation using an improved BP learning algorithm for feed-forward networks,” in IEEE Joint Conference on Neural Networks, 2008, pp. 2251–2258.
A. Hirose, “Complex-valued neural networks,” Springer New York, 2006.
A. Jeyasothy, S. Ramasamy, and S. Sundaram, “Meta-neuron learning based spiking neural classifier with time-varying weight model for credit scoring problem,” Expert Systems with Applications, vol. 178, p. 114985, 2021.
K. K. Nagalingam, R. Savitha, and A. Mamun, “Meta-cognitive extreme learning machine for regression problems,” in International Conference on Cognitive Computing and Information Processing (CCIP), 2016, pp. 1–6.
S. Ramasamy and K. Rajaraman, “A Hybrid Meta-cognitive Restricted Boltzmann Machine Classifier for Credit Scoring,” in IEEE TENCON, 2017, pp. 2313–2318.
K. Subramanian, R. Savitha, and S. Suresh, “A metacognitive complex-valued interval type-2 fuzzy inference system,” IEEE Transactions on Neural Networks and Learning Systems, vol. 25, Art. no. 9, 2014.
K. Subramanian, A. K. Das, S. Sundaram, and S. Ramasamy, “A meta-cognitive interval type-2 fuzzy inference system and its projection based learning algorithm,” Evolving Systems, vol. 5, pp. 219–230, 2014.
K. Subramanian, V. B. Radhakrishnan, and S. Ramasamy, “Database Independent Human Emotion Recognition with meta-cognitive neuro-fuzzy Inference System,” in IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), 2014, pp. 1–6.
K. Subramanian, R. Savitha, and S. Suresh, “A meta-cognitive interval type-2 fuzzy inference system classifier and its projection based learning algorithm,” in IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS), 2013, pp. 48–55.
S. Suresh, R. Savitha, and K. Subramanian, “A meta-cognitive interval type-2 fuzzy inference system classifier and its projection based learning algorithm,” IEEE Conference on Evolving and Adaptive Intelligent Systems (2013 : Singapore), 2013.
R. Savitha, S. Suresh, and N. Sundararajan, “Meta-cognitive learning algorithm for a fully complex-valued relaxation network (McFCRN),” Studies in Computational Intelligence, Springer-Verlag Germany, 2013.
S. Suresh, R. Savitha, and H. Kim, “A meta-cognitive Learning Algorithm for an Extreme Learning Machine Classifier,” Cognitive Computation, 2013.
B. R. Venkatesh, R. Savitha, S. Suresh, and B. Agarwal, “Subject independent human action recognition using spatio-depth information and meta-cognitive RBF network,” Engineering Applications of Artificial Intelligence, vol. 26, Art. no. 9, 2013.
AI for Healthcare
I. Q. Xu et al., “Predictive analysis of amyotrophic lateral sclerosis progression and mortality in a clinic cohort from Singapore,” Muscle & Nerve, John Wiley & Sons, Inc., 2025.
C. Jing, S. Ramasamy, J. Leong, S. Nag, and Z. Simmons, “A Neuromuscular Clinician’s Primer on Machine Learning,” Journal of Neuromuscular Diseases, 2024.
I. Xu, L. Guo, Z. Simmons, S. Ramasamy, and C. Jing, “Prediction of Survival Outcomes for Patients with Amyotrophic Lateral Sclerosis Utilizing Machine Learning,” 2024, pp. 619–619.
A. Jabbar et al., “Predicting amyotrophic lateral sclerosis (ALS) progression with machine learning,” Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration, vol. 25, no. 3–4, 2024.
A. Jabbar et al., “Describing and characterising variability in ALS disease progression,” Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration, vol. 25, no. 1–2, 2024.
M. Gaur, K. Chaturvedi, V. D. Kumar, S. Ramasamy, and M. Prasad, “Self-supervised ensembled learning for autism spectrum classification,” Research in Autism Spectrum Disorders, vol. 107, p. 102223, 2023.
Abdul, G. Yang, E. Pioro, S. Ramasamy, and C. Jing, “Implications and predictors of variability of disease progression in amyotrophic lateral sclerosis (ALS) (P1-1. Virtual),” Neurology, vol. 98, 2022.
Abdul, G. Yang, E. Pioro, S. Ramasamy, and C. Jing, “Predicting fast progressors in amyotrophic lateral sclerosis (ALS): A machine learning approach (P1-1. Virtual),” Neurology, vol. 98, 2022.
F. Fahimi et al., “A vital signs telemonitoring programme improves the dynamic prediction of readmission risk in patients with heart failure,” in AMIA Symposium, 2021, p. 432.
Z. Liu et al., “Fast prototyping a dialogue comprehension system for nurse-patient conversations on symptom monitoring,” arXiv preprint arXiv:1903.03530, 2019.
P. Krishnaswamy et al., “A Predictive Analytics Methodology to Assess and Optimize Readmission Risk in Heart Failure patients,” in AAAI Health Intelligence, 2018, pp. 463–464.
B. Mahanand, R. Savitha, and S. Suresh, “Computer aided diagnosis of ADHD using brain magnetic resonance images,” in Springer International Publishing, 2013, pp. 386–395.
S. Vigneshwaran, B. Mahanand, S. Suresh, and R. Savitha, “Autism spectrum disorder detection using projection based learning meta-cognitive RBF network,” in IJCNN, 2013, pp. 1–8.
AI for Materials
J. Pan et al., “Transfer learning-based artificial intelligence-integrated physical modeling to enable failure analysis for 3 nanometer and smaller silicon-based CMOS transistors,” ACS Applied Nano Materials, vol. 4, no. 7, 2021.
F. Oviedo et al., “Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks,” npj Computational Materials, vol. 5, 2019.
S. Sun et al., “Accelerated development of perovskite-inspired materials via high-throughput synthesis and machine-learning diagnosis,” Joule, vol. 3, pp. 1437–1451, 2019.
M. Zeng, J. N. Kumar, Z. Zeng, R. Savitha, V. R. Chandrasekhar, and K. Hippalgaonkar, “Graph convolutional neural networks for polymers property prediction,” arXiv preprint arXiv:1811.06231, 2018.
F. Oviedo et al., “Fast classification of small X-ray diffraction datasets using data augmentation and deep neural networks,” arXiv preprint arXiv:1811.08425, 2018.
S. Sun et al., “Accelerating photovoltaic materials development via high-throughput experiments and machine-learning-assisted diagnosis,” arXiv preprint arXiv:1812.01025, 2018.
L. Laugier et al., “Predicting thermoelectric properties from crystal graphs and material descriptors—first application for functional materials,” arXiv preprint arXiv:1811.06219, 2018.