Venkatesh Bhandage
Manipal Institute of Technology, KLEIT Hubballi, Visvesvaraya Technological University, Belagavi., Shri Jayachamarajendra college of Engineering, Basaveshwar Engineering College
0000-0002-9503-8196
About
Dr. Venkatesh Bhandage is an Associate Professor at the School of Computer Engineering, MIT, MAHE, Manipal. He previously worked in CSE at TCE, Gadag and ISE at SKSVMACET, Laxmeshwar. He interned at VMware, Bangalore, for a year. He earned his PhD from VTU, Belagavi, in 2020. He obtained his M.Tech in Computer Engineering, with university first rank, from SJCE, Mysore, in 2009 and a B.E. in CSE from BEC, Bagalkot, in 2007. His research interests include medical image processing, artificial intelligence, human pose/dance pose classification with evaluation, and classification of skin diseases, neurological disorders and spine degeneration. He has published more than 40 papers in reputed journals and conferences. He is guiding 3 research scholars and co-guiding 4 research scholars. He is also guiding UG and PG students for research projects. His present research is focused on AI in healthcare. He is presently working on the classification of facial acne scars, the classification of neurological disorders, the severity-based classification of spinal cord degeneration and knee arthritis.
Employment
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Manipal Institute of Technology Teaching and Research2021 - Present
Education
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KLEIT Hubballi, Visvesvaraya Technological University, Belagavi. Doctor of Philosophy2015 - 2021
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Shri Jayachamarajendra college of Engineering Master of Technology2007 - 2009
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Basaveshwar Engineering College Bachelor of Engineering2003 - 2007
Projects & Funding
Projects & funding information is unavailable.
Publications (42)
- An exhaustive review of the recent advancements in sentiment analysis on Dravidian languages Save
- DVTPRED: an explainable machine learning framework for the early prediction of deep vein thrombosis Save
- Explainable-AI Guided Deep Learning for Glaucoma Detection: Feature-Weighting Approach Using SHAP Values Save
- Harnessing Technological Solutions to Address Growing Demand for Healthcare Services: A Review Save
- Hybrid deep learning network enabled brain tumor detection with shape-aware loss-based structure correcting adversarial network using MRI Save
- Machine Learning Techniques for the Detection of Melasma Disease: A Comprehensive Review Save
- Explainable machine learning models for predicting current and voltage in photovoltaic systems Save
- Synergizing EEG and MRI for Autism Classification: Exploring the Feasibility of Machine Learning Models Save
- Univariate Data Analysis for Demand Forecasting in Blood Supply Chain Using Time Series and Machine Learning Models Save
- Tumour GAN-based augmentation and hybrid deep learning model for classification of brain tumour using MRI images Save