Sajja Tulasi Krishna | Computer Science | Research Excellence Award

Published-on by Indian Scientist

Dr. Sajja Tulasi Krishna | Computer Science | Research Excellence Award

Koneru Lakshmaiah Education Foundation | India

Dr. Sajja Tulasi Krishna is a distinguished researcher and academician in Computer Science and Engineering, currently serving as an Assistant Professor at Koneru Lakshmaiah Education Foundation. She has extensive teaching experience in areas including CI/CD, Cloud DevOps, Python Full Stack Development, MERN Stack Web Development, Deep Learning, and Data Structures. Dr. Krishna earned her Ph.D. in Computer Science and Engineering and holds advanced degrees in M.Tech and B.Tech, reflecting a strong academic foundation. Her research focuses on deep learning, machine learning, biomedical image processing, and intelligent systems, with contributions in multi-omics integration, lung cancer detection, COVID-19 diagnosis, and medicinal plant classification. She has published 18 research articles in SCIE, Scopus, and IEEE journals, achieving a total of 488 citations with an h-index of 5 and an i10-index of 5. Dr. Krishna has presented her work at multiple national and international conferences, serving as a reviewer for reputed journals and conferences. She has received multiple awards recognizing her excellence in teaching, research, and technical contributions, including Best Teacher, International Excellence, and Young Researcher Awards. With her expertise, she continues to advance innovative research while mentoring students and contributing to the academic community globally.

Profiles: Google Scholar | Scopus | Orcid

Featured Publications

Krishna, S. T., & Kalluri, H. K. (2019). Deep learning and transfer learning approaches for image classification. International Journal of Recent Technology and Engineering (IJRTE), 7(5S4), 427–432.

Sajja, T. K., Devarapalli, R. M., & Kalluri, H. K. (2019). Lung cancer detection based on CT scan images by using deep transfer learning. Traitement du Signal, 36(4), 339–344.

Sajja, T. K., & Kalluri, H. K. (2020). A deep learning method for prediction of cardiovascular disease using convolutional neural network. Revue d’Intelligence Artificielle, 34(5), 601–606.

Sajja, T. K., & Kalluri, H. K. (2021). Image classification using regularized convolutional neural network design with dimensionality reduction modules: RCNN–DRM. Journal of Ambient Intelligence and Humanized Computing, 12(10), 9423–9434.

Sajja, T. K., & Kalluri, H. K. (2019). Gender classification based on face images of local binary pattern using support vector machine and back propagation neural networks. Advances in Modelling and Analysis B, 62(1), 31–35.

Chellavelu Vijayakumaran | Computer Vision | Research Excellence Award

Published-on by Indian Scientist

Dr. Chellavelu Vijayakumaran | Computer Vision | Research Excellence Award

SRM Institute of Science and Technology | India

Dr. Rajesh Pandiyan is a dedicated researcher and academic with expertise spanning microbiology, biochemistry, materials engineering, and pharmaceutical technology. He has served in progressive teaching and research roles across leading institutions, contributing significantly to interdisciplinary advancements. His research journey includes extensive work in nanocomposite-based ultrafiltration membranes, magnetic nanomaterials for targeted drug delivery, medicinal plant-based therapeutics, and biochemical models for autoimmune and inflammatory diseases. He has also worked on environmental biotechnology, wastewater remediation, tissue engineering, and drug discovery. With a strong academic foundation built through doctoral and postgraduate training in microbiology, biochemistry, and chemistry, he has developed a wide research portfolio combining materials chemistry with biological applications. His scholarly contributions include 67 peer-reviewed research and review articles, supported by an H-index of 19, i10-index of 27, and more than 1300 citations, reflecting global recognition of his work. He has delivered invited talks, chaired scientific sessions, and served as a resource person for various scientific forums, in addition to contributing as an editorial member for reputed journals. His awards span excellence in research, oral and poster presentations, and international recognition for scientific contributions. Overall, his work integrates innovation and multidisciplinary approaches aimed at solving challenges in healthcare, environment, and materials science.

Profile: Google Scholar

Featured Publications

Vijayakumaran, C., Muthusenthil, B., & Manickavasagam, B. (2020). A reliable next generation cyber security architecture for industrial internet of things environment. International Journal of Electrical and Computer Engineering, 10(1), 387.

Alagurajan, M., & Vijayakumaran, C. (2020). ML methods for crop yield prediction and estimation: An exploration. International Journal of Engineering and Advanced Technology, 9(3), 1–5.

Karthikeyan, L., Vijayakumaran, C., Chitra, S., & Arumugam, S. (2021). Saldeft: Self-adaptive learning differential evolution based optimal physical machine selection for fault tolerance problem in cloud. Wireless Personal Communications, 118(2), 1453–1480.

Chinnappa Annamalai, H. K., Vijayakumaran, C., & Vijayakumar, P. (2023). Optimal ElGamal encryption with hybrid deep-learning-based classification on secure Internet of Things environment. Sensors, 23(12), 5596.

Sweetline, B. C., Vijayakumaran, C., & Samydurai, A. (2024). Overcoming the challenge of accurate segmentation of lung nodules: A multi-crop CNN approach. Journal of Imaging Informatics in Medicine, 37(3), 988–1007.