Computational Intelligence Lab
Computational Intelligence Lab
Overview
The Computational Intelligence Lab is a dedicated teaching and research facility that supports Machine Learning and Machine Learning for Data Science courses under the School of Computer Science and Engineering (SCOPE), VIT Vellore Campus. The lab focuses on the development and application of intelligent computational models, including machine learning algorithms, deep learning architectures, evolutionary optimization methods, fuzzy systems, and hybrid AI techniques for solving complex, data-driven problems
Objectives
- To provide practical exposure to core machine learning and data science algorithms through structured laboratory experiments.
- To strengthen students’ understanding of data-driven modelling, optimization, and intelligent decision-making techniques.
- To support course-integrated projects, mini-projects, and capstone work in machine learning and computational intelligence
- To enable research-oriented learning in emerging areas of artificial intelligence and data analytics.
- To promote the use of industry-standard frameworks and tools for scalable and reproducible machine learning solutions
- To foster interdisciplinary applications of machine learning across domains such as healthcare, finance, smart systems, and automation.
Key Focus Areas
- Machine Learning Algorithms : Supervised, unsupervised, and ensemble learning techniques
- Deep Learning : Neural Networks, CNNs, RNNs, transformers, and representation learning.
- Machine Learning for Data Science : Data preprocessing, feature engineering, model evaluation, and interpretability.
- Computational Intelligence Techniques : Evolutionary algorithms, swarm intelligence, fuzzy logic, and hybrid models.
- Optimization and Intelligent Decision-Making : Metaheuristics and learning-based optimization strategies.
- Applied AI and Analytics : Real-world datasets, case studies, and domain-specific applications.
- Applied AI and Analytics : Real-world datasets, case studies, and domain-specific applications.
On-going Project Titles :
- Stacked Ensemble Learning for Multimodal Speech Sentiment Analysis for Mental Health Monitoring
- Fine-Tuning a Medical LLM using C / CUDA.
Research Publications
- Ghosh, Ananya, and Jyotismita Chaki. “Fuzzy enhanced kidney tumor detection: Integrating Machine Learning Operations for a fusion of twin transferable network and weighted ensemble machine learning classifier.” IEEE Access (2025).
- Shekatkar, Atharva Rajesh, Aditya Deodeshmukh, and Ilanthenral Kandasamy. “Refined Neutrosophic Cognitive Maps: A Novel Framework for Modeling Indeterminate Causal Relationships.” International Journal of Fuzzy Systems (2025): 1-24.
- Guruprakash, J., P. Pradeep, and L. B. Krithika. “GenePixKolor (GPK) Fusion: A novel evolutionary algorithm-based optimized NFT card generation and rarity ranking method for gaming tokenomics.” IEEE Access (2025).
- Jayachandran, J., and K. Vimaladevi. “A Fuzzy Hybrid Zone Head Selection and Arbitrary Cluster Based Routing Algorithm for Border Surveillance in WSN.” Wireless Personal Communications (2025): 1-28.
- Dhanabal, S., Chandrashekhar Goswami, Rani Venkata Satya Praveen, and T. Vetriselvi. “Shuffle-F-ZFNet: ShuffleNet Fuzzy Zeiler and Fergus network for data aggregation in WSN data communication.” Wireless Networks (2025): 1-24.
