ABOUT PREM KUMAR
This is PREMKUMAR BORUGADDA, who received M.Tech (CSE) Degree from Pondicherry University, Pondicherry, India. I submitted my Ph.D. thesis in the month of January 2023 (Full-Time) in the stream of Artificial Intelligence & Machine Learning in the Department of Computer Science at Pondicherry University, Karaikal Campus. Qualified "AP.SET (2012), TN.SET (2012), and UGC.NET (2017)" in Computer Science and Applications for Assistant Professor/Lectureship. I have published 20 research articles in refereed national and international journals. My research area is Machine Learning, Deep Learning, and Computer vision. Research and teaching is my passion.
1. Reviewer for the IEEE International Conference on Computing, Communication, and Intelligent Systems (ICCCIS-2023) 03rd-04th Nov, 2023 Sharda University, Greater Noida, India.IEEE Conference Record #60361 ISBN No.: 979-8-3503-0611-8
2. Reviewer for the 2nd International Conference on Advancements in Smart Computing and Information Security (ASCIS 2023) 08th -09th December 2023 Marwadi University, Rajkot, India.
Education
- ➢ Ph.D. (full-time) in CSE (AI & ML) from Pondicherry University (A Central University), September 2017 to January 2023 (thesis submitted). Thesis Title: “A Novel Approach for Multi-Level Dimensional Reduction for Classification of Tomato Plant Leaf Diseases using Transfer Learning on VGG16 Model” under the guidance of Dr. R. Lakshmi, Associate Professor, Department of Computer Science, Pondicherry University, Puducherry
- ➢ M.Tech (CSE) from Pondicherry University, Puducherry, With 7.88 CGPA, 2011-2013
- ➢ Master of Computer Applications with 72% from A.K.R.G. College, Andhra University, 2006–2009
- ➢ Bachelor of Science in MPCs with 69% from Sri A.B.R. Degree College, Acharya Nagarjuna University, 2003–2006
Research Interest
- Machine learning
- Deep learning
- Computer Vision
Technical Skills
- Machine Learning Algorithms: Linear and logistic Regression, SVM, Decision Tree, Random Forest, KNN, ANN
- Deep Learning Algorithms: CNN models (LeNet, AlexNet, VGG16, VGG19, ResNet50)
- Modelling: Scikit-Learn, Keras, TensorFlow
- Visualization: Matplotlib, Seaborn
- Processing: NumPy, Pandas
Eligibility Test Qualified
- UGC.NET Qualified for Assistant Professor/Lectureship in November-2017
- AP.SET Qualified for Assistant Professor/Lectureship in July-2012
- TN.SET Qualified for Assistant Professor/Lectureship in October-2012