DEVELOPING HYBRID DEEP NEURAL NETWORKS FOR DETECTING THE MOVEMENT OF WILD ANIMALS AND GENERATING ALARM MESSAGES
Keywords:
Hybrid Deep Neural Networks, Wild Animal Detection, Convolutional Neural Networks, Long Short-Term Memory, Real-Time Monitoring, Alarm System, Wildlife Conservation, Human-Wildlife Conflict, Feature Extraction, Temporal Dependencies.Abstract
The escalating human-wildlife conflictsnecessitatethedevelopmentofadvanced monitoring systems to detectwild animal movements and mitigatepotential threats. This paper proposes ahybrid deep neural network (DNN)model that integrates
References
Samreen, A., et al. (2024). “Hybrid Deep Learning Model for Wild Animal Detection.” International Journal of Computer Applications.
Tøn, C., et al. (2024). “MetadataAugmented Deep Neural Networks for Animal Recognition.” arXiv preprint arXiv:2409.04825.
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Published
2024-03-05
How to Cite
Dr.P.SUNEEL KUMAR, JAGGAIAHGARI AVILA, ASKANI AMSHITHA, B.DEEPTHI. (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(2), 1364–1370. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/2537
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