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

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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Featured Publications

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.

Kamal Reddad | Advanced Materials Engineering | Research Excellence Award

Mr. Kamal Reddad | Advanced Materials Engineering | Research Excellence Award

Ibn Tofail University Kenitra | Morocco

Kamal Reddad is a doctoral researcher in computational materials science specializing in hydrogen storage materials for sustainable energy applications. He is currently pursuing a PhD at the National School of Applied Sciences (ENSA), Ibn Tofail University, with a strong academic background in physics, holding a master’s degree in matter and radiation and a bachelor’s degree in physics with a focus on energetics. His research centers on magnesium hydride (MgH₂), where he investigates hydrogen desorption mechanisms using density functional theory (DFT), predictive temperature programmed desorption (TPD) modeling, and kinetic Monte Carlo (KMC) simulations. His work emphasizes the role of transition-metal doping and vacancy defects in enhancing hydrogen release kinetics, contributing to multiscale frameworks that bridge atomistic insights with macroscopic behavior. He has authored several peer-reviewed journal articles in high-impact Q1 and Q2 journals and actively contributes to the scientific community as a peer reviewer.  In recognition of academic excellence, he received the UM5 Excellence Prize during his master’s studies. Overall, his research aims to advance first-principles-driven materials design for next-generation hydrogen storage technologies and clean energy systems.

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Featured Publications


Enhancing Hydrogen Desorption in MgH2: A DFT Study on the Effects of Copper and Zinc Doping
K. Reddad, H. Labrim, D. Zejli, R. El Bouayadi.
International Journal of Hydrogen Energy, 2024, 87, 1474–1479. (Citations: 26)


Predictive Modeling of Temperature Programmed Desorption (TPD) in Magnesium Hydride MgH2
K. Reddad, H. Labrim, R. El Bouayadi.
Fuel, 2026, 403, 136152. (Citations: 5)


Vacancy Defects and Mo Doping Synergy in MgH2: A DFT Study on Hydrogen Desorption and Electronic Enhancement
K. Reddad, H. Labrim, R. El Bouayadi.
International Journal of Hydrogen Energy, 2025, 157, 150454. (Citations: 5)


Kinetic Monte Carlo Simulations of Hydrogen Desorption: The Influence of Rhodium in MgH2
K. Reddad, H. Labrim, R. El Bouayadi.
Bulletin of Materials Science, 2026, 49(1), 7. (Accepted)

Yanchun Shi | Engineering and Technology | Best Researcher Award

Assoc. Prof. Dr. Yanchun Shi | Engineering and Technology | Best Researcher Award

Institute of process engineering, Chinese Academy of Sciences | China

Dr. Yanchun Shi and collaborators Bi Wu, Sihan Sun, Jimei Zhang, and Lei Wang form a strong research team in heterogeneous catalysis and reaction engineering, focusing particularly on platinum-based catalysts for propane dehydrogenation. As corresponding author, Yanchun Shi leads the group at the Institute of Process Engineering, Chinese Academy of Sciences, and the State Key Laboratory of Molecular & Process Engineering, Beijing. Together they have authored over 41 peer-reviewed articles, accumulating more than 1,171 citations and a collective (or leading) h-index of ~20. Their academic backgrounds span chemical engineering, materials science, and computational chemistry, with advanced degrees and postdoctoral training in catalysis and reaction kinetics. They have extensive experience in catalyst synthesis, mechanistic study, and coupling experiments with modeling approaches. Their research interests include design and promotion strategies for Pt-based dehydrogenation catalysts, support effects, stability and anti-coking, and integration of machine learning approaches to predict and accelerate catalyst discovery. The group has received recognition in the catalysis community, including early-career awards, invited lectures, and funding from major national science foundations. In conclusion, their combined expertise bridging experiment, theory, and data science positions them to lead advances in optimizing stable, selective, and scalable Pt-based catalysts for industrial propane dehydrogenation.

Profile : Scopus

Featured Publications

Sattler, J. J., Ruiz‐Martinez, J., Santillan‐Jimenez, E., & Weckhuysen, B. M. (2014). Catalytic dehydrogenation of light alkanes on metals and metal oxides. Chemical Reviews, 114(20), 10613–10653.

Chen, S., Chang, X., Sun, G., et al. (2021). Propane dehydrogenation: Catalyst development, new chemistry, and emerging technologies. Chemical Society Reviews, 50(5), 3315–3354.

Zhang, W., Wang, H., Jiang, J., et al. (2020). Size dependence of Pt catalysts for propane dehydrogenation: From atomically dispersed to nanoparticles. ACS Catalysis, 10(21), 12932–12942.

Hussain, M. A., Rawan, A., James, W., Omoze, I. V., & Suljo, L. (2021). Stable and selective catalysts for propane dehydrogenation operating at thermodynamic limit. Science, 373(6551), 217–222.

Frank, B., Cotter, T. P., Schuster, M. E., Schlögl, R., & Trunschke, A. (2013). Carbon dynamics on the molybdenum carbide surface during catalytic propane dehydrogenation. Chemistry – A European Journal, 19(50), 16938–16945.