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

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)

Mona Ali | Computer Science and Artificial Intelligence | Research Excellence Award

Prof. Mona Ali | Computer Science and Artificial Intelligence | Research Excellence Award

King Faisal University | Saudi Arabia

Dr. Mona Abdelbaset Sadek Ali is an Associate Professor of Computer Science specializing in artificial intelligence, machine learning, and image processing. She earned her PhD in Computer Science (Wireless Computer Communications) from Cardiff University, UK, after completing an MSc in Information Technology (Image Processing) and a BSc in Information Technology with honors from Cairo University. With extensive academic experience spanning the UK, Saudi Arabia, and Egypt, her research integrates deep learning, optimization techniques, computer vision, IoT, mobile security, and intelligent healthcare systems. Dr. Ali has authored over 30 peer-reviewed research articles published in high-impact Web of Science-indexed journals and conferences, achieving an h-index of approximately 17, with more than 871 citations and 29 research documents. Her work frequently appears in Q1 and Q2 journals such as Mathematics, Electronics, Sustainability, PLOS ONE, and Applied Sciences. She has led and co-led numerous funded research projects supported by national and institutional bodies and has supervised multiple postgraduate MSc and PhD researchers. Her academic excellence has been recognized through competitive research funding and research poster awards. Overall, Dr. Ali’s career reflects sustained contributions to applied artificial intelligence and data-driven solutions with strong interdisciplinary and societal impact.

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Featured Publications


Tomato leaves diseases detection approach based on support vector machines

11th International Computer Engineering Conference (ICENCO), 246–250, 2015 · Citations: 222


Identifying two of tomatoes leaf viruses using support vector machine

Information Systems Design and Intelligent Applications, 2015 · Citations: 145


Detection of breast abnormalities of thermograms based on a new segmentation method

Federated Conference on Computer Science and Information Systems, 2015 · Citations: 78


Thermogram breast cancer prediction approach based on neutrosophic sets and fuzzy c-means algorithm

IEEE Engineering in Medicine and Biology Conference, 2015 · Citations: 76


A hybrid segmentation approach based on neutrosophic sets and modified watershed: A case of abdominal CT liver parenchyma

11th International Computer Engineering Conference (ICENCO), 2015 · Citations: 70

Muhammad Amer Qureshi | Biomedical Engineering | Best Researcher Award

Assoc. Prof. Dr. Muhammad Amer Qureshi | Biomedical Engineering | Best Researcher Award

University of Nizwa | Oman

Associate Professor Muhammad Amer Qureshi is a distinguished mathematician. He holds a Ph.D. in Mathematics , an M.S. in Engineering Sciences , and an M.Sc. in Computational Mathematics . His academic career has spanned roles as Associate Professor at the University of Nizwa (Oman), Associate and Assistant Professor at KFUPM (Saudi Arabia), and earlier appointments at GIK Institute and the University of Auckland. His research interests lie in numerical methods for ordinary differential equations (including one-step and multistep integrators, symplectic schemes), computational fluid dynamics (especially nano-hybrid fluids and heat transfer modeling), and the application of symmetries in general relativity (Noether’s theorem, space–time symmetries). He has supervised numerous undergraduate and graduate research projects, secured multiple externally funded grants, and published extensively in ISI/Scopus journals. Among his honors are repeated recognition in the Top 2% of global scientists, an Excellence in Teaching Award, and merit-based scholarships for Ph.D. and MS studies. In sum, his multifaceted contributions to theory, computation, and pedagogy mark him as a leading researcher and educator dedicated to advancing mathematics and engineering science.

Profile : Google Scholar

Featured Publications

Qureshi, M. A., & Hussain, S., & Sadiq, M. A. (2021). Numerical simulations of MHD mixed convection of hybrid nanofluid flow in a horizontal channel with cavity: Impact on heat transfer and hydrodynamic forces. Case Studies in Thermal Engineering, 27, 101321.

Qureshi, M. A. (2022). Thermal capability and entropy optimization for Prandtl–Eyring hybrid nanofluid flow in solar aircraft implementation. Alexandria Engineering Journal, 61(7), 5295–5307.

Qureshi, M. A. (2020). Numerical simulation of heat transfer flow subject to MHD of Williamson nanofluid with thermal radiation. Symmetry, 13(1), 10.

Qureshi, M. A. (2021). A case study of MHD driven Prandtl–Eyring hybrid nanofluid flow over a stretching sheet with thermal jump conditions. Case Studies in Thermal Engineering, 28, 101581.

Shahzad, F., Jamshed, W., Ibrahim, R. W., Nisar, K. S., & Qureshi, M. A., et al. (2021). Comparative numerical study of thermal features analysis between Oldroyd-B copper and molybdenum disulfide nanoparticles in engine-oil-based nanofluids flow. Coatings, 11(10), 1196.