Dongju Yang | Computer Science and Artificial Intelligence | Research Excellence Award

Dr. Dongju Yang | Computer Science and Artificial Intelligence | Research Excellence Award

North China University of Technology | China

Dr. Dongju Yang is an Associate Researcher and Assistant Lead Professor for Artificial Intelligence at North China University of Technology, with over a decade of academic and research experience in intelligent computing systems. She earned her Ph.D. in Computer Software and Theory from Northwestern Polytechnical University and previously conducted advanced research at the Institute of Computing Technology, Chinese Academy of Sciences. Her research focuses on Large Language Models, Retrieval-Augmented Generation (RAG), AI Agents, Knowledge Graphs, Natural Language Processing, Service Computing, and Data Intelligence, with strong application impact in smart elderly care and intelligent education. Dr. Yang has led more than ten competitive research projects as principal investigator, including a sub-project of China’s National Key R&D Program and an NSFC General Program, while contributing to multiple major national initiatives. She has contributed to one national standard and three group standards and received the Beijing Science and Technology Progress Award. Overall, her work bridges fundamental AI research with scalable, real-world technological impact and talent cultivation.


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Featured Publications

Intelligent Orientation Robot Based on Large Language Models and Retrieval-Augmented Generation
Dongju Yang, 5th International Conference on Artificial Intelligence and Computer Engineering (ICAICE), 2024. (WOS)

Multi-source Autoregressive Entity Linking Based on Generative Method
Dongju Yang, Weishui Lan, Communications in Computer and Information Science, 2024. (Book Chapter)

IoT Service Distributed Management Architecture and Service Discovery Method for Edge-Cloud Federation
Dongju Yang, International Journal of Grid and Utility Computing, 2022. (WOS)

Construction and Analysis of Scientific and Technological Personnel Relational Graph for Group Recognition
Dongju Yang, International Journal of Software Engineering and Knowledge Engineering, 2021. (WOS)

Research on User Demand-Driven Service Matching Methodology
Huiying Zhang, Dongju Yang, IEEE 6th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA), 2021.

Bilal Khan | Engineering and Technology | Research Excellence Award

Dr. Bilal Khan | Engineering and Technology | Research Excellence Award

Department of Computer Science, University of Engineering and Technology, Mardan | Pakistan

Dr. Bilal Khan is a research‑driven academic with a Ph.D. in Computer Software Engineering and more than 15 years of university‑level teaching and research experience across leading higher‑education institutions in Pakistan, including University of Engineering & Technology, Mardan; City University of Science and Information Technology, Peshawar; Northern University, Nowshera; University of Swabi; and National Institute of Technology, Akora Khattak. His scholarly work focuses on Machine Learning, Data Science, Natural Language Processing, Healthcare & Bioinformatics Analytics, and Software Engineering, where he has authored a substantial portfolio of international journal publications indexed in venues such as IEEE Access and Journal of Healthcare Engineering. Dr. Khan has played key roles in curriculum development, postgraduate supervision, and academic coordination, and serves as a reviewer for high‑impact journals including IEEE Access, ACM Transactions on Healthcare, and Artificial Intelligence. His interdisciplinary work bridges theory and practice, and he remains actively engaged in collaborative research, external grant pursuits, and innovative solutions addressing real‑world challenges.

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Featured Publications

An empirical evaluation of machine learning techniques for chronic kidney disease prophecy
Khan, B., Naseem, R., Muhammad, F., Abbas, G., Kim, S., 2020.

Software defect prediction for healthcare big data: an empirical evaluation of machine learning techniques
Khan, B., Naseem, R., Shah, M.A., Wakil, K., Khan, A., Uddin, M.I., Mahmoud, M., Journal of Healthcare Engineering, 2021.

An Overview of ETL Techniques, Tools, Processes and Evaluations in Data Warehousing
Khan, B., Jan, S., Khan, W., Chughtai, M.I., Journal on Big Data, 6, 2024.

Performance assessment of classification algorithms on early detection of liver syndrome
Naseem, R., Khan, B., Shah, M.A., et al., Journal of Healthcare Engineering, 2020.

Exploring the landscape of automatic text summarization: a comprehensive survey
Khan, B., Shah, Z.A., Usman, M., Khan, I., Niazi, B., IEEE Access, 11,  2023.

Oussama El Othmani | Computer Science and Artificial Intelligence | Best Innovation Award

Mr. Oussama El Othmani | Computer Science and Artificial Intelligence | Best Innovation Award

Ecole Polytechnique de Tunisie | Tunisia

Oussama El Othmani is an emerging researcher and software engineer whose work bridges artificial intelligence, explainable machine learning, and applied computer engineering. He is currently pursuing a PhD in ETIC, following a strong academic foundation in computer engineering and preparatory mathematics–physics, with rigorous training in artificial intelligence, advanced learning algorithms, computer architecture, databases, and software methodology. Professionally, he has contributed to complex, mission-critical software systems, working across the full software development lifecycle while applying agile methodologies, object-oriented design, and hardware-aware optimization. His research interests focus on explainable and interpretable AI, machine learning, rough set theory, soft computing, computer vision, natural language processing, and AI applications in healthcare and high-stakes decision systems. He has led and contributed to multiple applied AI projects, including medical chatbots, diagnostic decision-support systems, blood anomaly detection, and antibiotic resistance classification. His scholarly output includes peer-reviewed publications in applied AI, He has also gained recognition through research-driven projects aligned with national and institutional initiatives. Overall, his profile reflects a strong balance of academic research, applied innovation, and real-world impact, positioning him as a promising contributor to the future of trustworthy and explainable artificial intelligence.

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