Sunil Datt Sharma | Computer Science | Best Researcher Award

Posted on by Indian Scientist

Dr. Sunil Datt Sharma | Computer Science | Best Researcher Award

Central University of Jammu | India

Dr. Sunil Datt Sharma is a distinguished researcher in the fields of Digital Signal Processing, Adaptive Signal Processing, and Machine Learning Applications, recognized for his contributions across computational biology, biomedical signal analysis, and intelligent imaging systems. With 289 citations, an h-index of 9, and 9 indexed documents, his research is widely acknowledged for its technical depth and interdisciplinary impact. He has authored numerous journal articles, conference papers, and book chapters covering areas such as CpG island detection, promoter identification using deep learning, image de-noising, transfer learning for fault diagnosis, micro-Doppler signature analysis, anisotropic diffusion models, and advanced frequency-domain algorithms. His academic background encompasses strong training in electronics, computing, and signal processing, complemented by extensive experience in teaching, research, and scholarly reviewing for reputed international journals. His research interests span computational genomics, machine learning-based biomedical systems, pattern recognition, and intelligent signal analysis. He has been actively engaged in professional peer-review activities for more than twenty journals, reflecting his standing within the global research community. His work integrates innovative algorithms with real-world applications, contributing to both theoretical advancement and practical solutions. Dr. Sharma continues to advance cutting-edge research aimed at addressing complex challenges across science and engineering.

Profile: Google Scholar

Featured Publications

Sharma, S. D., Shakya, K., & Sharma, S. N. (2011). Evaluation of DNA mapping schemes for exon detection. In 2011 International Conference on Computer, Communication and Electrical Technology (ICCCET). (Cited by: 42).

Sharma, S., Sharma, S. N., & Saxena, R. (2020). Identification of short exons disunited by a short intron in eukaryotic DNA regions. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 17(5). (Cited by: 33).

Sharma, S. D., Saxena, R., & Sharma, S. N. (2015). Identification of microsatellites in DNA using adaptive S-transform. IEEE Journal of Biomedical and Health Informatics, 19(3), 1097–1105. (Cited by: 23).

Garg, P., & Sharma, S. (2020). Identification of CpG islands in DNA sequences using short-time Fourier transform. Interdisciplinary Sciences: Computational Life Sciences, 12(3), 355–367. (Cited by: 19).

Sharma, S. D., Saxena, R., Sharma, S. N., & Singh, A. K. (2015). Short tandem repeats detection in DNA sequences using modified S-transform. International Journal of Advances in Engineering and Technology, 8(2). (Cited by: 16).

Subhodeep Moitra | Artificial Intelligence | Young Researcher Award

Posted on by Indian Scientist

Mr. Subhodeep Moitra | Artificial Intelligence | Young Researcher Award

Techno College Hooghly | India

Subhodeep Moitra is a computer science researcher focused on advancing artificial intelligence through the fusion of human-like visual perception and cognition. His academic foundation spans computer applications at both undergraduate and postgraduate levels, where he built strong expertise in machine learning, deep learning, computer vision, neural networks, adversarial robustness, and cognitive modeling. His research explores self-supervised reconstruction, adversarial recovery, AGI-oriented theoretical computing, medical prediction systems, and environmental forecasting, with publications in journals, conferences, preprint platforms, and book chapters. He has contributed to projects ranging from temperature forecasting and brain-stroke detection to adversarially robust autoencoders and AGI theory. His professional experience includes serving as a visiting faculty member, teaching programming, mentoring research projects, and engaging in active collaborative work. His technical skills extend across Python, deep learning frameworks, MERN stack development, and cloud-based AI tools, supported by multiple certifications from NASA, NVIDIA, CERN, IBM, Oracle, and Coursera. He has presented papers at international conferences and earned best paper presentation awards for his contributions in machine learning–driven forecasting and adversarial perception. His long-term research interest lies in building unified AI systems capable of perceiving, reasoning, and adapting with human-inspired intelligence, aiming to push the boundaries of next-generation cognitive AI.

Profile: Google Scholar

Featured Publications

Moitra, S., & Banerjee, D. (n.d.). Robustness as Latent Symmetry: A Theoretical Framework for Semantic Recovery in Deep Learning. OSF.

Moitra, S., & Banerjee, D. (n.d.). Are We Even on the Right Track? A Theoretical Framework for AGI Beyond Classical Computation. Authorea Preprints.

Moitra, S., & Banerjee, D. (n.d.). Skip the Chaos: A Self-Supervised Learning-Powered Autoencoder for Adversarial Recovery. OSF.

Pintu, P., Subhodeep, M., & Deblina, B. (n.d.). The mystery of Neural Network: Linked with quantum mechanics and universe. ResearchGate.

Pal, P., Moitra, S., & Banerjee, D. (n.d.). The mystery of Neural Network: Linked with quantum mechanics and universe.