Assistant Professor | AVN Institute of Engineering and Technology | India
Short Biography
Dr. Kalpana Ponugoti is an accomplished academic with a strong focus on Computer Science and Engineering, specializing in cutting-edge technologies like Artificial Intelligence and Machine Learning. Currently serving as an Assistant Professor at AVN Institute of Engineering and Technology, she brings over 8 years of teaching experience and expertise in curriculum development, along with significant contributions as an industry professional and researcher in Salesforce development.
Profile
ORCID
Education
Dr. Kalpana completed her Ph.D. in Computer Science and Engineering from VELS University (VISTAS), Chennai, anticipated in May 2024. Prior to this, she earned her M.Tech in Computer Science from BKBG Institute of Technology and her B.Tech in Information Technology from Jawaharlal Nehru Institute of Technology, both affiliated with JNTU-Hyderabad.
Experience
Her academic journey includes roles at prestigious institutions such as TKR Engineering College, Vignan Institute of Technology and Science, and Sreyas Institute of Engineering & Technology. She currently holds the position of Assistant Professor at AVN Institute of Engineering and Technology, where she actively contributes to research and academic advancements in the field of Computer Science.
Research Interests
Dr. Kalpana’s research interests are centered around Artificial Intelligence, particularly in the application of machine learning techniques to solve real-world problems. Her recent work focuses on developing innovative solutions for plant disease recognition and classification using advanced deep learning models.
Awards
She has been recognized for her scholarly contributions with several publications in reputed journals and conferences, including SCI-indexed and Scopus-indexed papers. Her dedication to academic excellence and research innovation has earned her acclaim in the scientific community.
Publications
Dr. Kalpana has authored numerous impactful publications, including:
- “Plant disease recognition using residual convolutional enlightened Swin transformer networks”, published in Scientific Reports, 2024.
- “A capsule attention network for plant disease classification”, published in Traitement du Signal, 2023.
- “FLY-CAPS- A Hybrid Firefly Feature Optimized Capsule Networks for Plant Disease Classification in Resource Constraint Internet of Things (IoT)”, published in International Journal on Recent and Innovation Trends in Computing and Communication, 2023.
- Several other contributions in conference proceedings and UGC Care-listed journals, contributing significantly to the fields of IoT, deep learning, and computer vision.