Norhazwani Md Yunos | Data Science and Analytics | Research Excellence Award

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Dr. Norhazwani Md Yunos | Data Science and Analytics | Research Excellence Award

Universiti Teknikal Malaysia Melaka | Malaysia

Dr. Norhazwani Md Yunos is an accomplished computer scientist and academic whose work bridges theoretical algorithms and applied data analytics. She has authored 11 Scopus-indexed documents, receiving 68 citations with an h-index of 4, reflecting steady scholarly impact across multiple domains. Her educational background is grounded in computer science and algorithmic research, with strong foundations in graph theory, optimization, and computational complexity. Over her academic career, she has gained extensive experience as a lecturer and researcher, contributing to both fundamental theory and real-world applications. Her research interests span machine learning, sentiment analysis, network and community detection, optimization algorithms, big data analytics, and combinatorial optimization, including notable contributions to polynomial-space exact algorithms for the Traveling Salesman Problem (TSP) and modern studies on dynamic community detection and data-driven classification models. Dr. Md Yunos has published in reputable journals such as Alexandria Engineering Journal, Pertanika Journal of Science and Technology, and Journal of Electronic Materials, demonstrating interdisciplinary reach from algorithm design to intelligent systems. Her scholarly record includes peer-reviewed journal articles, conference papers, and book chapters, alongside recognition through high citation performance in selected works. Overall, her research portfolio highlights a sustained commitment to advancing computational methods, intelligent data analysis, and algorithmic efficiency for contemporary scientific and industrial challenges.

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Amir Hossein akbari | Engineering and Technology | Research Excellence Award

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Dr. Amir Hossein akbari | Engineering and Technology | Research Excellence Award

Iran University of Science and Technology | Iran

Amir Hosein Akbari is an accomplished researcher in industrial engineering with a strong record of scholarly impact his academic background is grounded in advanced industrial engineering education, complemented by progressive research experience spanning optimization, decision sciences, and intelligent systems. His professional experience includes active involvement in high-quality research collaborations and contributions to applied and theoretical studies addressing complex industrial and societal problems. His core research interests focus on supply chain management, optimization, meta-heuristic and evolutionary algorithms, scheduling, decision support systems, and artificial intelligence–driven industrial applications, with several influential works in expert systems, soft computing, and manufacturing systems. His publications have appeared in high-impact venues such as Expert Systems with Applications, Soft Computing, and Neural Computing and Applications, reflecting both methodological rigor and practical relevance. Recognition of his work is demonstrated through strong citation performance and collaborations with well-established scholars in operations research and industrial engineering. Overall, his research portfolio highlights a consistent commitment to advancing intelligent optimization methods and decision-making frameworks, contributing valuable insights to academia and industry while strengthening the scientific foundations of modern industrial engineering.

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Ho-jun Song | Computer Science and Artificial Intelligence | Research Excellence Award

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Mr. Ho-jun Song | Computer Science and Artificial Intelligence | Research Excellence Award

Postech | South Korea

Ho-jun Song is a dedicated researcher and Ph.D. candidate in Computer Science and Engineering, specializing in federated learning, edge intelligence, and AIoT systems. With an academic foundation grounded in advanced distributed learning, he has contributed to developing personalized, scalable, and diffusion-based FL frameworks tailored for heterogeneous and resource-constrained environments. He has gained extensive experience through work on edge AI architectures, large-scale experimental pipelines, and applied AI systems for surveillance, security, and military decision support. Professionally, he leads AI initiatives as the Head of AI Development at the Army Artificial Intelligence Center, overseeing deepfake detection, ontology-based LLM systems, and intelligent multi-sensor surveillance solutions. His research interests span federated learning, personalized models, diffusion-based FL, distributed deep learning, and AIoT innovation. His academic journey includes rigorous research under expert mentorship and collaborations with interdisciplinary teams. Although early in his career, he has already contributed impactful ideas such as multidimensional trajectory optimization for FL personalization. He aspires to advance secure, efficient, and adaptive AI systems while contributing to global AI research communities through innovative, mission-driven research.

Profile : Orcid

Featured Publications

Song, H.-J., & Suh, Y.-J. (2025). HyFLM: A hypernetwork-based federated learning with multidimensional trajectory optimization on diffusion paths. Electronics, 14, Article 4704.