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@article{197437,
        author = {Nandha Kumar A and Nirmal S and Ruthra Prasath M and Srikaran M and Dr. K. Sasikala and Mrs. G. Kanmani and Mrs. A. Santhi},
        title = {AI-Enabled Real-Time Alert System for Detecting Wild Animal Intrusions at Forest and Village Borders},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {9593-9601},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=197437},
        abstract = {AI-Enabled Real-Time Alert System for Detecting Wild Animal Intrusions at Forest and Village Borders an AI-enabled system to detect and notify users of wild animal intrusions in agricultural fields and village areas adjacent to forests, aiming to mitigate human–wildlife conflicts. The proposed solution enhances existing YOLO-based and edge computing frameworks by incorporating GPS-based location tracking and dual alert mechanisms. The system employs ESP32-CAM modules and ultrasonic sensors to capture motion-triggered images along established animal pathways. These images are processed locally using a YOLO object detection model to identify animals such as elephants, leopards, and wild boars with minimal latency. Upon detection of an animal, alerts specifying the animal type and location are dispatched via offline SMS over GSM/4G networks and through an online interface, delivering real-time visual and auditory alerts to civilians and forest rangers. Detection data is stored in the cloud for movement analysis and model enhancement. The system is cost-effective, scalable, and well-suited for deployment in rural areas.},
        keywords = {Wild Animal Intrusion, Human-Wildlife Conflict, YOLO, Edge AI, TinyML, IoT Sensors, GPS Localization, SMS Alerts.},
        month = {April},
        }