0000-0002-3125-771X About
Dr. Pradeep Kumar Das is working as an Assistant Professor with the Department of Electronics and Communication Engineering at National Institute of Technology Warangal since December 2024. He has more than 2 years of experience an Assistant Professor (Senior Grade I) with the School of Electronics Engineering at Vellore Institute of Technology Vellore. He has been recognised in Stanford/Elsevier - Top 2% Scientists-2025. He is serving as an Associate Editor in IEEE Transactions on Instrumentation and Measurement. He received his Ph.D. degree in electronics and communication engineering from the National Institute of Technology Rourkela (NIT Rourkela). His research interests include computer vision, medical image processing, machine learning, deep learning, and biomedical signal processing. He has published in several Elsevier and IEEE Journals/ IEEE Transactions. He is an Active Reviewer in several IEEE and Elsevier journals and IEEE Transactions, such as IEEE Transactions on Cybernetics; IEEE Transactions on Systems, Man, And Cybernetics: Systems; IEEE Transactions on Geoscience and Remote Sensing; IEEE Transactions on Circuits And Systems for Video Technology; IEEE Transactions on Emerging Topics in Computational Intelligence; IEEE Access; Engineering Applications of Artificial Intelligence; and Neural Networks.
Employment
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National Institute of Technology Warangal Assistant Professor2024 - Present
Education
Education history is unavailable.
Projects & Funding
Projects & funding information is unavailable.
Publications (28)
- OSLTBDNet: Orthogonal softmax layer-based tuberculosis detection network with small dataset Save
- A Hybrid Deep Learning Framework for Automatic Detection of Brain Tumours Using Different Modalities Save
- An efficient deep learning system for automatic detection of Acute Lymphoblastic Leukemia Save
- An Integrated Framework for Infectious Disease Control Using Mathematical Modeling and Deep Learning Save
- Enhanced detection of acute leukemia: A hybrid machine learning framework with adaptive weight-optimized level set evolution Save
- ACDSSNet: Atrous Convolution-Based Deep Semantic Segmentation Network for Efficient Detection of Sickle Cell Anemia Save
- An efficient deep learning network with orthogonal softmax layer for automatic detection of tuberculosis Save
- An automatic sparse-based deep cascade framework with multilayer representation for detecting breast cancer Save
- A Deforestation Detection Network Using Deep Learning-Based Semantic Segmentation Save
- An efficient deep learning scheme to detect breast cancer using mammogram and ultrasound breast images Save