0009-0003-8883-2820 About
Dr. Usha P. Kosarkar, Assistant Professor in the Department of Science & Technology at G H Raisoni College of Engineering & Management, Nagpur, is a distinguished academic and researcher with a Ph.D. in Computer Science from G H Raisoni University. Her multidisciplinary educational foundation includes degrees in Computer Science (B.Sc., M.Sc.), Business Administration (MBA), and Education (B.Ed.). With over 15 years of teaching experience, Dr. Kosarkar specializes in Numerical Methods, Operational Research, Discrete Mathematics, Statistical Methods, PHP Programming, Python Programming, and R Programming.
She has made significant contributions to the field of deepfake detection and data security through impactful research published in SCOPUS and Springer-indexed journals and conferences. An accomplished author, she has published several books on computational methods and programming and holds patents related to advanced deepfake detection techniques using CNN and LSTM. Dr. Kosarkar has actively organized academic events like TechnoQuest, Raisoni Premier Cricket Showdown, and workshops on AI, NLP, and Linux programming, demonstrating her commitment to enhancing student engagement and fostering learning.
As a Reviewer and Technical Committee Member for international conferences such as IEEE AIC and CSCT, she continues to influence the research community. Her participation in prestigious workshops and faculty development programs at institutions like NIT Rourkela highlights her dedication to professional growth. A passionate educator, mentor, and researcher, Dr. Kosarkar is dedicated to contributing to academia through innovative teaching and groundbreaking research.
Employment
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G H RAISONI COLLEGE OF ENGINEERING AND MANAGEMENT Asst. Professor2007 - 2024
Education
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G H RAISONI COLLEGE OF ENGINEERING AND MANAGEMENT
Projects & Funding
Projects & funding information is unavailable.
Publications (6)
- A Hybrid Deep Learning Model for Robust Deepfake Detection Save
- Design an efficient VARMA LSTM GRU model for identification of deep-fake images via dynamic window-based spatio-temporal analysis Save
- Unmasking Deepfakes Advancements, Challenges, and Ethical Considerations Save
- Comprehensive study on image forgery techniques using deep learning Save
- An Analytical Perspective on Various Deep Learning Techniques forDeep Fake Detection Save
- Revealing and Classification of Deepfakes Video's Images using a Customize Convolution Neural Network Model Save