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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Featured Publications

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

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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

Liu Ying | Mechanical Engineering | Research Excellence Award

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Dr. Liu Ying | Mechanical Engineering | Research Excellence Award

East China Jiaotong University | China

Ying Liu is a dedicated researcher and Lecturer specializing in vehicle engineering with a strong academic trajectory and growing research impact, reflected in an h-index of 2, 4 published documents, and 14 citations across indexed platforms. She earned a combined Master’s and PhD degree in Mechanical Engineering from Shanghai University, followed by a Bachelor’s degree in Mechanical Design, Manufacturing and Automation from Changchun Institute of Technology. Her professional career includes serving as a Lecturer at East China Jiaotong University, where she contributes to teaching, mentoring, and interdisciplinary research. Her work focuses on target optimization, image detection, reinforcement learning, computer vision, intelligent control, and smart vehicle technologies. She has completed five research projects, contributed to two national-level and multiple provincial and horizontal projects, and actively engages in consultancy with ten industry-linked initiatives. She has authored seven SCI-indexed papers, produced twenty-five patents, and participated in major scientific collaborations including a project funded with 5 million yuan. As a recipient of Jiangxi’s Early-Career Young Talents Program, she continues to advance innovation in intelligent vehicle systems. She remains committed to impactful research, advancing engineering applications, and contributing to societal and technological development.

Profile : Scopus

Featured Publications

Liu, Y., & [Co-author(s) if any]. (2025). Continuous path tracking of robots based on positioning error compensation with iterative learning control. IEEE Transactions on Instrumentation and Measurement.

Hacene Mellah | Electrical Engineering | Best Researcher Award

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Dr. Hacene Mellah | Electrical Engineering | Best Researcher Award

bouira university | Algeria

Dr. Hacene Mellah is an Associate Professor of Electrical Engineering at Université de Bouira, Algeria. His education includes an Ingenieur degree (2006) with a focus on electrical machine control, a Magister (2009) in electrical machines and control, a PhD (2020) in electrical machines, and his habilitation à diriger des recherches (HDR). He conducts research in estimation techniques of intrinsic machine parameters and thermal behaviour, fault diagnosis, renewable energy systems (wind, PV, hybrid), and control strategies for advanced electrical machines and drives. According to the AD Scientific Index (2025), his total h-index is 7, with about 91 citations and 24 indexed documents. A recent years his work has focused on observer design, neural networks, finite element modelling (FEM), and smart control of doubly fed induction generators among other topics. He has published in a number of peer-reviewed journals as well as international conferences. His contributions have enhanced understanding of sensorless control, fault modelling, and thermal monitoring for electrical machines, particularly under transient or non-linear conditions. Looking forward, he aims to expand his research in intelligent control, sustainable energy integration, and improved diagnostics. He seeks collaborations and impact through both theoretical development and practical applications in electrical drive systems.

Profiles : Scopus | Orcid | Google Scholar

Featured Publications

Sahraoui, H., Mellah, H., Drid, S., & Chrifi-Alaoui, L. (2021). Adaptive maximum power point tracking using neural networks for photovoltaic systems according grid. Electrical Engineering & Electromechanics, (5), 57–66.

Mellah, H., Hemsas, K. E., & Taleb, R. (2016). Intelligent sensor based Bayesian neural network for combined parameters and states estimation of a brushed DC motor. International Journal of Advanced Computer Science and Applications (IJACSA), 7(7), 230–235.

Mellah, H., & Hemsas, K. E. (2013). Simulations analysis with comparative study of a PMSG performances for small WT application by FEM. International Journal of Energy Engineering, 3(2), 55–64.

Maafa, A., Mellah, H., Ghedamsi, K., & Aouzellag, D. (2022). Improvement of sliding mode control strategy founded on cascaded doubly fed induction generator powered by a matrix converter. Engineering, Technology & Applied Science Research, 12(5), 9217–9223.

Bounasla, N., Hemsas, K. E., & Mellah, H. (2015). Synergetic and sliding mode controls of a PMSM: A comparative study. Journal of Electrical and Electronic Engineering, 3(1-1), 22–26.