Optimized Physics-Informed Neural Network Framework for Wild Animal Activity Detection and Classification with Real Time Alert Message Generation

The growing contact between wild animals and humans has forced the creation of intelligent systems capable of monitoring, detecting, and classifying animal behaviors. This research describes a unique technique for wild animal activity detection and categorization that employs optimized Physics-Informed Neural Networks (PINNs) designed to provide real-time alarm signals. By incorporating domain-specific physical models into neural network training, the proposed method outperforms standard strategies in terms of accuracy and resilience. This article describes the model's design, optimization, and implementation, as well as its use in detecting animal activity in a variety of environments. The findings em phasize the model's ability to accurately classify and generate timely alerts, em phasizing its practical value for wildlife monitoring and protection. The findings offer a transformative perspective on deploying physics-informed deep learning for ecological applications.