Sunil Kumar

Sunil Kumar obtained his Bachelor of Technology degree in 2008 from the Institute of Engineering and Technology (IET), CSJM University, Kanpur, India, and his Master of Technology degree in 2010 from the YMCA Institute of Engineering, Faridabad, India. He is a doctoral candidate at J.C. Bose University of Science and Technology, YMCA, Faridabad, Haryana, India. His research focuses on the identification of pulmonary diseases using machine learning and imaging techniques. He is an Assistant Professor in the Department of Computer Science Engineering at UIET, CSJM University, Kanpur, India. He has more than twelve years of experience teaching computer science and engineering.

Publications

Improving the Resilience of Supply Chains in a Post-COVID-19 Era: A Systematic Examination Utilizing ML
Improving the Resilience of Supply Chains in a Post-COVID-19 Era: A Systematic Examination Utilizing ML
Sunil Kumar, Tamanna M. Prajapati, Mamata Mayee Panda, Prachi Chhabra, Shilpi Dubey, Amar Pal Yadav. © 2024. 15 pages.
The COVID-19 pandemic has highlighted the critical need for supply chain resilience in the face of unforeseen disruptions. This research investigates the application of machine...
Predictive Modeling of Supply Chain Disruptions in the COVID-19 Pandemic Using Advanced Machine Learning Approaches
Predictive Modeling of Supply Chain Disruptions in the COVID-19 Pandemic Using Advanced Machine Learning Approaches
Sunil Kumar, Digvijay Pandey, Sunil Kumar, Abhishek Dwivedi, Abhishek Kumar Mishra, Mohit Singh Chauhan. © 2024. 13 pages.
In light of the COVID-19 pandemic, this research examines supply chain disruptions critically, highlighting their devastating effect on businesses like as drugs and perishable...
Explainable Bayesian-Optimized XGBoost Model for Component Failure Detection in Predictive Maintenance
Explainable Bayesian-Optimized XGBoost Model for Component Failure Detection in Predictive Maintenance
Hemant Kumar, Krishna Kant Bhartiy, Dharmesh Dhabliya, Rashi Agarwal, Sunil Kumar, Shivneet Tripathi. © 2024. 19 pages.
This study employs predictive maintenance to enhance the Explainable XGBoost Model for predicting failures in industrial components. The research utilizes a model that employs...
Comparison of the Theoretical and Statistical Effects of the PCA and CNN Image Fusion Approaches
Comparison of the Theoretical and Statistical Effects of the PCA and CNN Image Fusion Approaches
Ashi Agarwal, Binay Kumar Pandey, Poonam Devi, Sunil Kumar, Mukundan Appadurai Paramashivan, Ritesh Agarwal, Pankaj Dadheech. © 2023. 13 pages.
An image plays a vital role in today's environment. An image is a visual representation of anything that can be used in the future for recollecting or memorizing that scene....