Ms. Vaishnavee Rathod | Analysis | Best Researcher Award
Research Scholar, Department of Computer Science Engineering SVNIT SURAT INDIA India
Vaishnavee Vijay Rathod is a Ph.D. candidate at SVNIT Surat, focusing on remote sensing and deep learning. She has earned her B.E., M.Tech., and is currently pursuing her Ph.D., with a specialized focus on satellite imaging, machine learning, and image processing. Vaishnavee has published multiple papers in renowned journals and conferences, contributing significantly to the fields of medical image analysis, remote sensing, and AI-based systems. She has also received recognition for her work, including the Best Paper Award at an international conference in 2020. Her innovative research on vehicle detection using computer vision and AI has gained attention in smart city development.
Publication Profile
🎓 Education
Vaishnavee completed her B.E. in Electronics and Telecommunication in 2018 from Thakur College of Engineering and her M.Tech. in Electronics and Digital Image Processing from GHRIET Nagpur in 2020. She is currently pursuing a Ph.D. in Satellite Imaging and Deep Learning at SVNIT Surat, specializing in deep learning-driven satellite image classification. Throughout her academic journey, she has achieved distinction and published several research papers in prominent conferences and journals.
💼 Experience
Vaishnavee has significant experience as a research scholar in deep learning and image processing. Her Ph.D. research includes the development of models for satellite image analysis and AI-based vehicle detection systems using UAV data. She has contributed to over 10 publications, including SCI-indexed journals and prestigious conferences. Additionally, Vaishnavee has been a recipient of several project funding awards, including SSIP 2.0 funding for her “RoadMitra” AI-based system project, showcasing her expertise in smart city technologies.
🏆 Awards & Honors
Vaishnavee has been recognized for her research contributions, including the “Best Paper Award” at the International Conference on Engineering Systems Design and Optimization in 2020. She has received various honors, including funding under SSIP 2.0 for her smart city project “RoadMitra,” aimed at detecting road issues using UAVs. Additionally, she has won awards for her research excellence and has received numerous accolades for her participation in international conferences and workshops.
🔬 Research Focus
Vaishnavee’s research focuses on the intersection of satellite imaging, remote sensing, and deep learning. She works on developing deep learning models for efficient satellite image classification and vehicle detection systems using UAV technology. Her work also extends to image enhancement, biomedical image processing, and AI applications in smart city infrastructure. Vaishnavee’s research contributes to the development of innovative solutions for urban challenges, including road crack detection, traffic analysis, and the enhancement of healthcare technologies using AI.
Publication Top Notes
- Title: Deep learning-driven UAV vision for automated road crack detection and classification
- Authors: Rathod, V.V., Rana, D.P., Mehta, R.G.
- Journal: Nondestructive Testing and Evaluation (2024)
- Citations: 0
- Status: Article in Press
- Title: Road Crack Detection and Classification Using UAV and Deep Transfer Learning Optimization
- Authors: Rathod, V., Rana, D., Mehta, R.
- Journal: Journal of the Indian Society of Remote Sensing (2024)
- Citations: 0
- Status: Article in Press
- Title: A computer vision approach to vehicle detection, classification, and tracking from UAV data for Indian traffic analysis
- Authors: Rathod, V.V., Rana, D.P., Mehta, R.G., Nath, V.
- Journal: IETE Journal of Research (2024)
- Citations: 0
- Status: Article in Press
- Title: An Extensive Review of Deep Learning Driven Remote Sensing Image Classification Models
- Authors: Rathod, V.V., Rana, D.P., Mehta, R.G.
- Conference: Proceedings of the 2022 3rd International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT 2022)
- Citations: 4