FusedMAC Framework Analysis for Enhanced Animal Condition Monitoring

The integration of advanced technologies especially with machine learning has the potential to revolutionize animal condition monitoring by improving animal welfare and health status through comprehensive condition monitoring across the agricultural and veterinary domains as well as wildlife conservation. This study focuses on Multiple Additive Regression Trees (MART) and CatBoost as well as the fusion techniques. The proposed FusedMAC framework analyzes complex and high-dimensional data. Stacking fusion techniques aim to provide a comprehensive understanding of animal conditions by integrating diverse data modalities. Also, the use of the explainable artificial intelligence (XAI) methods, called SHAP (Shapley Additive Explanations). This technique ensures the transparency and interpretability of the model’s predictions. Our proposed approach addresses key gaps in previous research, including the need for advanced machine learning integration, robust data fusion and real-world validation. This study contributes to more effective animal management practices by enhancing predictive accuracy and interpretability. Ultimately, improving animal welfare and health outcomes.