Mohamed Hamada

Professor of Computer Science

University of Aizu, Japan

AI • Machine Learning and Applications • Learning Technologies

Professor Mohamed Hamada
Citations

About

Leading researcher in Artificial Intelligence and Learning Technologies with a passion for developing innovative educational systems and mobile learning solutions.

AI Research

Machine learning, neural networks, and intelligent systems for educational applications.

Learning Technologies

Intelligent tutoring systems and adaptive e-learning platforms for enhanced education.

Mobile Learning

Innovative mobile applications and multimedia learning systems for global education.

Publications
Citations
Current Students
Years Experience

Recent Publications

Latest research contributions in AI and learning technologies

Scientific Reports (Springer Nature)• 2025

Deep learning-based multi-criteria recommender system for technology-enhanced learning

A hybrid model, which integrates deep learning and factorization-based techniques to improve multi-criteria recommendations...

Cited 4 times Read More →
IEEE Access (IEEE)• 2024

Enhancing early breast cancer detection through advanced data analysis

An enhanced machine-learning approach for breast cancer detection using the Wisconsin Breast Cancer (Diagnostic) (WDBC) dataset...

Cited 20 times Read More →
Evolutionary Intelligence (Springer)• 2024

Extended water wave optimization (EWWO) technique: a proposed approach for task scheduling in IoMT and healthcare applications

This paper presents an overview of the integration of IoMT and cloud computing technologies...

Cited 8 times Read More →
Multimedia Tools and Applications (Springer)• 2023

A prediction-based lossless image compression procedure using dimension reduction and Huffman coding

This paper proposes a lossless image compression procedure by reducing image dimension and using a prediction technique...

Cited 29 times Read More →
Applied Sciences • 2022

State-of-the-art survey on deep learning-based recommender systems for e-learning

Comprehensive analysis of deep learning approaches in educational recommender systems...

Cited 46 times Read More →
Electronics • 2022

A machine learning method for classification of cervical cancer

Novel machine learning approach for accurate cervical cancer classification using neural networks...

Cited 124 times Read More →