Developed an advanced ensemble learning system combining Transformers and Convolutional Neural Networks for masked face recognition, achieving 94.22% accuracy through innovative stacking techniques. The project enhanced existing methodologies by implementing optimized weight distribution and stacked generalization, demonstrating significant improvements over state-of-the-art approaches. Notable achievements included a 2.2% accuracy gain over traditional ensemble methods and robust performance across multiple datasets.

Developed a sophisticated multi-class sentiment analysis system using BERT, achieving 80.69% accuracy on aspect-based opinion classification. The project successfully addressed class imbalance challenges through weighted loss functions and optimised model architecture, demonstrating robust performance in categorising opinions as positive, negative, or neutral.

Developed a semantic image segmentation system for post-hurricane disaster assessment using UAV imagery, achieving 62.65% accuracy with SeResNet architecture. The project successfully identified and localized 27 different asset classes in residential areas, providing crucial support for disaster relief efforts in the Houston region after Hurricane Harvey.

Developed an innovative web-based diabetes detection system using CNN and transfer learning, achieving high accuracy on the Pima Indians dataset. The project successfully implemented novel techniques for transforming tabular medical data into image representations, demonstrating effective application of deep learning in healthcare diagnostics.