Creating Alert Messages Based On Wild Animals Activity Detection

Abstract

Monitoring wild animal activity plays a vital role in improving wildlife management, reducing human–animal conflicts, and supporting conservation initiatives. This paper presents an innovative method for generating alert messages based on the detection of wild animal activity using hybrid deep neural networks. The proposed system integrates Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to process data obtained from multiple sensors, including motion detectors, camera traps, and audio devices. The CNN component is used to extract spatial features from images or video frames, whereas the LSTM captures temporal relationships within the sensor data, enabling precise prediction of animal movement patterns. By training the hybrid model on a comprehensive dataset of labeled animal activity, the system learns to identify patterns that signify the presence of wildlife in a specific region. Upon detecting such activity, the system generates real-time alert notifications to inform concerned authorities or nearby individuals, allowing quick response or preventive measures. The proposed approach demonstrates high accuracy and efficiency in identifying wild animal movements, even under challenging conditions such as low visibility or varying motion speeds. Through timely alerts, the system helps reduce risks for both humans and wildlife, promoting safer coexistence. This work contributes to the advancement of automated wildlife monitoring technologies and offers a dependable solution for effective conservation practices.