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.

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.

Gokalp Çınarer | Artificial Intelligence | Best Researcher Award

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Assist. Prof. Dr. Gokalp Çınarer | Artificial İntelligence | Best Researcher Award

Yozgat Bozok University Computer Engineering Department | Turkey

Dr. Gökalp Çınarer is an Assistant Professor in the Department of Computer Engineering at Yozgat Bozok University. His expertise lies in artificial intelligence, machine learning, image processing, and deep neural networks. Over the years, he has contributed significantly to the academic community with applications of AI in diverse fields, including medicine, agriculture, food technologies, and environmental analysis.

Professional profile👤

Google Scholar

ORCID

Scopus

Strengths for the Awards✨

  • Diverse Research Portfolio: Dr. Çınarer has made significant contributions across multiple domains — medicine, agriculture, food technologies, and environmental analysis — leveraging artificial intelligence and machine learning, showcasing versatility and cross-disciplinary impact.
  • High Research Output: With over 50 publications indexed in SCI-Expanded, SSCI, and Scopus, his research output is impressive, indicating a consistent contribution to advancing knowledge in computer engineering and AI applications.
  • Innovative Work in Medical Imaging: His PhD thesis on brain tumor detection through image processing and classification algorithms reflects a critical application of AI in medical diagnostics, directly contributing to healthcare advancements.
  • Academic Leadership: As Head of the Department of Information Processing and Computer Engineering Software at Yozgat Bozok University, Dr. Çınarer plays a pivotal role in shaping academic programs, guiding research initiatives, and mentoring students.
  • Teaching Excellence: He teaches a broad range of courses, including Machine Learning, Deep Learning, and Artificial Intelligence Applications, fostering the next generation of AI researchers and practitioners.

🎓 Education

Dr. Çınarer earned his PhD in Computer Engineering from Kırıkkale University between 2017 and 2021. His doctoral research focused on “Detection of Brain Tumors with Image Processing Techniques and Analysis with Classification Algorithms.”

💼 Experience

Since 2021, Dr. Çınarer has been serving as the Head of the Department of Information Processing and the Head of the Department of Computer Engineering Software at Yozgat Bozok University. He teaches undergraduate and graduate courses, including Machine Learning, Deep Learning, Python Programming, Algorithm Analysis, and Artificial Intelligence Applications.

🔬 Research Interests On Artificial Intelligence

Dr. Çınarer’s research interests encompass artificial intelligence, machine learning, image processing, and deep neural networks. His work delves into AI applications across various fields such as medicine, agriculture, food technologies, and environmental analysis.

🏆 Awards

Dr. Çınarer has been recognized for his pioneering work in artificial intelligence and its applications across multiple disciplines, earning accolades for his contributions to AI-driven medical analysis and agricultural technologies.

📚 Publications

  • Classification of brain tumors by machine learning algorithms
    Authors: G Çınarer, BG Emiroğlu
    Year: 2019
    Citations: 74

  • Prediction of Glioma Grades Using Deep Learning with Wavelet Radiomic Features
    Authors: G Çınarer, BG Emiroğlu, AH Yurttakal
    Year: 2020
    Citations: 57

  • Öğretmenlerin Teknolojik Araçlarla Eğitime Yönelik Tutumlarının Çeşitli Değişkenlere Göre İncelenmesi Yozgat İli Örneği
    Author: G Çınarer
    Year: 2016
    Citations: 27

  • Classification of hazelnuts according to their quality using deep learning algorithms
    Authors: N Erbaş, G Çınarer, K Kiliç
    Year: 2022
    Citations: 21

  • A comparative study on segmentation and classification in breast mri imaging
    Authors: AH Yurttakal, H Erbay, T İkizceli, S Karacavus, G Çinarer
    Year: 2018
    Citations: 21

  • Brain Tumor Classification Using Deep Neural Network
    Authors: G Çınarer, BG Emiroğlu, RS Arslan, AH Yurttakal
    Year: 2020
    Citations: 16

  • Application of various machine learning algorithms in view of predicting the CO2 emissions in the transportation sector
    Authors: G Çınarer, MK Yeşilyurt, Ü Ağbulut, Z Yılbaşı, K Kılıç
    Year: 2024
    Citations: 11

  • Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation: Proceedings of the INFUS 2021 Conference
    Authors: G Çınarer, C Kahraman, S Cebi, SC Onar, B Oztaysi, AC Tolga, IU Sari
    Year: 2021
    Citations: 9

  • Classification of Diabetic Rat Histopathology Images Using Convolutional Neural Networks
    Authors: AH Yurttakal, H Erbay, G Çınarer, H Baş
    Year: 2021
    Citations: 8

  • A Deep Transfer Learning Framework For The Staging Of Diabetic Retinopathy
    Authors: G Çınarer, K Kılıç, T Parlar
    Year: 2022
    Citations: 6

📄 Conclusion

Dr. Gökalp Çınarer stands at the forefront of AI research, leveraging machine learning and image processing to solve complex problems in medicine, agriculture, and environmental science. His work continues to inspire advancements in interdisciplinary fields and pave the way for future innovations.