Nafiz Fahad | Computer Science | Best Researcher Award

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Mr. Nafiz Fahad | Computer Science | Best Researcher Award

Multimedia University | Malaysia

Mr. Nafiz Fahad is an emerging AI researcher at Multimedia University, Cyberjaya, Malaysia, recognized for his growing contributions to artificial intelligence in healthcare, computer vision, and natural language processing. His research focuses extensively on explainable AI, clinical decision support systems, and data-driven healthcare intelligence. According to Scopus, he has 19 indexed publications, 122 citations, and an h-index of 6, reflecting the influence and visibility of his scholarly work within the global research community. His scientific output spans chronic disease prediction, dementia analytics, lung disease classification, hypertension ontology development, wound-image segmentation, obesity prediction, and precision public health. These studies incorporate techniques such as deep learning, transfer learning, ensemble learning, hybrid architectures, and explainable machine learning to advance diagnostic accuracy and interpretability in medical AI systems. Beyond health-focused research, Fahad has also contributed high-impact work in fake news detection, generative AI, machine learning security, student performance prediction, agricultural disease detection, vision transformers for physics data, and federated learning enhanced with homomorphic encryption. His ongoing research extends to mental health analytics, EEG decoding models, diabetic retinopathy detection, and agentic AI solutions for healthcare innovation. Fahad’s growing academic recognition includes research awards, best paper achievements, and contributions to high-impact journals and conferences. His multidisciplinary scholarship positions him as a promising young researcher advancing applied AI at the intersection of healthcare, societal well-being, and intelligent systems.

Profiles: Scopus | Google Scholar | LinkedIn

Featured Publications

1. Ahmed, Z., Shanto, S. S., Rime, M. H. K., Morol, M. K., Fahad, N., Hossen, M. J., … (2024). The generative AI landscape in education: Mapping the terrain of opportunities, challenges and student perception. IEEE Access.

2. Mahamud, E., Fahad, N., Assaduzzaman, M., Zain, S. M., Goh, K. O. M., & Morol, M. K. (2024). An explainable artificial intelligence model for multiple lung diseases classification from chest X-ray images using fine-tuned transfer learning. Decision Analytics Journal, 12, 100499.

3. Ahmed, R., Fahad, N., Miah, M. S. U., Hossen, M. J., Morol, M. K., Mahmud, M., … (2024). A novel integrated logistic regression model enhanced with recursive feature elimination and explainable artificial intelligence for dementia prediction. Healthcare Analytics, 6, 100362.

4. Fahad, N., Goh, K. M., Hossen, M. I., Shopnil, K. M. S., Mitu, I. J., Alif, M. A. H., & Tee, C. (2023). Stand up against bad intended news: An approach to detect fake news using machine learning. Emerging Science Journal, 7(4), 1247–1259.

5. Hossain, M. N., Fahad, N., Ahmed, R., Sen, A., Al Huda, M. S., & Hossen, M. I. (2024). Preventing student’s mental health problems with the help of data mining. International Journal of Computing, 23(1), 101–108.

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.