Vinay Vakharia
Pandit Deendayal Energy University, Indian Institute of Information Technology Design and Manufacturing Jabalpur
0000-0001-9791-7525
About
Dr. Vinay Vakharia is working as Associate Professor in the Department of Mechanical Engineering at the School of Technology, Pandit Deendayal Energy University (PDEU), Gandhinagar, Gujarat, India. He earned his Ph.D. from PDPM IIITDM Jabalpur, where his research focused on Bearing Fault Diagnosis using Signal Processing Techniques and Machine Learning applications. Dr. Vakharia was awarded a silver medal for outstanding academic performance in Mechanical Engineering (PG) during IIITDM Jabalpur's Convocation.
Dr. Vakharia has been internationally recognized for his contributions. He delivered an invited talk at the 12th International Conference on Sound and Vibration (ICSV) in Florence, Italy. He was honored with the Best Oral Presentation Award at the 7th International Conference on Mechanical and Aerospace Engineering (ICMAE), supported by IEEE, in Prague, Czech Republic. He has also delivered a several keynote address at various national and international workshop.
In addition, Dr. Vakharia has chaired and co-chaired sessions at several national and international conferences organized by prestigious institutions like NITs. He has published 65 research papers in SCI/Scopus-indexed journals and conferences and has submitted and published 14 Indian Design and Utility Patents.
Employment
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Pandit Deendayal Energy University Associate Professor2016 - Present
Education
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Indian Institute of Information Technology Design and Manufacturing Jabalpur Ph.D.2012 - 2015
Projects & Funding
Projects & funding information is unavailable.
Publications (35)
- Optimizing Production Supply Chain With Markov Jump System for Logistics Collaboration Save
- Deep Learning-Enhanced Small-Sample Bearing Fault Analysis Using Q-Transform and HOG Image Features in a GRU-XAI Framework Save
- Deep Learning-Based Stock Market Prediction and Investment Model for Financial Management Save
- Optimizing Supply Chain Management Through BO-CNN-LSTM for Demand Forecasting and Inventory Management Save
- Utilizing TGAN and ConSinGAN for Improved Tool Wear Prediction: A Comparative Study with ED-LSTM, GRU, and CNN Models Save
- Predicting Li-Ion Battery Remaining Useful Life: An XDFM-Driven Approach with Explainable AI Save
- Enhancing Tool Wear Prediction Accuracy Using Walsh–Hadamard Transform, DCGAN and Dragonfly Algorithm-Based Feature Selection Save
- Estimation of Lithium-ion Battery Discharge Capacity by Integrating Optimized Explainable-AI and Stacked LSTM Model Save
- A Comparative Study to Predict Bearing Degradation Using Discrete Wavelet Transform (DWT), Tabular Generative Adversarial Networks (TGAN) and Machine Learning Models Save
- Tool wear prediction in face milling of stainless steel using singular generative adversarial network and LSTM deep learning models Save