Innovative Research Award
Ibrahim El Didi
Prince Mohammad Bin Fahd University, Saudi Arabia
Ibrahim El Didi is a researcher affiliated with Prince Mohammad Bin Fahd University in Saudi Arabia whose documented scholarly work spans machine learning, artificial intelligence, cybersecurity, healthcare Internet of Medical Things (IoMT), renewable-energy systems, and computational methods. His recent publication record includes journal and conference contributions addressing intelligent intrusion detection, deep-learning-based cybersecurity, photovoltaic-system resilience, and machine-learning-supported healthcare applications.[1][2]
Abstract
Ibrahim El Didi is associated with research at the intersection of machine learning and applied intelligent systems. His documented publications include work on Parkinson’s disease classification using gait-related information, AI-based resilience for photovoltaic systems, intelligent intrusion detection for healthcare IoMT environments, layered security frameworks for cloud-connected healthcare systems, and deep-learning approaches to cybersecurity. The publication record also includes a contribution in computational mathematics, illustrating a broader engagement with applied computational research.[1][2][3]
Keywords
Machine learning; artificial intelligence; deep learning; cybersecurity; intrusion detection; healthcare IoMT; photovoltaic systems; network security; Parkinson’s disease classification; intelligent systems; computational methods; applied research.
Introduction
Machine learning has become an important methodological component of modern research in engineering, healthcare, cybersecurity, and energy systems. Its application to complex datasets allows researchers to develop classification, prediction, anomaly-detection, and decision-support approaches for problems that may be difficult to address using conventional analytical techniques alone. El Didi’s listed publications reflect this broader development by applying artificial intelligence and machine learning to multiple technological contexts.[1][4]
Research Profile
The available bibliographic information identifies Ibrahim El Didi with Prince Mohammad Bin Fahd University and associates his research subject area with machine learning. The supplied Scopus profile information records Scopus Author ID 59699937500, three indexed documents, one citation, and an h-index of 1. These metrics describe the indexed record available at the time represented by the supplied profile data and should be interpreted in the context of publication timing, indexing coverage, and the normal evolution of an academic research profile.[6]
Research Contributions
The documented contributions can be grouped into several applied research themes. In healthcare, the Parkinson’s disease study applies a physiology-anchored multiple-instance framework with confidence-stratified training to gait-based classification. Such a research direction connects machine-learning methodology with physiological information and disease-oriented classification problems.[1][2]
Publications
- A Physiology-Anchored Multiple-Instance Framework with Confidence-Stratified Training for Parkinson’s Disease Classification Based on Gait. Applied Sciences, 2026-08-22.
- SPARK: Shutdown Prevention and AI-Based Resilience Framework for PV Systems. 2026 IEEE 15th International Conference on Communication Systems and Network Technologies (CSNT), 2026-04-07.
- CloudMed-IDS: Intelligent Multi-class and Zero-day Intrusion Detection System for HealthCare IoMT. 10th International Conference on Engineering, Technologies & Applied Sciences (ICETAS), Bahrain, 2026-04-01. Contributors.
- MedGuard-IoMT: A Machine Learning-Enabled Layered Security Framework for Intrusion Detection in Cloud-Connected Healthcare Systems. 2025 IEEE 6th International Conference on Computer, Big Data, Artificial Intelligence (ICCBD+AI), 2025-11-21.
Research Impact
The potential significance of El Didi’s research lies primarily in its application-oriented use of machine learning across domains where reliability, security, and intelligent analysis are important. Healthcare IoMT systems require mechanisms capable of detecting diverse cyber threats, while medical classification tasks require computational methods that can process physiological and behavioral information. The listed publications address both dimensions of intelligent healthcare systems.[1][3][4]
Award Suitability
The Innovative Research Award recognizes research activity characterized by the development or application of innovative approaches to significant academic or technological problems. Based on the supplied publication record, Ibrahim El Didi demonstrates several characteristics relevant to this recognition, including research activity in machine learning, application of artificial intelligence to healthcare and cybersecurity, and interdisciplinary work addressing renewable-energy systems.[1][2][4]
Conclusion
Ibrahim El Didi’s documented research profile reflects an interdisciplinary application of machine learning and artificial intelligence to healthcare, cybersecurity, renewable-energy systems, and computational research. His recent publications include work on Parkinson’s disease classification, photovoltaic-system resilience, intelligent intrusion detection for healthcare IoMT, cloud-connected healthcare security, deep-learning-based network protection, and computational differential equations.[1][2][3].
External Links
References
- Farfoura, M. E., Alkhatib, A. A. A., Elkhodr, M., El Didi, I., & Al-Sabbagh, A. (2026). A Physiology-Anchored Multiple-Instance Framework with Confidence-Stratified Training for Parkinson’s Disease Classification Based on Gait. Applied Sciences.
- Guler, N., Houda, T., El Didi, I., & Hindi, J. (2026). SPARK: Shutdown Prevention and AI-Based Resilience Framework for PV Systems. 2026 IEEE 15th International Conference on Communication Systems and Network Technologies (CSNT).
DOI: https://doi.org/10.1109/csnt69054.2026.11502347
- El Didi, I., Guler, N., & Ben Hazem, Z. (2026). CloudMed-IDS: Intelligent Multi-class and Zero-day Intrusion Detection System for HealthCare IoMT. 10th International Conference on Engineering, Technologies & Applied Sciences (ICETAS), Bahrain.
- El Didi, I., Elkhodr, M., Ullah, F., Zhao, Y., & Al-Sabbagh, A. (2025). MedGuard-IoMT: A Machine Learning-Enabled Layered Security Framework for Intrusion Detection in Cloud-Connected Healthcare Systems.
DOI: https://doi.org/10.1109/iccbdai66607.2025.11388099
- El Didi, I., Trad, F., & Chehab, A. (2024). Enhancing Network Security Through Deep Learning: Mitigating Feature Engineering and Zero-Day Attacks. 2024 4th Intelligent Cybersecurity Conference (ICSC).
DOI: https://doi.org/10.1109/icsc63108.2024.10895964
- Youssef, K., Moussa, M., Al-Husseini, M., El Misilmani, H. M., Kabalan, K. Y., & El Didi, I. (2024). Characteristic Mode Solution of Complex-Coefficient Complex-Solution Differential Equations.
DOI: https://doi.org/10.61841/turcomat.v15i1.10082