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.

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.

Profile: Google Scholar

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).