Dr. Maher Alrahhal is an Assistant Professor at Amity University Dubai. His research focuses on Artificial Intelligence, Machine Learning, Deep Learning, Explainable AI (XAI), Generative AI, Large Language Models (LLMs), Natural Language Processing, Big Data, and intelligent image, video, and signal processing applications.
Prior to joining Amity University Dubai, Dr. Alrahhal worked as a Postdoctoral Researcher at the University of Sharjah, UAE, and Universiti Putra Malaysia (UPM), Malaysia. He also serves as an Adjunct Assistant Professor at McMaster University, Canada.
Dr. Alrahhal received his Ph.D. in Computer Science and Engineering from Jawaharlal Nehru Technological University, India, and his Master of Technology in Computer Science and Engineering from the National Institute of Technology Warangal, India. He earned his Bachelor's degree in Computer Engineering from the University of Aleppo, Syria, graduating with honors as the top student in his class.
His research spans trustworthy and explainable AI, medical image analysis, computer vision, deep learning, image retrieval, big data analytics, and AI-enabled intelligent systems. He has published extensively in peer-reviewed journals and international conferences, including work in Scientific Reports, The Computer Journal, Medical Engineering & Physics, IEEE Access, and other international venues.
Dr. Alrahhal is also a Co-Principal Investigator on an AED 1 million research project funded by the UAE Federal Authority for Nuclear Regulation (FANR), focusing on AI-driven radiation plume monitoring for nuclear emergency preparedness.
- Ph.D. in Computer Science and Engineering, Jawaharlal Nehru Technological University, India, 2024.
- M.Tech. in Computer Science and Engineering, National Institute of Technology Warangal, India, 2018.
- B.Eng. in Computer Engineering, University of Aleppo, Syria, 2014.
Journals
- M. Alrahhal., Elgack, O.A., AlShabi, M. et al. Explainable multi-model machine learning framework for tensile property prediction of FeNiCrCoCu high-entropy alloys. Scientific Reports (2026).
- M. Alrahhal, F. Alqahtani, R. Latip, M. AlShabi, and W. M. Abd-Elhafiez, "Deepfake face detection using hybrid bag-of-visual-words and multi-CNN feature fusion," Scientific Reports, 2026.
- H. Ardah, M. Alrahhal, W. M. Abd-Elhafiez, and D. Trabay, "Robust coffee plant disease classification using deep learning and advanced feature engineering techniques," PeerJ Computer Science, vol. 11, p. e3386, 2025.
- A.-K. Hamid, M. Alrahhal, K. Obaideen, T. Bonny, Y. C. Tan, and M. I. Hussein, “Artificial intelligence for smart solar energy monitoring: Genetic attention-based hybrid deep–handcrafted fusion for faulty solar panel image classification,” Results in Engineering, p. 107900, 2025.
- A. Alobaid, T. Bonny, and M. Alrahhal, “Disruptive attacks on artificial neural networks: A systematic review of attack techniques, detection methods, and protection strategies,” Intelligent Systems with Applications, vol. 26, p. 200529, 2025.
- M. Alrahhal and V. K. Shukla, “MapReduce model for efficient image retrieval: A Hadoop-based framework,” International Journal of Information Technology, vol. 17, no. 2, pp. 925–939, 2025.
- T. Bonny et al., “A new 5-D hyperchaotic system with a line equilibrium, its bifurcation analysis, circuit simulation, FPGA implementation, and data prediction using long-term-short memory,” IEEE Access, 2025.
- M. Alrahhal and K. Supreethi, “Enhancing image retrieval systems: A comprehensive review of machine learning integration in CBIR,” International Journal of Intelligent Systems and Applications in Engineering, vol. 12, no. 4, pp. 4195–4210, Nov. 2024.
- N. K. Al-Qazzaz, M. Alrahhal, S. H. Jaafer, S. H. B. M. Ali, and S. A. Ahmad, “Automatic diagnosis of epileptic seizures using entropy-based features and multimodel deep learning approaches,” Medical Engineering & Physics, vol. 130, no. 1, p. 104206, 2024.
- M. Alrahhal and K. Supreethi, “Integrating machine learning algorithms for robust content-based image retrieval,” International Journal of Information Technology, vol. 16, no. 8, pp. 5005–5021, 2024.
- M. Alrahhal and K. Supreethi, “Enhancing image retrieval accuracy through multi-resolution HSV-LNP feature fusion and modified K-NN relevance feedback,” International Journal of Information Technology, pp. 1–15, 2024.
- S. Sardin, S. P. Dewi, M. Saleh, and M. Alrahhal, “The guided note-taking learning model effectively improves students’ mathematics learning creativity,” International Journal of Mathematics and Mathematics Education (IJMME), vol. 1, no. 3, pp. 236–249, 2023.
- D. Islamiaty, N. H. P. S. Putro, and M. Alrahhal, “Senior high school students’ perceptions of online learning during the pandemic era,” International Journal of Contemporary Studies in Education, vol. 2, no. 3, pp. 190–196, 2023.
- M. Alrahhal and S. KP, “COVID-19 diagnostic system using medical image classification and retrieval: A novel method for image analysis,” The Computer Journal, vol. 65, no. 8, pp. 2146–2163, 2022.
- S. K. Maher Alrahhal, “Multimedia image retrieval system by combining CNN with handcraft features in three different similarity measures,” 2022.
- M. M. Alrahhal and M. Rahhal, “Detection of COVID-19 disease using artificial intelligence techniques and radiographic medical images,” J. King Abdulaziz Univ.-Comput. Inf. Technol., vol. 10, 2021. · M. Alrahhal and K. Supreethi, “Full direction local neighbors pattern (FDLNP),” International Journal of Advanced Computer Science and Applications, vol. 12, no. 1, 2021.
- Trustworthy and Explainable Artificial Intelligence for Healthcare and Medical Image Analysis
- Hallucination-Aware Vision-Language Models for Medical Image Report Generation
- Explainable Deep Learning for Medical Image Classification and Diagnosis
- Federated Learning and Privacy-Preserving Artificial Intelligence for Smart IoT Applications
- Artificial Intelligence and Deep Learning for Image and Video AnalysisContent-Based Image Retrieval using Machine Learning and Deep Learning
- Big Data Analytics and Hadoop-Based Intelligent Systems
- Natural Language Processing and Large Language Models for Intelligent Applications
- Artificial Intelligence for Signal Processing and Biomedical Data Analysis
- AI-Driven Radiation Plume Monitoring for Nuclear Emergency Preparedness
- Adjunct Assistant Professor, McMaster University, Canada
- Postdoctoral Researcher and Research Fellow, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia (UPM), Malaysia
- External Research Member, Research Institute of Sciences and Engineering (RISE), University of Sharjah, UAE
- ITEC 300 – Computer Communications and Networking
- ITEC 310 – Internet of Things
- CSCI 310 – Principles of Programming Languages
- CSE 447 – Introduction to Natural Language Processing
