Mahmoud Ragab

Biochemistry — King Abdulaziz University, Saudi Arabia

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Mahmoud Ragab King Abdulaziz University Saudi Arabia
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Biochemistry

Subspecialties: Informatics - Physical Medicine - Psychology

Full career – Last 3 years

Main Topics

Publications and Clinical Studies

Publications – Clinical Studies

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Short Biography

Prof. Mahmoud Ragab obtained his Ph.D. degree in stochastic algorithms, Faculty of Mathematics and Natural Sciences, Christian-Albrechts-University at Kiel (CAU), Schleswig-Holstein, Germany. He is a professor of data science at Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia and Computer Science, Mathematics Department, Faculty of Science, Al-Azhar University, Cairo, Egypt as well. He worked in different research groups at many universities such as the Combinatorial Optimization and Graph Algorithms Group (COGA), Berlin University of Technology, Berlin, Germany, Faculty of Informatics and Computer Science at the British University in Egypt, Automation, Integrated Communication Systems Group, Ilmenau University of Technology, Ilmenau, Thüringen, Germany. His research focuses on: Data Science, AI, DL, Data driven optimization, Mathematical modeling, Neural networks, Time series analysis and Decision Support Systems.


Organizations

Career arc: 2020 - 2026

Source: PubMed

King Abdulaziz University

Publications Synthesis

Sources: PubMed, OpenAlex

Number : 87

Citations

Citations: 804 1st author: 35 Last author: 30 Unique author: 4


Best Journals

Breakdown by type

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Latest Publications

5 most recent indexed articles

Source: Pubmed

Title Journal Year Citations
The impact of artificial intelligence with multi-head self-attention based deep learning model for students' academic performance monitoring.
intelligence, multiple chronic conditions, classification, algorithms, deep learning, academic performance, learning, artificial intelligence, education
Front Artif Intell20260
Smartphone based clinical decision support system for early detection of neonatal jaundice using artificial intelligence with temporal convolutional network.
intelligence, jaundice, classification, biomedical engineering, smartphone, jaundice, neonatal, decision support systems, clinical, artificial intelligence, physical exertion, bilirubin, biomedical technology
Technol Health Care20260
Advances in electrocardiogram signal analytics for person identification using fine-grained fusion-driven deep representation learning under arrhythmic conditions.
heart, classification, deep learning, learning, electrocardiography
Front Digit Health20260
Explainable multi-backbone deep feature fusion based wheelchair detection with disability-aware accessibility monitoring in smart urban environments.
wheelchairs, multiple chronic conditions, deep learning, self-help devices, environment
Disabil Rehabil Assist Technol20260
Deep neural architecture empowered by explainable artificial intelligence for accurate and early diagnosis of gynaecological cancer using medical images.
early diagnosis, intelligence, uterine cervical neoplasms, neoplasms, diagnosis, natural science disciplines, artificial intelligence
Sci Rep20260

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