Mahmoud Ragab
Biochemistry — King Abdulaziz University, Saudi Arabia
Mahmoud Ragab
LeadR Profile
Biochemistry
Subspecialties: Informatics - Physical Medicine - Psychology
Main Topics
Publications and Clinical Studies
Profile data updated on
Expert Rankings
Top experts on the MeSH terms this expert works on
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
Number : 87
Citations
Main journals
Best Journals
Breakdown by type
Click a legend label to hide or show it in the chart.
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 Intell | 2026 | 0 |
| 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 Care | 2026 | 0 |
| 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 Health | 2026 | 0 |
| 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 Technol | 2026 | 0 |
| 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 Rep | 2026 | 0 |
LeadR Networks
Main global connection map