0000-0001-6951-3240 About
Prof. Pravin Kumar Samanta has been working as an Assistant Professor since mid of 2017, in School of Electronics Engineering Department at Kalinga Institute of Industrial Technology (KIIT), Deemed to be University in Bhubaneswar. He did his B.Sc Honors in Physics with a first class degree from Rama Krishna Mission Residential College at Narendrapur, Kolkata (affiliated to Calcutta University). At graduate level he was awarded Scholarship for Higher Education (SHE) under the prestigious program, Innovation in Science Pursuit for Inspired Research (INSPIRE) conducted by the DST, Govt. of India. He holds B.Tech degree in Radio Physics and Electronics from the Institute of Radio Physics and Electronics under Calcutta University and pursued his M.Tech with specialization in VLSI Design from Calcutta University. He has two years research experience in VLSI domain under Special Manpower Development Project, 2016-17 (SMDP C2SD Project, by DeitY, Government of India). He has more than five years of teaching & research experiences. Currently he is perusing his Ph.D. in KIIT Deemed to be University in the area of Biomedical Image Processing. His research areas are mainly focused on Machine Learning, Deep Learning, Biomedical Signal & Image Processing and FPGA Design. Besides Technological knowledge, he has a deep thrust for research in Science and Spirituality, Cosmology, Vedic wisdom and Indian Culture.
Employment
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KIIT University Assistant Professor (II)2017 - Present
Education
Education history is unavailable.
Projects & Funding
Projects & funding information is unavailable.
Publications (33)
- Leveraging AI and Blockchain technology in 6G Network Save
- Structure-Aware Image Inpainting Using GANs with Low-Rank Regularization Save
- Transparent Intrusion Detection via Explainable LSTM–HMM Hybrid: SHAP and LIME-Based Interpretability Save
- MULTI-STAGE RESIDUAL U-NET WITH PARAMETRIC MISH ACTIVATION FOR BREAST CANCER DETECTION Save
- AI-Driven Circuit Design: Tracing the Path from Rules to Generative Models Save
- A Data Driven CNN Based Approach for Accurate Power System Fault Classification Save
- Fallacy-Aware Q-Learning: A Lightweight Risk-Aware Approach for Grid World Environments Save
- Hybrid GAN for Synthetic Data Augmentation in Marine Biodiversity: Enhancing Underwater Imaging and Species Classification Save
- Digital Twins: Strategies, Challenges, and Future Directions Save
- X-GAN: Explainable Generative Adversarial Networks for Rare Disease Data Augmentation and Clinical Insights Save