Sivakamasundari | Computer Science | Best Researcher Award

Published-on by Indian Scientist

Ms. Sivakamasundari | Computer Science | Best Researcher Award

SRM Institute of Science and Technology | India

Ms. P. Sivakamasundari is a dedicated academic and researcher in Computer Science and Engineering, recognized for her contributions to deep learning-based medical image analysis. With qualifications spanning Diploma, Bachelor’s, and Master’s degrees in Computer Science and Engineering, she is currently pursuing her Ph.D. at SRM Institute of Science and Technology. She has extensive teaching experience as an Assistant Professor for more than a decade, during which she has guided students in core computing subjects including algorithms, computation theory, compiler design, and image classification. Her research focuses on advanced deep learning frameworks for healthcare applications, particularly diabetic retinopathy and diabetic foot ulcer detection, resulting in book chapters, conference publications, and journal manuscripts under review. She has published and filed patents related to medical imaging and automated disease detection systems, demonstrating her innovation-driven approach. Her scholarly presence includes 1 citation, 1 h-index, and 0 i10-index, indicating emerging research visibility. She has completed multiple professional certifications and participated in workshops, FDPs, and internships in machine learning, biometrics, accelerated computing, and high-performance healthcare analytics. Her work reflects strong commitment toward applying AI for societal benefit, and she continues to advance her expertise through active research and academic contributions.

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Featured Publications

Sivakamasundari, P., Anandhi, S., Kumaran, A. A., Vijayakumar, K., Birnica, Y. J., & others. (2024). Early detection of glaucoma utilizing retinal nerve fiber layer (RNFL) investigation. International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems.

Sivakamasundari, P., & Niranjana, G. (2025). An automatic detection and classification of diabetic foot ulcers using Chebyshev chaotic ladybug beetle optimized extended Swin Transformer–InceptionV3 model. Biomedical Signal Processing and Control, 110, 108268.

Gomathi, G., Sumathy, V., Sivakamasundari, P., & Deepa, R. (2024). A various approaches of machine learning algorithms for kidney disease prediction. International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems.

Sivakamasundari, P., & Niranjana, G. (2024). Diabetic foot ulcer classification using deep learning approach. International Conference on Computer, Communication and Signal Processing (ICCCSP).

Sivakamasundari, P., & Niranjana, G. (2023). A critique on deep learning methodologies employed for the identification of diabetic retinopathy using fundus images. Intelligent Computing and Control for Engineering and Business Systems (ICCEBS).

Sunil Datt Sharma | Computer Science | Best Researcher Award

Published-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.

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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).