Research

• M. A. Haq, S. B. H. Hassine, S. J. Malebary, H. A. Othman, and E. M. Tag-Eldin, “3D-CNNHSR: A 3-Dimensional Convolutional Neural Network for Hyperspectral Super-Resolution.,” Comput. Syst. Sci. & Eng., vol. 47, no. 2, 2023.

• M. A. Haq, A. Ahmed, and J. Gyani, “Implementation of CNN for Plant Identification using UAV Imagery,” Int. J. Adv. Comput. Sci. Appl., vol. 14, no. 2, pp. 369–378, 2023.

• M. A. Haq, I. Khan, A. Ahmed, S. M. Eldin, A. Alshehri, and N. A. Ghamry, “DCNNBT: a novel deep 

• N. M. Shakti Raj Chopra Parulpreet Singh Ahmed Alhussen and M. A. Haq, “Power Optimized Multiple-UAV Error-Free Network in Cognitive Environment,” Comput. Mater. Contin., vol. 75, no. 2, pp. 3189–3201, 2023.

• N. Sharma et al., “Deep Learning and SVM-Based Approach for Indian Licence Plate Character Recognition,” Comput. Mater. Contin., vol. 74, no. 1, pp. 881–895, 2023.

• R. M. Saleem et al., “Internet of Things Based Weekly Crop Pest Prediction by Using Deep Neural Network,” IEEE Access, 2023.

• I. Segmentation et al., “U-Net-Based Models towards Optimal MR Brain Image Segmentation,” Diagnostics, vol. 13, no. 9, p. 1624, 2023.

• Anubha, R. P. S. Bedi, A. A. Khan, M. A. Haq, A. Alhussen, and Z. S. Alzamil, “Efficient Optimal Routing Algorithm Based on Reward and Penalty for Mobile Adhoc Networks,” Comput. Mater. Contin., vol. 75, no. 1, pp. 1331–1351, 2023.

• P. R. L. V. R. D. K. Kavin Kumar P. M. Dinesh and M. A. Haq, “Brain tumor identification using data augmentation and transfer learning approach,” Comput. Syst. Sci. Eng., vol. 46, no. 2, pp. 1845–1861, 2023.

• A. Alabdulwahab, M. A. Haq, and M. Alshehri, “Cyberbullying Detection using Machine Learning and Deep Learning,” Int. J. Adv. Comput. Sci. Appl., vol. 14, no. 10, pp. 424–432, 2023.

• M. A. Haq, “Optimal cluster head selection for energy efficient wireless sensor network using hybrid competitive swarm optimization and harmony search algorithm,” Sustain. Energy Technol. Assessments, vol. 52, no. 102243, pp. 1–5, 2022.

• M. A. Haq, “A Review on Deep Learning Techniques for IoT Data,” Electronics, vol. 11, no. 1604, pp. 1–23, 2022.

• M. A. Haq, “DNNBoT: Deep Neural Network-Based Botnet Detection and Classification,” Comput. Mater. Contin., vol. 71, no. 1, pp. 1769–1788, 2022.

• M. A. Haq, M. A. R. Khan, and M. Alshehri, “Insider Threat Detection Based on NLP Word Embedding and Machine Learning,” Intell. Autom. Soft Comput., vol. 33, no. 1, pp. 619–635, 2022, doi: 10.32604/iasc.2022.021430.

• C. S. Yadav et al., “Malware Analysis in IoT & Android Systems with Defensive Mechanism,” Electronics, vol. 11, no. 15, p. 2354, 2022.

• A. Jawaharlalnehru et al., “Target Object Detection from Unmanned Aerial Vehicle (UAV) Images Based on Improved YOLO Algorithm,” Electronics, vol. 11, no. 15, p. 2343, 2022.

• M. A. Haq, “CNN Based Automated Weed Detection System Using UAV Imagery,” Comput. Syst. Sci. Eng., vol. 42, no. 2, pp. 837–849, 2022.

• M. A. Haq, “SMOTEDNN: A Novel Model for Air Pollution Forecasting and AQI Classification,” Comput. Mater. Contin., vol. 71, no. 1, pp. 1403–1425, 2021.

• M. A. Haq, A. Ahmed, I. Khan, J. Gyani, and A. Mohamed, “Analysis of environmental factors using AI and ML methods,” Sci. Rep., pp. 1–16, 2022, doi: 10.1038/s41598-022-16665-7.

• M. A. Haq, “Development of PCCNN-Based Network Intrusion Detection System for EDGE Computing,” Comput. Mater. Contin., vol. 71, no. 1, pp. 1729–1750, 2021.

• J. Gyani, A. Ahmed, and M. A. Haq, “MCDM and Various Prioritization Methods in AHP for CSS: A Comprehensive Review,” IEEE Access, vol. 10, no. Mcdm, pp. 33492–33511, 2022, doi: 10.1109/access.2022.3161742.

• P. Paliwal, J. L. Webber, A. Mehbodniya, M. A. Haq, A. Kumar, and P. K. Chaurasiya, “Multi-agent-based approach for generation expansion planning in isolated micro-grid with renewable energy sources and battery storage,” J. Supercomput., pp. 1–27, 2022.

• M. A. Haq, “Planetscope Nanosatellites Image Classification Using Machine Learning,” Comput. Syst. Sci. Eng., vol. 42, no. 3, pp. 1031–1046, 2022.

• M. A. Haq, P. Baral, S. Yaragal, and B. Pradhan, “Bulk processing of multi‐temporal modis data, statistical analyses and machine learning algorithms to understand climate variables in the Indian Himalayan region,” Sensors, vol. 21, no. 21, pp. 1–25, 2021, doi: 10.3390/s21217416.

• M. A. Haq, “Deep Learning Based Modeling of Groundwater Storage Change,” Comput. Mater. Contin., vol. 70, no. 3, pp. 4599–4617, 2021.

• DCNNBT: A Novel Deep Convolution Neural Network-Based Brain Tumor Classification Model

  • Sponsoring Agency: Ministry of Education, KSA and Majmaah University, ID: IFP-2020-96

  • Project Duration: Dec 2022 - March 2023

  • Grant Awarded (Equivalent SAR): 14,400

• DNNBoT: Deep Neural Network-Based Botnet Detection and Classification

  • Sponsoring Agency: DSR, Majmaah University, ID: R-2021-220

  • Project Duration: Jan 2021 - Dec 2021

  • Grant Awarded (Equivalent SAR): 9,600

• Development of PCCNN-Based Network Intrusion Detection System for EDGE Computing

  • Sponsoring Agency: DSR, Majmaah University, ID: R-2021-117

  • Project Duration: Jan 2021 - Dec 2021

  • Grant Awarded (Equivalent SAR): 9,600

• Modeling of Crop Selection Based on Environmental and Associated Parameters using Machine Learning

  • Sponsoring Agency: Ministry of Education, KSA and Majmaah University, ID: 2020-14

  • Project Duration: Aug 2020 - July 2021

  • Grant Awarded (Equivalent SAR): 26,400

• CDLSTM: A Novel Model for Climate Change Forecasting

  • Sponsoring Agency: DSR, Majmaah University, ID: R-2021-236

  • Project Duration: Jan 2021 - Dec 2021

  • Grant Awarded (Equivalent SAR): 9,600

• SMOTEDNN: A Novel Model for Air Pollution Forecasting and AQI Classification

  • Sponsoring Agency: DSR, Majmaah University, ID: R-2021-202

  • Project Duration: Jan 2021 - Dec 2021

  • Grant Awarded (Equivalent SAR): 9,600

• Efficiency of Artificial Neural Networks for Glacier Ice-Thickness Estimation: A Case Study in Western Himalaya, India

  • Sponsoring Agency: DSR, Majmaah University, ID: R-2021-10

  • Project Duration: Jan 2020 - Dec 2020

  • Grant Awarded (Equivalent SAR): 9,600

• AI-based Modeling the Snow Properties for Their Classification and Identification

  • Sponsoring Agency: DST, Govt. of India

  • Project Duration: 21 April 2016 - 01 Dec 2020

  • Grant Awarded (Equivalent SAR): 1,72,234

• Understanding the Geomorphology of Martian Surface using Mars Orbiter Mission Datasets

  • Sponsoring Agency: Indian Space Research Organization, Govt. of India

  • Project Duration: 10 Oct 2016 - 10 Oct 2019

  • Grant Awarded (Equivalent SAR): 89,000

• Using Artificial Intelligence and Cloud Computing to Monitor Groundwater Resources in Rajasthan, India

  • Sponsoring Agency: Microsoft Inc. Redmond. US

  • Project Duration: 06 April 2019 - 1 Dec 2020

  • Grant Awarded (Equivalent SAR): 45,000

• Coupling GPR Measurements and ANN Modelling for Mountain Glacier Volume Assessment in India and Russia

  • Sponsoring Agency: DST-Russian Foundation of Science

  • Project Duration: 2016

  • Grant Awarded (Equivalent SAR): 1,20,866

• Development of Glacial Lake Monitoring Techniques in The Uttarakhand Himalayas Using Geomatics Techniques

  • Sponsoring Agency: NIIT University, India

  • Project Duration: 15 Sep 2014 - 14 March 2016

  • Grant Awarded (Equivalent SAR): 18,532

• Glacial Lake Monitoring Techniques in The Uttarakhand Himalayas Using Geomatics Technique ID: 29121

  • Sponsoring Agency: European Space Agency

  • Project Duration: 17 Dec 2014 - 1 Nov 2015

  • Grant Awarded (worth of ERS data): 40,000

• Google Scholar

• ResearchGate

• Orcid

• Scopus

1)    Academic Editor at PLoS One: https://journals.plos.org/plosone/static/editorial-board?ae_name=Mohd+Anul+Haq ; 15 December 2023 to Until Now

2)    Guest Editor at Frontiers of Environmental Science

https://www.frontiersin.org/research-topics/20729/big-earth-data-intelligence-for-environmental-modeling#impact; 25 March 2021-27 September 2021.

3)    Guest Editor at Frontiers of MDPI, Remote Sensing https://www.mdpi.com/journal/remotesing/special_issues/pattern_mining; 11 November 2021- 21 August 2022.

4)    Review Editor in Frontiers of Remote Sensing, April 18 2022 to Until now.

For manuscripts reviewed from December 2018 to December 2023

• Plos One (44)

• Remote Sensing (19)

• Applied Sciences (12)

• Sustainability (9)

• Land (9)

• Water (8)

• Sensors (8)

• IEEE Access (8)

• Information (4)

• International Journal of Remote … (4)

• Atmosphere (4)

• Electronics (4)

• International Journal of Retail an… (3)

• Toxics (2)

• Energies (2)

• Axioms (2)

• Forests (2)

• Agriculture (2)

• Future Internet (2)

• ISPRS International Journal of Ge… (2)

• The Journal of Supercomputing (2)

• Geocarto International (2)

• IEEE Geoscience and Remote Se… (1)

• Coatings (1)

• Applied Artificial Intelligence (1)

• International Journal of Digital E… (1)

• Soft Computing (1)

• Pollutants (1)

• Natural Resources Research (1)

• UASG 2023 (5)

• UASG 2022 (4)

1. Australian Patent: Status Granted 

Patent number: 2021104641

Title of invention:

FUSION ARTIFICIAL NEURAL NETWORK MODEL FOR DETERMINING INFLUENCING POLLUTANT IN

SMART CITIES ENVIRONMENT

2. Indian  Patent: Status Published 

Patent number: 202111056643

Title of invention:

BLOCKCHAIN BASED METHODOLOGY FOR TRANSMISSION OF SCANNED IMAGE OVER DATA NETWORK TO A FILE SERVER

Statement of Research

While the opening places a strong emphasis on computer science, I believe that my interdisciplinary background in Applied AI and Machine Learning (ML) positions me as an exceptional candidate for the computer system role. My work extends far beyond traditional computer science boundaries, encompassing a wide array of critical domains, including climate science, natural sciences, cyber security, and even medical sciences. This breadth of experience equips me with a holistic understanding of how computer systems can be applied to address real-world challenges across various sectors.

My research and practical applications in AI have been instrumental in addressing complex issues in climate and environmental science, emphasizing my capability to leverage computing systems for data analysis and modeling. Furthermore, my work in cyber security demonstrates my proficiency in developing and implementing robust computer systems to safeguard critical information.

My commitment to bridging the gap between AI/ML and diverse scientific disciplines showcases my adaptability and ability to apply computer science principles innovatively. I strongly believe that this multidisciplinary approach offers a unique and invaluable perspective, allowing me to contribute significantly to the computer systems field by not only leveraging the principles of computer science but also integrating them with real-world applications, making me an ideal fit for the role.

My academic journey commenced at Majmaah University, where I embarked on a transformative research project funded by the Ministry of Education, KSA. This project aimed to model crop selection based on environmental and associated parameters, harnessing high-resolution planetscope datasets to adapt agricultural practices to the ever-evolving climate [1]. Additionally, I collaborated with an international team from Université Grenoble Alpes and IIT Indore to develop an artificial neural network (ANN) model for predicting surface ice thickness in glaciers, showcasing the global relevance of my research [2].

A critical thread in my research is addressing water scarcity, particularly in arid regions such as Saudi Arabia. Alarming statistics regarding groundwater depletion and population growth underscore the urgency of monitoring Terrestrial Water Storage Change (TWSC) and Ground Water Storage Change (GWSC). Given the challenges of traditional in-situ measurements, my research endeavors aimed to analyze changes in TWSC and GWSC from 2003 to 2020, investigating relationships between TWSC, GWSC, satellite-based rainfall, and meteorological station data, estimate net budget modeling of groundwater, and forecast TWSC and GWSC changes using Long Short-Term Memory (LSTM) models, both for Saudi Arabia and its five basins [3].

The diversity of my applied AI research has multiple facets including sustainable agriculture [4]–[6]. Climate change and environmental issues such as air pollution were among the main applications [7]–[10]. I have an interest in applying ML and AI for different types of Geo datasets including remote sensing imagery [11]–[16].

Looking ahead to the future, I am excited to explore the possibilities offered by vision transformers. I envision creating and implementing cutting-edge applications based on vision transformers to tackle complex challenges, including climate change analysis, groundwater modeling, sustainable agriculture practices, and the development of smart cities. These forward-looking endeavors will not only push the boundaries of AI but also make significant contributions to societal and commercial advancements, aligning seamlessly with the overarching goals of promoting external engagement and societal impact.

In conclusion, my research journey is underpinned by the aspiration to apply AI and machine learning to address critical issues in climate, earth, and environmental science. With a strong track record of impactful research and a vision for the future, my research endeavors resonate with the goals of promoting external engagement and societal impact, contributing to a brighter and more sustainable future.

References

[1]  M. A. Haq and M. Y. A. Khan, “Crop Water Requirements with Changing Climate in an Arid Region of Saudi Arabia,” Sustainability, vol. 14, no. 13554, pp. 1–24, 2022.

[2]  M. A. Haq, M. F. Azam, and C. Vincent, “Efficiency of artificial neural networks for glacier ice-thickness estimation: A case study in western Himalaya, India,” J. Glaciol., vol. 67, no. 264, pp. 671–684, 2021, doi: 10.1017/jog.2021.19.

[3]  M. A. Haq, “Deep Learning Based Modeling of Groundwater Storage Change,” Comput. Mater. Contin., vol. 70, no. 3, pp. 4599–4617, 2021.

[4]  M. Anul Haq, “Planetscope Nanosatellites Image Classification Using Machine Learning,” Comput. Syst. Sci. Eng., vol. 42, no. 3, pp. 1031–1046, 2022, doi: 10.32604/csse.2022.023221.

[5]  M. A. Haq, “CNN Based Automated Weed Detection System Using UAV Imagery,” Comput. Syst. Sci. Eng., vol. 42, no. 2, pp. 837–849, 2022.

[6]  P. Mangan et al., “Analytic Hierarchy Process Based Land Suitability for Organic Farming in the Arid Region,” Sustainability, vol. 14, no. 4542, pp. 1–16, 2022.

[7]  M. A. Haq, “CDLSTM: A novel model for climate change forecasting,” Comput. Mater. Contin., vol. 71, no. 2, pp. 2363–2381, 2022, doi: 10.32604/cmc.2022.023059.

[8]  M. A. Haq, “SMOTEDNN: A Novel Model for Air Pollution Forecasting and AQI Classification,” Comput. Mater. Contin., vol. 71, no. 1, pp. 1403–1425, 2021.

[9]  M. A. Haq, P. Baral, S. Yaragal, and B. Pradhan, “Bulk processing of multi‐temporal modis data, statistical analyses and machine learning algorithms to understand climate variables in the indian himalayan region,” Sensors, vol. 21, no. 21, 2021, doi: 10.3390/s21217416.

[10] M. A. Haq, A. Ahmed, I. Khan, J. Gyani, and A. Mohamed, “Analysis of environmental factors using AI and ML methods,” Sci. Rep., pp. 1–16, 2022, doi: 10.1038/s41598-022-16665-7.

[11] U. G. M. A. R. K. V. R. Kriti Mohd Anul Haq, “Fusion-Based Deep Learning Model for Hyperspectral Images Classification,” Comput. Mater. Contin., vol. 71, no. 1, pp. 939–957, 2022.

[12] M. A. Haq, M. Alshehri, G. Rahaman, A. Ghosh, P. Baral, and C. Shekhar, “Snow and glacial feature identification using Hyperion dataset and machine learning algorithms,” Arab. J. Geosci., vol. 14, no. 15, 2021, doi: 10.1007/s12517-021-07434-3.

[13] P. Baral and M. A. Haq, “Spatial prediction of permafrost occurrence in Sikkim Himalayas using logistic regression, random forests, support vector machines and neural networks,” Geomorphology, vol. 371, 2020, doi: 10.1016/j.geomorph.2020.107331.

[14] M. A. Haq and P. Baral, “Study of permafrost distribution in Sikkim Himalayas using Sentinel-2 satellite images and logistic regression modelling,” Geomorphology, vol. 333, pp. 123–136, 2019, doi: 10.1016/j.geomorph.2019.02.024.

[15] M. A. Haq, P. Baral, S. Yaragal, and G. Rahaman, “Assessment of trends of land surface vegetation distribution, snow cover and temperature over entire Himachal Pradesh using MODIS datasets,” Nat. Resour. Model., vol. 33, no. 2, 2020, doi: 10.1111/nrm.12262.

[16] M. A. Haq, M. Alshehri, G. Rahaman, A. Ghosh, P. Baral, and C. Shekhar, “Snow and glacial feature identification using Hyperion dataset and machine learning algorithms,” Arab. J. Geosci., vol. 14, no. 15, pp. 1–21, 2021.