Md Rahat Kader Khan received his B.Sc. in Computer Science and Engineering from Ahsanullah University of Science and Technology, Dhaka, Bangladesh, in 2022. He is currently working toward his M.S. in Computer Engineering from the University of Mississippi, Oxford, USA. He worked as a machine learning engineer at Canada Syntex Machine Learning, Alberta, Canada for about 1.5 years. He is currently working as both a teaching assistant and research assistant with the Department of Electrical and Computer Engineering, University of Mississippi. His research interests include artificial intelligence, machine learning, and computer vision.
Publications:
Tamador Mohaidat, MD Rahat Kader Khan, Kasem Khalil. “Curvature-Based Piecewise Linear Approximation Method of GELU Activation Function in Neural Networks.” In 2024 IEEE International Conference on Artificial Intelligence, Blockchain, and Internet of Things, (AIBThings). IEEE, 2024. [< < PDF > >]
MD Rahat Kader Khan, Tamador Mohaidat, Kasem Khalil. “Predicting Acute Myocardial Infarction Using Machine Learning Algorithms.” In 2024 IEEE International Conference on Artificial Intelligence, Blockchain, and Internet of Things, (AIBThings). IEEE, 2024. [< < PDF > >]
Kasem Khalil, Samiul Islam Niloy, Tamador Mohaidat, MD Rahat Kader Khan, Magdy Bayoumi, “Efficient Deep Learning Approach for Arthritis Prediction.” In 2024 IEEE International Conference on Artificial Intelligence, Blockchain, and Internet of Things, (AIBThings). IEEE, 2024. [< < PDF > >]
Kasem Khalil, MD Rahat Kader Khan, Tamador Mohaidat, Magdy Bayoumi, “Enhanced Heart Attack Detection with Neural Networks.” In 2024 International Conference on Intelligent Systems, Blockchain, and Communication Technologies, (ISBCom). Springer, 2024. [< < PDF > >]
Kasem Khalil, Md Rahat Kader Khan, M. Bayoumi and Ahmed Sherif, "Efficient Hardware Design of Convolutional Neural Networks for Accelerated Deep Learning," 2024 IEEE 67th International Midwest Symposium on Circuits and Systems (MWSCAS), Springfield, MA, USA, 2024, pp. 1075-1079. [< < PDF > >]