Mahmoud Abd-Ellah | Computer Science | Best Researcher Award

Published on by Forensic Scientist

Assoc. Prof. Dr. Mahmoud Abd-Ellah | Computer Science | Best Researcher Award

Egyptian Russian University | Egypt

Dr. Mahmoud Khaled Abd-Ellah is an accomplished Assistant Professor at the Faculty of Artificial Intelligence, Egyptian Russian University, Badr, Egypt, widely recognized for his pioneering research at the intersection of artificial intelligence, medical imaging, and deep learning. Holding a Ph.D. in Electrical Engineering from Minia University, his doctoral research focused on brain tumor diagnosis through MRI using advanced machine learning techniques. His impressive publication portfolio includes 22 Scopus-indexed papers, collectively cited 830 times by 774 documents, with an h-index of 12 demonstrating substantial scientific impact and research excellence. His scholarly work has been featured in leading journals such as Scientific Reports, Neural Computing and Applications, and Ecological Informatics, advancing AI-driven approaches for medical image analysis, automated brain tumor detection, COVID-19 classification, and environmental data modeling. Beyond research, Dr. Abd-Ellah actively contributes to academic governance and quality enhancement as a member of the Egyptian International Ranking Committee, head of the Quality Management Unit, and ranking official at the Egyptian Russian University. He is also an active member of multiple IEEE councils, engaging in the development and application of AI across engineering and biomedical domains. His ORCID profile (0000-0002-6840-2503) and Scopus ID (57191265348) further reflect his consistent record of impactful scholarship and international collaboration. With his interdisciplinary expertise, editorial service, and mentorship of Ph.D. and master’s students, Dr. Mahmoud Khaled Abd-Ellah exemplifies academic leadership, innovation, and a transformative approach to research that advances both science and society.

Profile: Scopus | Google Scholar | ORCID | ResearchGate

Featured Publications

  • Abd-Ellah, M. K., Awad, A. I., Khalaf, A. A. M., & Hamed, H. F. A. (2019). A review on brain tumor diagnosis from MRI images: Practical implications, key achievements, and lessons learned. Magnetic Resonance Imaging, 61, 300–318.

  • Abd-Ellah, M. K., Awad, A. I., Khalaf, A. A. M., & Hamed, H. F. A. (2018). Two-phase multi-model automatic brain tumour diagnosis system from magnetic resonance images using convolutional neural networks. EURASIP Journal on Image and Video Processing, 2018(1), 1–10.

  • El-Rawy, M., Abd-Ellah, M. K., Fathi, H., & Ahmed, A. K. A. (2021). Forecasting effluent and performance of wastewater treatment plant using different machine learning techniques. Journal of Water Process Engineering, 44, 102380.

  • Abd-Ellah, M. K., Awad, A. I., Khalaf, A. A. M., & Hamed, H. F. A. (2016). Design and implementation of a computer-aided diagnosis system for brain tumor classification. In 2016 28th International Conference on Microelectronics (ICM) (pp. 73–76).

  • MostafaShokry, A. A. M. K., Awad, A. I., & Abd-Ellah, M. K. (2022). Systematic survey of advanced metering infrastructure security: Vulnerabilities, attacks, countermeasures, and future vision. Future Generation Computer Systems, 1–21.

Ozgur Tonkal | Computer Science and Artificial Intelligence | Best Researcher Award

Published on by Forensic Scientist

Assoc. Prof. Dr. Ozgur Tonkal | Computer Science and Artificial Intelligence | Best Researcher Award

Samsun University | Turkey

Dr. Ozgur Tonkal is a distinguished academician and researcher at Samsun University, specializing in cybersecurity, Software-Defined Networks (SDN), and AI-driven threat detection. He earned his Ph.D. in Computer Engineering from Gazi University in 2022, where his doctoral research introduced an autonomous intrusion detection and mitigation model for SDN, providing adaptive and traffic-aware defense against volumetric attacks. Building on this foundation, he developed a multimodal spam email detection framework that integrates Distil BERT embeddings with structural features, achieving 99.62% accuracy and exposing concept drift vulnerabilities across eras of spam. Dr. Ozgur Tonkal has contributed significantly to both academic research and practical applications through international journal articles, conference papers, and book chapters that advance explainable and continually learning cybersecurity systems. His completed research includes DDoS detection using machine learning and neighborhood component analysis, while ongoing projects focus on adaptive spam detection, IoT traffic analysis, and explainable deep learning models for robust network security. Beyond academia, he serves as ISO/IEC 27001:2022 Lead Auditor, coordinator of a university Cyber Incident Response Team, and technical advisor for the Ministry of Education International Robotics Competition, demonstrating his ability to translate research into real-world solutions. Notable publications include studies on multimodal spam detection, AI-based dementia diagnosis, and DDoS attack detection in SDN. Dr. Ozgur Tonkal exemplifies excellence in research, innovation, and the practical implementation of cybersecurity solutions.

Profile: Google Scholar | ORCID | ResearchGate | LinkedIn | Staff Page

Featured Publications

  • Tonkal, Ö., Polat, H., Başaran, E., Cömert, Z., & Kocaoglu, R. (2021). Machine learning approach equipped with neighbourhood component analysis for DDoS attack detection in software-defined networking. Electronics, 10(1227), 1–18. Cited by 128.

  • Tonkal, Ö., & Polat, H. (2021). Traffic classification and comparative analysis with machine learning algorithms in software-defined networks. Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, 9(1), 1–12. Cited by 15.

  • Sertkaya, M. E., Ergen, B., Türkoğlu, M., & Tonkal, Ö. (2024). Accurate diagnosis of dementia and Alzheimer’s with deep network approach based on multi‐channel feature extraction and selection. International Journal of Imaging Systems and Technology, 34(3), e23079. Cited by 4.

  • Ouhsousou, S., & Tonkal, Ö. (2024). Analysis of global language dynamics: A cross-cultural examination of the most spoken languages and perceived learning ease. 8th International Artificial Intelligence and Data Processing Symposium, 1–6. Cited by 1.

  • Selimdaroğlu, Y., Yusuf, & Tonkal, Ö. (2025). Acil durum çağrı merkezi uygulamalarında kullanıcı memnuniyeti ve performans analizi: 112 örneği. International Journal of Advances in Engineering and Pure Sciences, 37(2), 45–60.