Research Interests
Formal research interests for the laboratory, organized by core themes and application domains.
- Artificial Intelligence / Machine Learning / Big Data Analytics
- Generative AI (GANs for video anomaly detection)
- Soft Computing Techniques
- Data Mining
More Specifically
- Agricultural plant leaves disease detection and classification using deep learning.
- Ensemble learning of classifiers.
- Improvement in clustering algorithms.
- Multimodal optimization using differential evolution and its applications.
- Design of semi-supervised neuro-fuzzy systems.
- Optimization of soft computing frameworks.
- Cybersecurity using machine learning algorithms.
- Binary neural network learning with quantum processing.
Specialization Field
- Soft computing with innovations in Big Data handling, including neural network learning algorithms, genetic programming, support vector machines, fuzzy sets, and rough sets.
- Deep learning algorithms and architectures, especially generative adversarial networks (GANs).
- AI and ML algorithms for classification, clustering, prediction, and data mining.