Applied AI for Vision, Agriculture & Smart Monitoring

Combines Graph Neural Networks with Gated Recurrent Units for collective sports activity modeling. The GraphSAGE architecture samples and aggregates neighborhood features while GRU layers model sequential dependencies, enabling accurate recognition of team formations, player roles, and tactical movements in team sports.

Deep learning-based plant identification using leaf images for precision agriculture, weed management, and biodiversity monitoring. The framework employs convolutional neural networks with transfer learning to classify plant species from leaf morphology, vein patterns, and textural features.

Aerial surveillance system for real-time vehicle detection and tracking from UAV video using pixel-level semantic segmentation and particle filter tracking. The system segments vehicles from aerial views and maintains robust tracking through occlusions and camera motion, enabling traffic monitoring, disaster assessment, and military reconnaissance.

A wearable IoT framework integrating multimodal biosensors with intelligent signal processing for fall detection and remote health monitoring of elderly patients. The system fuses accelerometer, gyroscope, and heart rate data through deep learning models to distinguish between falls and daily activities.