Mahmoud Abd-Ellah | Computer Science | Best Researcher Award

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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

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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.

Wael Badawy | Computer Science | Best Researcher Award

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Prof. Dr. Wael Badawy | Computer Science | Best Researcher Award

Egyptian Russian University | Egypt

Prof. Wael Badawy, is a distinguished engineer, researcher, and academic leader with over 28 years of experience in higher education, research, technology commercialization, and innovation management. He earned his Ph.D. in Computer Engineering from the University of Louisiana at Lafayette, USA, and an equivalent Ph.D. in Electrical Engineering recognized by the Egyptian Higher Council of Universities, complemented by M.Sc. and B.Sc. degrees in Computer Science and Automatic Control Engineering from Alexandria University, Egypt. Prof. Wael Badawy has held senior academic and leadership positions, including Executive Director of ABM College, Canada, Program Head of Data Science and Cybersecurity at the Egyptian Russian University, and professorships at Nile University, Badr University, and the American University in Cairo, where he has taught and supervised students in Artificial Intelligence, Deep Learning, Multimedia Engineering, Cybersecurity, and Information Technology Management. His research contributions encompass over 400 publications in high-impact journals and conferences, 56 books and proceedings, and 34 co-invented patents, with highly cited work including the IEEE Transactions on Circuits and Systems for Video Technology (2018). Prof. Badawy has received more than 90 prestigious awards and honors, including the QS Reimagine Education Awards (2023, shortlisted), Silicon Review “30 Innovative Brands of the Year” (2022), and multiple distinctions in STEM, business innovation, and leadership. He actively serves on international standardization committees, professional organizations such as IEEE and ACM, and national research councils, contributing to curriculum development, program design, and strategic planning in higher education. Prof. Wael Badawy’s extensive contributions to research, innovation, and education demonstrate his sustained impact on technology, society, and the global academic community, making him an exemplary candidate for the Best Researcher Award.

Profile: Google Scholar | ORCID | LinkedIn | Staff Page

Featured Publications

  • Du, S., Ibrahim, M., Shehata, M., & Badawy, W. (2012). Automatic license plate recognition (ALPR): A state-of-the-art review. IEEE Transactions on Circuits and Systems for Video Technology, 23(2), 311–325.

  • Rahman, C. A., Badawy, W., & Radmanesh, A. (2003). A real-time vehicle’s license plate recognition system. In Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance.

  • Shehata, M. S., Cai, J., Badawy, W. M., Burr, T. W., Pervez, M. S., Johannesson, R. J., … (2008). Video-based automatic incident detection for smart roads: The outdoor environmental challenges regarding false alarms. IEEE Transactions on Intelligent Transportation Systems, 9(2), 349–360.

  • Ghallab, Y. H., Badawy, W., Kaler, K. V. I. S., & Maundy, B. J. (2005). A novel current-mode instrumentation amplifier based on operational floating current conveyor. IEEE Transactions on Instrumentation and Measurement, 54(5), 1941–1949.

  • Du, S., Shehata, M., & Badawy, W. (2011). Hard hat detection in video sequences based on face features, motion and color information. In 2011 3rd International Conference on Computer Research and Development, 4, 25–29.

  • Ghallab, Y., & Badawy, W. (2004). Sensing methods for dielectrophoresis phenomenon: From bulky instruments to lab-on-a-chip. IEEE Circuits and Systems Magazine, 4(3), 5–15.

  • Badawy, W., & Gomaa, H. (2015). Analyzing a segment of video. U.S. Patent No. 9,014,429.

  • Ghallab, Y. H., & Badawy, W. (2010). Lab-on-a-chip: Techniques, circuits, and biomedical applications. Artech House.

  • Badawy, W. (2009). Mesh based frame processing and applications. U.S. Patent No. 7,616,782.

  • Badawy, W. (2009). Video based monitoring system. U.S. Patent No. 7,612,666.

Yongyi Yan | Computer Science and Artificial Intelligence | Best Researcher Award

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Prof. Dr. Yongyi Yan | Computer Science and Artificial Intelligence | Best Researcher Award

Henan University of Science and Technology | China

Prof. Dr. Yongyi Yan, Professor of Control Science at Henan University of Science and Technology, has devoted over 15 years to advancing robust control and intelligent optimization for industrial and autonomous systems, demonstrating a rare blend of theoretical excellence and practical impact. He has authored 65 publications, including influential articles in IEEE Transactions on Automation Science and Engineering and SCIENCE CHINA Information Sciences, accumulating 506 citations with an h-index of 10, reflecting his work’s high recognition and application in both academia and industry. Professor Yan has led multiple National Natural Science Foundation of China projects (U1804150, 62073124, 12571584), supervised three PhD graduates, and served as associate editor for Control Engineering Practice. His pioneering contributions include an adaptive robust control algorithm that reduces oscillation by 40% in high-precision machining and an AI-driven optimization framework (patents ZL2021 1 0419210.4 and ZL202110418903.1) that decreases energy consumption by 18% in automotive assembly lines, now adopted by leading manufacturers. His collaborations with Luoyang Sanwu Cable Group have resulted in significant breakthroughs in high-conductivity aluminum stranded wire cable production, integrating multi-machine coordination, dynamic compensation, and laser-guided visual positioning to enhance efficiency, product quality, and equipment control. By bridging fundamental research with industrial applications, Professor Yan has advanced smart manufacturing, autonomous navigation, and process optimization. His sustained innovation, leadership, and scholarly achievements exemplify research excellence, making him highly deserving of the Best Researcher Award for transformative contributions to control science and engineering.

Profile: Scopus

Featured Publications

  • Li, X., Yan, Y., Yue, J., & Zhang, S. (2025, September). Algebraic insight into universal logic functions and implications for logical system modeling.

  • An, Z., Yan, Y., Yue, J., & Li, X. (2025, May). Construction of automaton observer based on matrix semi-tensor product. In Conference proceedings.

  • Wang, X., Yan, Y., Yue, J., & An, Z. (2025, May). Construction and synchronization analysis of state power set automata based on algebraic methods. In Conference proceedings.

  • Dong, C., Yan, Y., Li, H., & Yue, J. (2024, November). Semi-tensor product approach to controllability, reachability, and stabilizability of extended finite state machines.

  • Zhang, S., Yan, Y., Hao, P., & Yue, J. (2024, October). Structural simplification of finite state machines using pruning operators based on semi-tensor product of matrices. In Conference proceedings.

  • Zhang, S., Yan, Y., Wang, C., & Yue, J. (2024, October). Implementation of automaton product networks: From formal language to algebraic models. In Conference proceedings.

  • Yan, Y., Hao, P., Yue, J., & Feng, J. (2024, October). An STP look at logical blocking of finite state machines: Formulation, detection, and search.

  • Yan, Y., Dong, C., Li, H., & Yue, J. (2024, July). Algebraic implementation of extended finite state machine networks.