M Shamim Kaiser
Also known as: M S Kaiser
Jahangirnagar University, BRAC, Anglia Ruskin University, Asian Institute of Technology, Eastern University of Bangladesh, University of Dhaka
Research areas: Computer Science · Information Systems · Artificial Intelligence in Healthcare · Emotion and Mood Recognition · Internet of Things and AI
About
Dr. M Shamim Kaiser is currently working as a Professor at the Institute of Information Technology of Jahangirnagar University, Savar, Dhaka-1342, Bangladesh. He received his Bachelor's and Master's degrees in Applied Physics Electronics and Communication Engineering from the University of Dhaka, Bangladesh in 2002 and 2004 respectively, and the Ph. D. degree in Telecommunication Engineering from the Asian Institute of Technology (AIT) Pathumthani, Thailand, in 2010. His current research interests include Data Analytics, Machine Learning, Wireless Network & Signal processing, Cognitive Radio Network, Big data and Cyber Security, Renewable Energy. He has authored more than 100 papers in different peer-reviewed journals and conferences.
He is an Associate Editor of the IEEE Access Journal, Guest Editor of Brain Informatics Journal, and Cognitive Computation Journal. Dr. Kaiser is a Life Member of Bangladesh Electronic Society; Bangladesh Physical Society. He is also a senior member of IEEE, USA and IEICE, Japan, and active volunteer of the IEEE Bangladesh Section. He is the founding Chapter Chair of the IEEE Bangladesh Section Computer Society Chapter.
Employment
- 2011 - Present
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BRAC Assistant Professor2011 - 2011
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Eastern University of Bangladesh Assistant Professor2010 - 2011
- 2007 - 2007
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Daffodil International University Lecturer2005 - 2006
Education
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Anglia Ruskin University Post Doc2016 - 2017
- 2007 - 2010
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University of Dhaka Master2002 - 2004
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University of Dhaka Bachelor of Science1997 - 2001
Projects & Funding
Projects & funding information is unavailable.
Publications (247)
- A Multi-modal Stacking Ensemble Framework for Dengue Outbreak Prediction in Dhaka Save
- An Explainable Cleanlab-Guided Uncertainty-Based Active Learning Framework for Data-Efficient Parkinson’s Disease Classification Save
- Impact of Temperature-Humidity Index and Feeding Practices on Milk Yield in Dairy Cattle Using Machine Learning Save
- Revolutionizing Residential Living: A Systematic Review of IoT Applications, Challenges, and Future Trends in Smart-Home Automation Save
- Privacy‐preserving multi‐agent systems for human‐centred inclusive healthcare AI Save
- Aerial intelligence Save
- A multi-view RGB image dataset of four potato varieties with visually assessed scab and dry-rot severity under dual-background imaging conditions Save
- Breaking barriers in federated transfer learning: A systematic review on federated transfer learning for non-overlapping domains Save
- MVIC-Net: Multi-view interactive convolutional networks for ECG-based sleep apnea detection with explainable AI Save
- Quantum-Enhanced Multimodal Fusion Networks for Integrated Cancer Diagnosis: Combining CT, Genomics, and Clinical Records Save
- Enhanced Facial Realism in Personalized Diffusion Models: A Memory-Optimized DreamBooth Implementation for Consumer Hardware Save
- A Comparative Study of Machine Learning Models for Identification of Antiviral Peptides Using Various Encoded Features Save
- A Secure and Interpretable Federated Learning Framework for Diabetes Prediction with Blockchain-Enabled Security Save
- Anemia Detection Using Explainable Boosting Machine Save
- Exploring Stacking Algorithm for PCOS Detection Save
- Lightweight Machine Learning Model for Speech Emotion Recognition in English and Bengali Language Save
- Blockchain and InterPlanetary Framework for Decentralized and Secure Electronic Health Record Management Save
- Intelligent Terrain Mapping with a Quadruped Spider Robot: A Bluetooth-Enabled Mobile Platform for Environmental Reconnaissance Save
- Self-Balancing Mobile Robot with Bluetooth Control: Design, Implementation, and Performance Analysis Save
- QiMARL: Quantum-Inspired Multi-Agent Reinforcement Learning Strategy for Efficient Resource Energy Distribution in Nodal Power Stations Save