Developing Hybrid Deep Neural Networks for Detecting the Movement of Wild Animals and Generating Alarm Messages
Keywords:
Hybrid Deep Neural Networks, Movement, Wild Animals, Generating Alarm MessagesAbstract
The increasing human-wildlife conflict necessitates advanced surveillance systems to monitor and detect the movement of wild
animals in vulnerable areas such as farmlands, highways, and human settlements. This study proposes a Hybrid Deep Neural Network (HDNN) model that combines Convolutional Neural Networks (CNNs) for feature extraction and Recurrent Neural Networks (RNNs) for sequential pattern recognition to enhance accuracy in detecting wild animal movements.
References
Smith, J., & Brown, K. (2021)."Challenges in Wildlife Surveillance: A Review of Traditional and AI-Based Methods." Journal of Wildlife Research, 45(3), 123-135
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Published
2024-09-10
How to Cite
P. Suneel Kumar, J. Avila, B. Deepthi, A. Amshitha. (2024). Developing Hybrid Deep Neural Networks for Detecting the Movement of Wild Animals and Generating Alarm Messages. Journal of Computational Analysis and Applications (JoCAAA), 33(05), 1141–1148. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/1868
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