Dr Pallavi M O
Associate Professor
Academic Area: BE, MTech, PhD
Areas of Interest: CSE - AI & DS
Profile
Dr. Pallavi M.O. is an accomplished academician with 14 years of teaching experience in Computer Science and Engineering. She is currently working as an Associate Professor at Sapthagiri NPS University and has previously served at reputed institutions including REVA University and Acharya Institute of Technology. Her areas of expertise include Computer Networks, DBMS, Cyber Security, Operating Systems, and Artificial Intelligence. She has published numerous research papers, filed patents, and actively contributes to conferences, research activities, and technical events. She is dedicated to mentoring students, fostering innovation, and promoting academic excellence through quality teaching and research.
Published papers
The research paper titled “BreastNet: An Optimized Convolutional Neural Network for Mammogram Analysis with Multi-Magnification Feature Recognition and Enhanced Interpretability” has been successfully published in a Q2 indexed journal. The work presents an advanced deep learning framework for accurate breast cancer detection through mammogram image analysis. The proposed model utilizes multi-magnification feature extraction and enhanced interpretability techniques to improve classification accuracy, reliability, and clinical decision support in medical imaging applications.
FDPs / MDPs / Workshops
Awards
Training & Consultation
Patents/Projects
Artificial Intelligence Based Quantitative Immunoassay Analyzer is an advanced diagnostic system that combines Artificial Intelligence with immunoassay technology to provide accurate, fast, and automated medical test analysis. The system is designed to quantitatively detect and measure biomarkers, antibodies, antigens, hormones, and proteins present in biological samples such as blood or serum. By utilizing AI and machine learning algorithms, the analyzer can automate data processing, improve diagnostic accuracy, reduce human errors, and generate real-time results for effective clinical decision-making. This intelligent system supports early disease detection, health monitoring, and predictive analysis, making it highly useful in hospitals, diagnostic laboratories, healthcare centers, and biomedical research applications.