V. Vakharia

发表

A multiscale permutation entropy based approach to select wavelet for fault diagnosis of ball bearings

P. K. Kankar, V. K. Gupta, V. Vakharia, 2015 .

Modeling and prediction of surface roughness using multiple regressions: A noncontact approach

M. B. Kiran, V. Vakharia, M. Kiran, 2020, Engineering Reports.

Fault identification of ball bearings using Fast Walsh Hadamard Transform, LASSO feature selection, and Random forest classifier

V. Vakharia, V. Dave, H. Thakker, 2022, FME Transactions.

Surface roughness prediction of machined components using gray level co-occurrence matrix and Bagging Tree

M. B. Kiran, V. Vakharia, M. Kiran, 2020 .

Tool wear prediction in face milling of stainless steel using singular generative adversarial network and LSTM deep learning models

V. Vakharia, D. Pimenov, Khaled Giasin, 2022, The International Journal of Advanced Manufacturing Technology.

A comparison of feature ranking techniques for fault diagnosis of ball bearing

P. K. Kankar, V. K. Gupta, V. Vakharia, 2015, Soft Computing.

Experimental investigations and prediction of WEDMed surface of Nitinol SMA using SinGAN and DenseNet deep learning model

V. Vakharia, D. Pimenov, S. Wojciechowski, 2022, Journal of Materials Research and Technology.

Enhancing Tool Wear Prediction Accuracy Using Walsh–Hadamard Transform, DCGAN and Dragonfly Algorithm-Based Feature Selection

V. Vakharia, Milind Shah, Himanshu Borade, 2023, Sensors.

Estimation of Lithium-ion Battery Discharge Capacity by Integrating Optimized Explainable-AI and Stacked LSTM Model

V. Vakharia, Pankaj Sahlot, P. Nair, 2023, Batteries.

A comparison of feature ranking techniques for fault diagnosis of ball bearing

P. K. Kankar, V. Vakharia, V. K. Gupta, 2016, Soft Comput..

Efficient fault diagnosis of ball bearing using ReliefF and Random Forest classifier

Pavan Kumar Kankar, V. Vakharia, P. K. Kankar, 2017 .

A Comparative Study to Predict Bearing Degradation Using Discrete Wavelet Transform (DWT), Tabular Generative Adversarial Networks (TGAN) and Machine Learning Models

V. Vakharia, D. Pimenov, R. Chaudhari, 2022, Machines.

Bandgap prediction of metal halide perovskites using regression machine learning models

V. Vakharia, I. Castelli, Ankur Solanki, 2021, Physics Letters A.

Bearing Fault Diagnosis Using Feature Ranking Methods and Fault Identification Algorithms

Pavan Kumar Kankar, V. Vakharia, P. K. Kankar, 2016 .

Tool wear rate prediction using discrete wavelet transform and K-Star algorithm

V. Vakharia, S. Pandya, P. Patel, 2018, Life Cycle Reliability and Safety Engineering.

Tool wear rate prediction using discrete wavelet transform and K-Star algorithm

V. Vakharia, S. Pandya, P. Patel, 2018, Life Cycle Reliability and Safety Engineering.

Detection of Compound Faults in Ball Bearings Using Multiscale-SinGAN, Heat Transfer Search Optimization, and Extreme Learning Machine

V. Patel, V. Vakharia, Milind Shah, 2022, Machines.

Ball Bearing Fault Diagnosis Using Mutual Information and Walsh–Hadamard Transform

V. Vakharia, Sukhjeet Singh, Vipul K. Dave, 2019, Reliability, Safety and Hazard Assessment for Risk-Based Technologies.

Feature extraction and classification of machined component texture images using wavelet and artificial intelligence techniques

Vinay Vakharia, M. B. Kiran, Neil Jayeshbhai Dave, 2017, 2017 8th International Conference on Mechanical and Aerospace Engineering (ICMAE).

Ball Bearing Fault Diagnosis Using Supervised and Unsupervised Machine Learning Methods

Pavan Kumar Kankar, V. Vakharia, P. K. Kankar, 2015 .

Appliances Energy Prediction Using Random Forest Classifier

V. Vakharia, S. Vaishnani, H. Thakker, 2020, Lecture Notes in Mechanical Engineering.

Predicting Li-Ion Battery Remaining Useful Life: An XDFM-Driven Approach with Explainable AI

V. Vakharia, P. Nair, Himanshu Borade, 2023, Energies.

Watershed prioritization and decision-making based on weighted sum analysis, feature ranking, and machine learning techniques

Vinay Vakharia, Kishanlal Ramlal Darji, Dhruvesh Prehladbhai Patel, 2021, Arabian Journal of Geosciences.