Real-Time Wildlife Behavior Alerts with Hybrid DL Approaches

The increasing frequency of animal attacks poses a significant threat to rural populations and forest workers, emphasizing the need for advanced monitoring systems to ensure safety. Traditional machine-learning approaches used for tracking and identifying wild animals are often costly and complex. To challenges to be addressed, we propose a hybrid model that integrates VGG-19 for effective image recognition with a Bidirectional Gated Recurrent Unit (GRU) layer for superior temporal and spatial feature optimization. The Bidirectional GRU enhances accuracy in processing image features, making it highly suitable for this application. Experiments show that the suggested model is able to classify animal photos, follow their movements, and produce real-time alert signals with accurate position data with an accuracy of over 97%. This approach offers a cost-effective, accurate, and reliable solution to mitigate risks associated with animal attacks, ensuring enhanced safety for forest workers and rural communities