Lab Vision and Mission

• Vision: The lab aims to explore cutting-edge methodologies in database systems and data mining techniques to solve real-world problems in domains like healthcare, smart systems, e-commerce, and more.

• Mission: To advance research and foster innovation in database management systems, big data analytics, and machine learning algorithms for efficient data mining, predictive modeling, and decision-making.

Research Focus Areas

Your lab could focus on several key areas within the scope of database systems and data mining:

• Database Modeling: EER, relational, object-oriented, and NoSQL databases.

• Data Mining Algorithms: Association rule mining, clustering, classification, anomaly detection, regression, and predictive modeling.

• Big Data Technologies: Leveraging Hadoop, Spark, and other tools for processing and analyzing large datasets.

• Data Privacy and Security: Applying encryption, access control, and secure computing in data management and mining.

• Smart Systems: Data mining techniques in IoT, smart grids, and healthcare.

• Real-Time Data Mining: Developing systems for real-time data collection and processing.

• Data Visualization: Advanced visualization techniques for large-scale data insights.

• Predictive Modeling: Using statistical algorithms and machine learning techniques to analyze historical data and make predictions about future outcomes. 

Lab Infrastructure

• Hardware:

  • High-performance computing (HPC) clusters for data processing and model training.

  • Servers for hosting databases and big data frameworks.

• Software:

  • Database management systems (DBMS): MySQL, PostgreSQL, MongoDB, Oracle, etc.

  • Data mining and machine learning tools: Python (Pandas, Scikit-learn, TensorFlow), R, Weka, RapidMiner, KNIME.

  • Big Data platforms: Hadoop, Apache Spark.

  • Data visualization tools: Tableau, Power BI, D3.js.

• Cloud Resources: AWS, Google Cloud, or Azure for scalable storage and computation.

• Security Tools: Tools for encrypting data, implementing access control, and ensuring compliance with data protection regulations.

Key Research Projects

• Project 1: Predictive Healthcare Analytics: Leveraging data mining to build predictive models for patient diagnosis, treatment recommendation, and outcome forecasting using large-scale health records.

• Project 2: Database Optimization in Smart Systems: Developing algorithms to improve database performance for time-sensitive smart applications, such as traffic management or energy grids.

• Project 3: Blockchain-Enabled Data Mining for Security: Researching how blockchain can be integrated with data mining techniques for securing data and ensuring privacy in sensitive domains like healthcare or financial transactions.

• Project 4: Big Data Integration in Smart Cities: Utilizing data mining techniques on real-time data from sensors and devices in smart cities to provide insights into traffic, energy, and security.

Future Plans

• Expand the lab's research areas to include emerging fields like quantum computing applications in databases or artificial intelligence-driven database management systems.

• Set up a database and data mining-specific startup incubator within the lab to foster innovation and commercialization of research outputs.

1. KalaiSelvi, B., Anandan, P., Sathishkumar V E, & Cho, J. (2025). IoT-driven cancer prediction: Leveraging AI for early detection of protein structure variations. Alexandria Engineering Journal, 118, 21-35. 

2. Kande, G. B., Nalluri, M. R., Manikandan, R., Cho, J., & Sathishkumar V E. (2025). Multi scale multi attention network for blood vessel segmentation in fundus images. Scientific Reports, 15(1), 3438. 

3. Sathishkumar V E., Xuan, K. O. Y., Putra, L., Ern, N. C., & Sheng, T. J. (2025). Comparative Study on Oracle, Neo4J, Cassandra, Redis, and MongoDB. Information Research Communications, 1(2), 104-119. 

4. Sangeetha, S. K. B., Mathivanan, S. K., Muthukumaran, V., Cho, J., & Sathishkumar V E (2024). An Empirical Analysis of Transformer-Based and Convolutional Neural Network Approaches for Early Detection and Diagnosis of Cancer Using Multimodal Imaging and Genomic Data. IEEE Access. 

5. Mathivanan, S. K., Rajadurai, H., Cho, J., & Sathishkumar V E (2024). A multi-modal geospatial–temporal LSTM based deep learning framework for predictive modeling of urban mobility patterns. Scientific Reports, 14(1), 31579. 

6. Geeitha, S., Prabha, K. R., Cho, J., & Sathishkumar V E (2024). Bidirectional recurrent neural network approach for predicting cervical cancer recurrence and survival. Scientific Reports, 14(1), 31641. 

7. Thirupattur, J. I., Tan, A. X. T., Lim, S. L., Wong, L. H. E., Yong, M. J., & Sathishkumar V E (2024, December). Comparative Performance Analysis of SQL and NoSQL Databases for Optimizing Retail and E-Commerce Operations. In 2024 9th International Conference on Communication and Electronics Systems (ICCES) (pp. 1063-1070). IEEE. 

8. Sathishkumar V E (2024, December). Comparative Analysis of Database Models for Data Management and Security: Evaluating MySQL, Azure Cosmos DB, ScyllaDB, Couchbase, and Firestore in a Student Record System. In 2024 9th International Conference on Communication and Electronics Systems (ICCES) (pp. 779-785). IEEE. 

9. Sangeetha, S. B., Selvarathi, C., Mathivanan, S. K., Cho, J., & Sathishkumar V E (2024). Secure Healthcare Access Control System (SHACS) for Anomaly Detection and Enhanced Security in Cloud-Based Healthcare Applications. IEEE Access. 

10. Karthik, R., Inamdar, R., Sundarr, S. K., Cho, J., & Sathishkumar V E (2024). Point Cloud-based 3D Object Classification with Non local attention and Lightweight Convolution Neural Networks. IEEE Access. 

11. Karthik, R., Menaka, R., Atre, S., Cho, J., & Sathishkumar V E (2024). A Hybrid Deep Learning Approach for Skin Cancer Classification using Swin Transformer and Dense Group Shuffle Non-Local Attention Network. IEEE Access. 

12. Sathishkumar V E (2024). Utilizing big data and deep learning to improve healthcare intelligence and biomedical service delivery. Frontiers in Big Data, 7, 1502398. 

13. First, B. M., Shanmugavadive, K., Sathishkumar V E, Maruthappa, M., Subramanian, M., & Thinakaran, R. (2024). Attention Mechanism-Based CNN-LSTM for Abusive Comments Detection and Classification in Social Media Text. International Journal of Advanced Computer Science & Applications, 15(10). 

14. Karthik, R., Ajay, A., Bisht, A. S., Cho, J., & Sathishkumar V E (2024). An Explainable Deep Learning Network for Environmental Microorganism Classification using Attention-Enhanced Semi-Local features. IEEE Access. 

15. Geeitha, S., Ravishankar, K., Cho, J., & Sathishkumar V E (2024). Integrating cat boost algorithm with triangulating feature importance to predict survival outcome in recurrent cervical cancer. Scientific Reports, 14(1), 19828. 

16. Sangeetha, S. K. B., Immanuel, R. R., Mathivanan, S. K., Cho, J., & Sathishkumar V E (2024). An Empirical Analysis of Multimodal Affective Computing Approaches for Advancing Emotional Intelligence in Artificial Intelligence for Healthcare. IEEE Access. 

17. Jamunadevi, C., Prasath, S., Sathishkumar V E, Pandikumar, S., & Akshaya, J. (2024, July). Enhancing Loan Approval Prediction Through Advanced Machine Learning Models. In 2024 2nd International Conference on Sustainable Computing and Smart Systems (ICSCSS) (pp. 957-964). IEEE. 

18. Jamunadevi, C., Sathishkumar V E., Prasath, S., Sadhanayaki, S., & Jaikishore, N. (2024, July). Optimized Fabric Imperfection Detection via Image Analysis and Isolation Forest Technique. In 2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT) (pp. 1-6). IEEE. 

19. Nithya, K., Krishnamoorthi, M., Sathishkumar V E, Dhivyaa, C. R., Yoo, S., & Cho, J. (2024). Hybrid approach of deep feature extraction using BERT–OPCNN & FIAC with customized Bi-LSTM for rumor text classification. Alexandria Engineering Journal, 90, 65-75. 

20. Anilkumar, C., Lenka, S., Neelima, N., & Sathishkumar V E (2024). A Secure Method of Communication Through BB84 Protocol in Quantum Key Distribution. Scalable Computing: Practice and Experience, 25(1), 21-33. 

Currently I am looking for MS/PhD students. Interested students can send their resume to sathishv@sunway.edu.my