Bilal Khan | Engineering and Technology | Research Excellence Award

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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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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.

Meriem Smati | Engineering and Technology | Research Excellence Award

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Ms. Meriem Smati | Engineering and Technology | Research Excellence Award

INSA Lyon – Polytechnique Montreal | France

Meriem Smati is a doctoral researcher in a dual-degree PhD program in Computer Science and Industrial Engineering at INSA Lyon and Polytechnique Montréal, focusing on advanced digital engineering and intelligent systems. She holds an Engineer and Master’s degree in Systems Engineering from the Higher School of Computer Science (ESI SBA) and a Bachelor’s degree in Information Systems and Software Engineering, graduating as valedictorian. Her academic and professional experience includes PhD-level research, adjunct lecturing in computer science, and multiple international research internships. Her research interests center on Digital Twins, System-of-Systems engineering, resilience modeling, cognitive and data-driven twins, IoT systems, anomaly detection, and smart city applications, integrating machine learning and simulation-based architectures. She has authored and co-authored peer-reviewed journal and conference publications, a scientific book, and several applied research reports. Her scholarly output is reflected in an approximate. Her work has been recognized through prestigious distinctions, including Best Paper and Best Poster awards at international conferences. Overall, her profile reflects a dynamic early-career researcher contributing impactful methodologies and architectures for resilient, intelligent, and sustainable digital systems.

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Chahira Cherif | Computer Science and Artificial Intelligence | Research Excellence Award

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Dr. Chahira Cherif | Computer Science and Artificial Intelligence | Research Excellence Award

Université Oran 1 | Algeria

Dr. Chahira Cherif is an Associate Professor of Computer Science with a strong academic and research profile in higher education and software systems research. She holds a State Engineering degree in Computer Science, a Magister (Master’s) degree, and a Ph.D. in Computer Science, reflecting a solid and progressive academic foundation in computing and information technologies. Her professional experience includes service as a State Computer Science Engineer before transitioning into academia, where she has been actively engaged in teaching at both Bachelor’s and Master’s levels under the LMD system, along with supervising Master’s theses. Her research interests focus on intelligent software systems, software evolution, system security, and advanced computing methodologies, with active involvement in nationally recognized research initiatives, including the PREFU project on intelligent evolution and security of software systems. She has participated in international conferences, workshops, and scientific gatherings across Algeria and France, contributing to scholarly exchange and collaboration. Her scientific output is reflected through peer-reviewed publications indexed in international databases, with an established h-index-1 supported by multiple research documents-5 and citation-4 records. Through her combined roles as educator, researcher, and project contributor, she continues to make meaningful contributions to computer science research and academic development, strengthening innovation, knowledge dissemination, and the advancement of intelligent and secure software systems.

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Predictive approach to the degree of business process change

C. Cherif, M.K. Abdi, A. Ahmad
International Journal of Computing and Digital Systems, 14(1), 10505–10513, 2023 · Citations: 5


Change impact study by Bayesian networks

C. Cherif, M.K. Abdi
Modeling Approaches and Algorithms for Advanced Computer Applications, pp. 429–438, 2013 · Citations: 5


Cancer classification through the selection of genes extracted from microarray data

M. Maiza, S. Chouraqui, C. Cherif, A. Taleb-Ahmed
Przegląd Elektrotechniczny, 101(4), 2025 · Citations: 4


Étude de l’impact de changement dans les systèmes à objet par les réseaux bayésiens

C. Chahira, A. Mustapha Kamel
Communication Science et Technologie, 13(1), pp. 90–108, 2015 · Citations: 4


Towards a probabilistic approach for better change management in BPM systems

C. Cherif
IEEE 23rd International Enterprise Distributed Object Computing Conference, 2019 · Citations: 3

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

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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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