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S Kanchana

S Kanchana

Associate Professor

About

Dr. S. Kanchana received the Ph.D. degree in Computer Science from Anna University, Chennai, India, in 2016. She is currently an Associate Professor with the School of Computer Science and Engineering, RV University, Bengaluru, India. She has over 22 years of teaching and research experience. Her research interests include Artificial intelligence, Machine learning, Deep learning, Educational data mining, cryptography, Biometrics, and the Internet of Things. She has published several articles in Scopus- and Web of Science-indexed journals and serves as a reviewer for international journals. She is a recognized Ph.D. supervisor and has received research funding from ICSSR and TNSCST for academic and research initiatives.

Education is the manifestation of the perfection already in man

Enhanced Student Performance Prediction Using Augmentation with Ensemble Generative Adversarial Network (2024)
This study introduces an Ensemble Generative Adversarial Network (E-GAN) framework to address class imbalance in educational datasets and improve student performance prediction. By combining data augmentation and machine learning techniques, the proposed model enhances prediction accuracy and supports early identification of academically at-risk students, enabling timely interventions and improved learning outcomes. Indian Journal of Science and Technology, Vol. 17, Issue 44, 2024.
A Multi-View Deep Learning Approach for Enhanced Student Academic Performance Prediction (2024)
This research presents a multi-view deep learning model that integrates academic, behavioral, and contextual data to predict student performance. The framework captures complex relationships among multiple factors influencing learning outcomes and demonstrates superior predictive performance compared to traditional approaches, supporting personalized learning and evidence-based educational decision-making. Communication on Applied Nonlinear Analysis, Vol. 31, Issue 6, 2024.
Predicting Student Performance Using Mental Health and Linguistic Attributes with Deep Learning (2023)
This work explores the influence of mental health indicators and linguistic attributes on academic performance using deep learning techniques. By analyzing psychological and language-based features, the study provides insights into student well-being and learning behavior, contributing to the development of proactive support systems for improving student success and retention. Revue d’Intelligence Artificielle, Vol. 37, Issue 4, 2023.
Techniques for Examining Students’ Data for Indicators of Future Success (2023)
This research examines educational data mining techniques for identifying patterns that indicate future student success. The study evaluates key academic and behavioral factors that influence performance and highlights the role of predictive analytics in supporting student progression, retention, and institutional planning. The findings demonstrate how data-driven approaches can enhance educational effectiveness and learner outcomes. International Journal of Innovative Science and Research Technology, Vol. 8, Issue 4, 2023.

Published research in reputed Scopus- and Web of Science-indexed journals.

Focused on developing intelligent, data-driven solutions for complex real-world challenges.

Contributed to advancements in predictive analytics, intelligent computing, and emerging technologies.

Secured funded research projects and authored scholarly publications and book chapters.

Actively engaged in interdisciplinary research, innovation, and academic collaborations.

Research Interests

Machine Learning

Deep Learning

Educational Data Mining

Predictive Analytics

Computer Vision

Cyber security

Received the Best Paper Award for Innovative Teaching Methodologies at the PSG Institutions Annual Conclave (2019).

Honored with the Research Contribution Award for outstanding scholarly achievements during the academic years 2019–2025.

Published 15 research papers in reputed Scopus-indexed, Web of Science-indexed, and peer-reviewed journals.

Authored Springer book chapters in emerging areas of Computer Science.

Successfully guided one Ph.D. scholar to completion.

Secured research funding from ICSSR and the Tamil Nadu State Council for Science and Technology (TNSCST).

S Kanchana

Associate Professor

MCA, M.Phil, Ph.D

School of Computer Science and Engineering

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