Rate My Professor A. Sri Krishna

Dr. A. Sri Krishna serves as Professor and Head of the Department of Artificial Intelligence at Shri Vishnu Engineering College for Women in Bhimavaram, Andhra Pradesh, a position he has held since joining the institution on September 15, 2014. His academic background includes a Ph.D. in Data Privacy from the Department of Computer Science and Systems Engineering at Andhra University, awarded in 2015. He earned an M.Tech. in Information Technology from Andhra University in 2009 and a B.Tech. in Information Technology from Jawaharlal Nehru Technological University Hyderabad in 2007. Additionally, he completed a PG Diploma in Data Science from IIIT Bangalore during 2017-2018. With nine years of teaching experience documented on his profile, Dr. Sri Krishna has supervised two Ph.D. scholars to completion and is currently guiding two more.

Dr. Sri Krishna's research specializations encompass Data Privacy, Machine Learning, Deep Learning, and Natural Language Processing. He maintains professional memberships in ACM and the Institution of Engineers (IE). His scholarly contributions include numerous publications on privacy-preserving techniques and machine learning applications. Key works are: “A Study of Privacy Attacks on Social Network Data” in International Journal of Global Research in Computer Science (2014); “PBLR: Priority Based Local Recoding Anonymization” in International Journal of Computer Technology and Applications (2014); “Checking Anonymity Levels for Anonymized data” in Springer Lecture Notes in Computer Science (2011); “Attribute Based Anonymity for Preserving Privacy” in Springer Communications in Computer and Information Science (2011); “An Efficient and Dynamic Concept Hierarchy Generation for Data Anonymization” in Springer Lecture Notes in Computer Science (2013); “Handling emotional speech: a prosody based data augmentation technique for improving neutral speech trained ASR systems” in International Journal of Speech Technology (2022); “Transfer Learning-based Optimal Feature Selection with DLCNN for Shrimp Recognition and Classification” in International Journal of Intelligent Engineering & Systems (2022); “SDNet: Integrated Unsupervised Learning with DLCNN for Shrimp Disease Detection and Classification” at IEEE International Conference on Data Science and Information System (2022); “Detection of Retinal Degeneration via High-Resolution Fundus Images using Deep Neural Networks” at International Conference on Electronics and Renewable Systems (2023); and “Enhanced Deep Convolutional Neural Network for Identifying and Classification of Silicon Wafer Faults in IC Fabrication Industries” at International Conference on Wireless Communications Signal Processing and Networking (2023).