Dr. DHIVYA S
@ssmartscollege.in
ASSISTANT PROFESSOR - COMPUTER SCIENCE
SSM COLLEGE OF ARTS AND SCIENCE
Dr. S. Dhivya is a distinguished academic and researcher in the field of Computer Applications, with a Ph.D. from Anna University, Chennai, specializing in meta-heuristic and artificial intelligence–based models for breast cancer image enhancement. With over eight years of teaching and research experience, she currently serves as an Assistant Professor at SSM College of Arts & Science, Dindigul.
She has an impressive portfolio of SCI-indexed journal publications and has been honored with prestigious recognitions including the LEAP 2025 International Education Award – Faculty of the Year for her innovative contributions to education. Dr. Dhivya is a recognized Research Supervisor at Kalasalingam University, where she actively guides multiple Ph.D. scholars.
Her expertise spans Artificial Intelligence, Medical Image Processing, Evolutionary Computing, and Advanced Programming, with strong proficiency in Python, Java, and C++. In addition to her academic and research achievements, she p
EDUCATION
Ph.D. in Computer Applications — Anna University, Chennai
PSNA College of Engineering & Technology (Full-Time)
Completed with Commendation | June 2024
Master of Computer Applications (MCA) — Bharathiar University, Coimbatore
Pioneer College of Arts & Science
CGPA: 8.430 | First Class with Distinction | 2016
Bachelor of Science (B.Sc.) in Computer Science — Bharathiar University, Coimbatore
Pioneer College of Arts & Science
84.5% | First Class with Distinction | 2009
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Science, Artificial Intelligence, Computer Science Applications, Software
FUTURE PROJECTS
AI-Based Early Breast Cancer Detection and Risk Prediction System
This project aims to develop an AI-driven system for early breast cancer detection using advanced medical image processing and deep learning models. Meta-heuristic optimization techniques will be integrated to enhance mammogram quality and improve lesion segmentation accuracy. The system will analyze imaging and patient data to predict cancer risk levels with high reliability. A decision-support interface will assist clinicians in identifying high-risk cases at an early stage. The proposed framework is expected to significantly improve diagnostic accuracy and reduce delayed cancer detection.
Applications Invited
Intelligent and Secure Healthcare IoT Monitoring Platform
This project proposes an intelligent and secure IoT-based platform for real-time monitoring of patient health parameters. AI algorithms will be used to analyze sensor data and detect abnormalities at an early stage. Advanced security mechanisms will ensure privacy, data integrity, and protection against cyber threats. The system will support remote healthcare services and timely clinical decision-making. The proposed platform aims to enhance patient safety, reliability, and efficiency in smart healthcare environments.
Applications Invited
Scopus Publications
Scopus Publications
Dhivya Samraj and Muralidharan Karuppusamy
Springer Science and Business Media LLC
Dhivya Samraj, Kuppuchamy Ramasamy, and Balasubramanian Krishnasamy
Springer Science and Business Media LLC
Dhivya Samraj, Kuppuchamy Ramasamy, and Muralidharan Karuppusamy
Zarqa University
In this research, Particle Swarm Optimization (PSO) based image equalization is projected to enhance the contrast of different breast cancer images. Breast cancer is the highest and another important root of tumor disease in females worldwide. Mass and microcalcification clusters are a significant early signs of breast cancer. The mortality rate can effectively be decreased by early diagnosis and treatment. Most practical approach for the early detection and identification of breast cancer diseases is mammography. Mammographic images contaminated by noise usually involve image enhancement techniques to aid interpretation. Contrast enhancement is divided into two categories: development of direct contrast and enhancement of indirect contrast. Indirect contrast improvement is used in the image histogram update. Histogram Equalization (HE) is the modest enhancement of the indirect contrast approach usually used for contrast enhancement. The proposed method's average entropy is 5.3251 with the highest structural similarity index 0.99725. The best contrast improvement of this method is 1.0404 and PSNR is 46.3803. The MSE value is 2157.08. This paper recommends an innovative method of enhancing digital mammogram image contrast based on different histogram equalization approaches. The performance of the projected method has been related to other prevailing techniques using the parameters, namely, discrete entropy, contrast improvement index, structural similarity index measure, mean square error, and peak signal-to-noise ratio. Investigational findings indicate that the projected strategy is efficient and robust and shows better results than others.
RECENT SCHOLAR PUBLICATIONS
Publications
High-Impact SCI Journal Publications
Dhivya S., Kuppuchamy R., & Balasubramanian K. (2023)
Enhancement and Diagnosis of Breast Cancer Mammography Images Using Histogram Equalization and Genetic Algorithm
Multidimensional Systems and Signal Processing (Springer) | Impact Factor: 2.030 | SCI Indexed
DOI: 10.1007/s11045-023-00880-0
Dhivya S., Kuppuchamy R., & Muralidharan K. (2023)
Evolutionary Computing Model for Detecting Breast Cancer Masses Using Image Enhancement and AI Algorithms
International Arab Journal of Information Technology, Vol. 20(4) | Impact Factor: 1.2 | SCI Indexed
DOI: 10.34028/iajit/20/4/10
Dhivya S. & Muralidharan K. (2025)
A Metaheuristic-Based Histogram Equalization Method for Mammogram Enhancement Using Brightness-Preserving Cuckoo Search Algorithm
Journal of Optics | Impact Factor: 2.5 | SCI Indexed
DOI: 10.1007/s12596-025-02834-0
RESEARCH OUTPUTS (PATENTS, SOFTWARE, PUBLICATIONS, PRODUCTS)
🧾 Patent Details
Title: Graphical User Interface for an AI-Powered IoT Security Dashboard
Design Application No.: 453265-001
CBR System No.: 206641
Date of Filing: 27 March 2025
Status: Under Technical Examination
Brief Description:
The patent covers the design of an intelligent graphical user interface for an AI-enabled IoT security dashboard, aimed at real-time monitoring, threat detection, and visualization of cybersecurity risks in connected systems.
Industry, Institute, or Organisation Collaboration
Kalasalingam Academy of Research and Education – Ph.D. scholar supervision and collaborative research guidance