Deep Learning-based Approaches for Preventing and Predicting Wild Animals Disappearance: A Review

Abstract

This paper examines the effectiveness of deep learning in preventing and predicting wild animal disappearances. We analyze existing research on applications in surveillance systems, behavioral analysis, tracking mechanisms, and animal welfare. Deep learning algorithms demonstrate promising results, achieving high accuracy in predicting disappearances and detecting abnormal behaviors. Challenges regarding data availability and model generalization remain, but future research in integrating diverse data sources, developing generalized models, and advancing sensor technologies can overcome these obstacles. Responsible implementation of deep learning has the potential to revolutionize wild animal conservation, ensuring the safety and well-being of these animals.