Dr Muhammad Fayaz is an Associate Professor of Computer Science at UCA’s School of Arts and Sciences. Before joining UCA, he worked as a Visiting Lecturer and Teaching Assistant at the University of Malakand in Chakdara (Pakistan) for two years.
Throughout his academic career, he has published over 40 research papers in the reputed Web of Science and Scopus indexed international journals and conferences. His research interests include combinatorial optimisation problems, machine learning, image processing, Internet of Things (IoT), fuzzy inference systems, and other related areas.
He earned his PhD in Computer Engineering from Jeju National University (JNU) in Jeju, South Korea in 2019. During his studies, he worked at a Mobile Computing Lab at JNU. His responsibilities included preparing project proposals, designing IoT-based smart solutions, the application and implementation of artificial intelligence algorithms, and writing project reports. During his stay in South Korea, he also worked on several projects on the Underground System and the SAR-based Big Data Classification and Analysis of Disaster/Damage type, funded by the Electronics and Telecommunication Research Institute, and Korean Aerospace Research Institute, respectively.
His research interest is machine learning and optimisation, focusing on combinatorial problems IoT applications, image processing, and fuzzy systems.
Publications
- Muradi, Z. H., Hussain, A., & Fayaz, M. (2025). A Model for Leishmaniasis Disease Classification Based on Machine Learning and Deep Learning Algorithms. In 2025 IEEE 15th Symposium on Computer Applications & Industrial Electronics (ISCAIE) (pp. 01-05).
- Fayaz, M., Khan, J., & Bilal, M. (2024). Effectual Energy Consumption and User Comfort Optimization Based on Dynamic User Set Parameters in Electric Vehicles. IEEE Transactions on Intelligent Vehicles, 9(1), 178-189.
- Nawaal, B., Haider, U., Khan, I. U., & Fayaz, M. (2023). Signature-Based Intrusion Detection System for IoT. In Cyber Security for Next-Generation Computing Technologies (pp. 141-158). CRC Press.
- Alam, B., Hussain, A., & Fayaz, M. (2023). An Effective Approach for Air Quality Prediction in Bishkek Based on Machine Learning techniques. In Proceedings of the 2023 7th International Conference on Advances in Artificial Intelligence (pp. 42-47). ACM.
- Haider, J., Fayaz, M., & Qureshi, M. S. (2023). Enhancing Brain MRI Classification Through a Hybrid Machine Learning Methodology. In 2023 9th International Conference on Control, Decision and Information Technologies (CoDIT) (pp. 1996-2001). IEEE.