Dr. Yogesh Kumar Rathore
Shri Shankaracharya Institute of Professional Management and Technology, Raipur Institute of Technology, National Institute of Technology
0000-0003-0314-5611
About
Dr. Yogesh Kumar Rathore has completed a Ph. D. in Information Technology at the esteemed National Institute of Technology, Raipur, in 2024. Before that, he completed a Master of Technology from Chhattisgarh Swami Vivekanand Technical University in 2011, and a Bachelor of Engineering from Pt. Ravishankar Shukla University in 2005. He has over 19 years of experience in Computer Science Engineering and has held positions as an Associate Professor in the Department of Computer Science Engineering at various institutions in Chhattisgarh. He is working at Shri Shankaracharya Institute of Professional Management and Technology (SSIPMT) as an Associate Professor in the Department of Computer Science Engineering.
Dr. Rathore has pursued continuous learning through certifications such as IBM Certified Rational Developer and Deep Learning from institutions like C-DAC, leadingindia.ai, Bennett University Noida, and Nvidia. He has made significant contributions to academia, having over 50 publications in reputable journals and conferences, including notable entries in SCI, Scopus, and UGC care journals. Additionally, he has authored an edited book, contributed chapters to internationally edited books, and authored a textbook on data mining. Dr. Rathore has also been involved in patenting, with three Indian patents published and two USA-granted patents, showcasing his expertise in Computer Science Engineering.
Employment
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Shri Shankaracharya Institute of Professional Management and Technology Associate Professor and Training and Placements(Dean)2021 - Present
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Raipur Institute of Technology Asst. Prof.2006 - 2021
Education
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National Institute of Technology Ph.D.2017 - 2024
Projects & Funding
Projects & funding information is unavailable.
Publications (62)
- An Image Segmentation-Based Machine Learning Approaches for Detecting and Classifying Skin Disease Save
- Advancements in Image Segmentation: An Analysis of Conventional and ANN Based Methods Save
- Preface Save
- Preface Save
- Optimizing Image Preprocessing for Oral Cancer Detection: A Comparative Study of Contrast and Edge Enhancement in Machine Learning Save
- Improved Alzheimer's Disease Categorization Using SMOTE and CNN Integrated Optimizer Evaluation Save
- Hypertension Risk Prediction Using Support Vector Machines (SVM) in Electronic Health Record Data Save
- Epileptic Seizure Detection in EEG Signals Using Machine Learning and Discrete Wavelet Transform Save
- Early Diagnosis of Alzheimer's Disease Using Deep Convolutional Neural Networks (CNNs) in MRI Analysis Save
- Cloud-Enhanced AI for Early Prediction of Chronic Kidney Disease: A Deep Learning and Machine Learning Approach Save