Rate My Professor K. Bhargavi

Dr. K. Bhargavi serves as Professor and Head of the Department of Information Technology at Keshav Memorial Institute of Technology (KMIT) in Hyderabad, Telangana. She holds a PhD in Soft Computing and possesses M.Tech qualifications, contributing over 19 years to academic teaching and research in computer science fields. Her leadership extends to being the Advisor for the IEEE Computer Society Student Branch at KMIT, fostering student engagement in professional societies. Additionally, she is an Editorial Board Member for the International Journal of Science, Engineering and Technology (IJSET).

Dr. Bhargavi's research focuses on soft computing, machine learning, artificial intelligence applications in healthcare, image and video processing, and related domains. Notable publications include 'Deep Learning Techniques for Lung Cancer Recognition' published in 2024 in Engineering, Technology & Applied Science Research; 'IVFD: An Intelligent Video Forgery Detection Framework' in 2025 in Indonesian Journal of Electrical Engineering and Informatics; 'Identifying Novel Compounds for Targeted Cancer Therapies' in the Journal of Applied Zoology; 'Artificial Intelligence-Driven Drug Discovery: Identifying Novel Compounds for Targeted Cancer Therapies' presented at the International Conference on Intelligent Computing and Emerging Communication Technologies in 2023; 'Automated Skin Disease Detection Using Machine Learning Techniques' in 2022 in Innovations in Signal Processing and Communication; 'Multiclass Classification to Predict the Level of Storm and Damages Using Support Vector Machine' in 2018 at the Fourteenth International Conference on Information Processing; and 'Classification of DNA sequence using soft computing techniques: a survey' in 2016 in Indian Journal of Science and Technology. She holds patents including 'A Medical Image Processing System, Image Processing Method, and Medical Image Processing Device'. Her scholarly output reflects contributions to medical diagnostics, forgery detection, and computational biology through AI and ML methodologies.