Nurul Amin Choudhury

About

Nurul Amin Choudhury is working as an Assistant Professor in the Department of CSE, NIT Meghalaya, India. He received the B. Tech degree in Computer Science and Engineering from the Jawaharlal Nehru Technological University, Hyderabad, India, in 2018. He did his Master of Technology (M. Tech) in Computer Science and Engineering at the National Institute of Technology (NIT) Meghalaya, Shillong, India, and subsequently did his Doctor of Philosophy (PhD) in CSE from the National Institute of Technology (NIT) Silchar, India. His current research interests include Machine Learning, Deep Learning, the Internet of Things (IoT), Data Analytics, AI in IoT for healthcare, and Smart Learning algorithms.

Employment

• National Institute of Technology Meghalaya Assistant Professor
  2025 - Present
• National Institute Of Technology Silchar Research Scholar
  2021 - 2025

Education

Education history is unavailable.

Projects & Funding

Projects & funding information is unavailable.

Publications (17)

• KNEE-HAR: A ConvLSTM-Based Lightweight Hybrid Deep-Learning Model for Human Activity Recognition With Knee Abnormalities Using Raw Physiological Sensor Data
  IEEE Sensors Journal 2025 DOI: 10.1109/JSEN.2025.3540903
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• An Efficient Ensemble Framework for Human Gait Recognition Using CNN-LSTM With Extra Tree Classifier and Smartphone Sensors in Real-World Environment
  IEEE Sensors Letters 2024 DOI: 10.1109/LSENS.2024.3435719
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• An Efficient and Optimized CNN-LSTM Framework for Complex Human Activity Recognition System Using Surface EMG Physiological Sensors and Feature Engineering
  2024 IEEE Students Conference on Engineering and Systems: Interdisciplinary Technologies for Sustainable Future, SCES 2024 2024 DOI: 10.1109/SCES61914.2024.10652396
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• Effect of Shallow Learning Techniques and Feature Selection for Breast Cancer Detection
  Lecture Notes in Electrical Engineering 2024 DOI: 10.1007/978-981-99-6855-8_13
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• In-depth analysis of design & development for sensor-based human activity recognition system
  Multimedia Tools and Applications 2024 DOI: 10.1007/s11042-023-16423-5
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• Enhanced Complex Human Activity Recognition System: A Proficient Deep Learning Framework Exploiting Physiological Sensors and Feature Learning
  IEEE Sensors Letters 2023 DOI: 10.1109/LSENS.2023.3326126
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• An Efficient and Lightweight Deep Learning Model for Human Activity Recognition on Raw Sensor Data in Uncontrolled Environment
  IEEE Sensors Journal 2023 DOI: 10.1109/JSEN.2023.3312478
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• An Adaptive Batch Size-Based-CNN-LSTM Framework for Human Activity Recognition in Uncontrolled Environment
  IEEE Transactions on Industrial Informatics 2023 DOI: 10.1109/TII.2022.3229522
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• Gait Recognition Using Activities of Daily Livings and Ensemble Learning Models
  Advances in IoT and Security with Computational Intelligence 2023 DOI: 10.1007/978-981-99-5085-0_20
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• An Efficient CNN-LSTM Approach for Smartphone Sensor-Based Human Activity Recognition System
  2022 5th International Conference on Computational Intelligence and Networks (CINE) 2022 DOI: 10.1109/cine56307.2022.10037495
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