<![CDATA[Monitoring elephant movement is crucial for wildlife conservation, especially under threats such as poaching and habitat loss. With the availability of large-scale GPS tracking data, anomaly detection can help identify abnormal behaviors linked to critical events. However, challenges such as data imbalance, GPS noise, and real-time deployment constraints remain. This paper proposes an end-to-end framework for anomaly detection in elephant movement using GPS data. The approach combines multi-view anomaly modeling with a weighted scoring mechanism and a lightweight Random Forest model. To address class imbalance, the pipeline integrates SMOTE (Synthetic Minority Over-sampling Technique), under sampling, and class-weighted learning. Feature selection and quantization further optimize the system for edge and FPGA deployment. Experimental results show strong performance, with F1-Macro ≈ 0.98, ROC-AUC ≈ 0.99, and high recall for anomaly detection. The proposed framework provides an efficient and practical solution for real-time wildlife monitoring.]]>
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