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Dr. Md. Masudur Rahman studied Computer Science Engineering and was employed by JASSO as a postdoctoral researcher at Kanagawa University in Japan in 2024. He obtained his Ph.D. from Kanagawa University, Japan, in 2023 under the supervision of Professor Minoru Yoshida, with a Japanese Government Scholarship (MEXT scholarship). Throughout his doctoral studies, Dr. Rahman collaborated with esteemed scholars, including Professor Hidetoshi Oya at Tokyo City University, Professor Shuji Kawasaki at Iwate University, and Professor Sergio Albeverio at Bonn University in Germany. Presently, Dr. Rahman is a faculty member in the Department of Information and Communication Engineering at Noakhali Science and Technology University. He has made significant contributions to both academia and the software industry. He served as a faculty member in the Computer Science and Engineering Department at Uttara University in Bangladesh, where he shared his expertise and knowledge, and has also worked professionally as a software engineer in the industry. His research interests encompass Code Smell Detection, System Design, Human–Computer Interaction, Human Healthcare Development, Medical Image and Signal Processing, Data Mining, Learning Analytics, Explainable AI for Feedback, and Software Clustering & Testing.
Thesis Title: Hybrid Fusion-Based Multimodal Approach for Detecting Cyberbullying in Bengali Social Media Memes
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Thesis Title: An Ensemble Machine Learning Approach for Accurate Asthma Prediction
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Thesis Title: Advancing Bangla Sign Language Recognition Through Dataset Creation, Model Comparison and Deployment
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Thesis Title: An Effective Pre-Processing of Maize Leaf Images & Deep Learning Ensemble Approach for Accurate Disease Detection
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Thesis Title: Driver Drowsiness Detection using CNN: An Effective Dataset Preparation Approach
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Thesis Title: Fish Inspection with Machine Learning: A Method for Freshness and Species Identification
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Thesis Title: A Sustainable Method for Lung Segmentation using Watershed algorithm with Sobel filter and detection of cancer from CT Images with risk level using Deep Learning
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Thesis Title: Building Surface Crack Detection Using Deep Learning Technology
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Noakhali Science and Technology University