Advancing the frontiers of Artificial Intelligence and Learning Technologies through innovative research in intelligent tutoring systems, mobile learning, and educational technology.
My research spans multiple domains within computer science, focusing on the intersection of artificial intelligence and educational technology.
Machine learning algorithms, neural networks, and deep learning applications in education. Development of intelligent systems for personalized learning and adaptive content delivery.
Development of intelligent tutoring systems, adaptive e-learning platforms, and educational software that personalizes learning experiences based on individual needs.
Design and development of mobile-based multimedia learning systems, smartphone applications, and context-aware learning environments for ubiquitous education.
Research in formal languages, automata theory, and compiler design. Development of educational tools for teaching computational models and programming concepts.
Over 100 peer-reviewed publications in top-tier conferences and journals, contributing to the advancement of AI and educational technology.
Authors: L. Salau, M. Hamada, Y. Abdulsalam, M. Hassan
A hybrid model, which integrates deep learning and factorization-based techniques to improve multi-criteria recommendations.
Authors: Md Atiqur Rahman, Mohamed Hamada, Shayla Sharmin, Tanzina Afroz Rimi, Atia Sanjida Talukder, Nafees Imran, Khadijatul Kobra, Md Ridwan Ahmed, Md Rabbi, Md Mafiul Hasan Matin, M Ameer Ali
An enhanced machine-learning approach for breast cancer detection using the Wisconsin Breast Cancer (Diagnostic) (WDBC) dataset.
Authors: Bhasker Bapuram, Murali Subramanian, Anand Mahendran, Ibrahim Ghafir, Vijayan Ellappan, Mohammed Hamada
This paper presents an overview of the integration of IoMT and cloud computing technologies.
Authors: L. Salau, M. Hamada, R. Prasad, M. Hassan, A. Mahendran, Y. Watanobe
This comprehensive survey analyzes the current state of deep learning approaches in educational recommender systems, examining architectures, datasets, evaluation metrics, and future research directions.
Authors: J.J. Tanimu, M. Hamada, M. Hassan, H. Kakudi, J.O. Abiodun
Novel machine learning approach utilizing advanced neural network architectures for accurate cervical cancer classification, achieving significant improvements in diagnostic accuracy and clinical applicability.
Authors: M.A. Rahman, M. Hamada
Comprehensive review of lossless compression methods with performance analysis, comparative evaluation, and recommendations for various application domains including medical imaging and data archiving.
Authors: M. Hamada, M. Hassan
Development of an interactive virtual environment for teaching information and communication theory, incorporating adaptive learning mechanisms and real-time feedback systems.
Authors: M. Hamada
Pioneering work on virtual environments for collaborative learning in computational theory, featuring real-time interaction, automated assessment, and personalized learning paths.
Editor: M. Hamada
Comprehensive edited volume examining mobile learning trends, user attitudes, and effectiveness measures across diverse educational contexts and technological platforms.
Editor: M. Hamada
Comprehensive edited volume examining e-learning new technologies, practical applications, and effectiveness measures across diverse educational contexts and platforms.
View complete publication list with citation metrics and collaboration networks
Google Scholar ProfileMeasuring the influence and reach of research contributions across the global academic community.