Bingqin Han | Computer Science and Artificial Intelligence | Research Excellence Award

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Mr. Bingqin Han | Computer Science and Artificial Intelligence | Research Excellence Award

Tianjin University | China

Mr. Bingqin Han, is a leading researcher at the intersection of artificial intelligence, consumer psychology, and business ethics, focusing on the factors that influence AI adoption in both consumer and managerial contexts. His work integrates behavioral science, marketing, and technology studies to examine how trust, risk perception, ethical considerations, and cognitive biases shape the acceptance and utilization of AI technologies. He has pioneered innovative frameworks for modeling human–AI interaction, most notably the “onion model” of AI humanoid robot adoption in domestic settings, which conceptualizes acceptance across technical, psychological, and social layers, offering both theoretical and practical insights. Mr. Bingqin Han’s scholarly contributions include three peer-reviewed publications, which have garnered three citations and an h-index of 1 according to Scopus, reflecting his emerging influence in the field. Beyond publications, he has contributed to interdisciplinary research projects, participated in editorial and peer-review activities, and engaged in collaborative initiatives that bridge academia and industry, highlighting his commitment to advancing both theory and practice. His work has significantly enriched understanding of human–AI integration, providing actionable guidance for developers, policymakers, and researchers aiming to optimize AI deployment in real-world environments. By combining rigorous empirical research, innovative conceptual frameworks, and interdisciplinary collaboration, Han has made substantial contributions to AI adoption research, behavioral decision-making, and ethical technology development, establishing a foundation for future innovations and informing responsible, human-centered implementation of AI technologies across diverse contexts.

Profile: Scopus

Featured Publications

  • Han, B., Song, S., Liu, D., & Mo, J. (2024). Mechanism of online public opinion formation in major risk events in China: A qualitative comparative analysis.

  • Han, B., & Liu, D. (2025). Peeling back acceptance: An onion model of AI humanoid robot adoption in homes.

 

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.