Creating Alert Messages Based on Wild Animal Activity Detection Using Hybrid Deep Neural Networks

Natarajan, B.; Elakkiya, R.; Ramachandran, B.; Saleem, Kashif; Chaudhary, Dharminder; Samsudeen, S.

doi:10.1109/access.2023.3289586

Cited by 27 publications

(2 citation statements)

References 40 publications

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“…With the development of deep learning techniques, the performance of animal behavior recognition has been significantly improved. Natarajan et al [6] achieve high-accuracy detection of wild-animal behavior using deep learning models. In order to ensure real-time performance, Fuentes et al [7] proposed a behavior recognition algorithm for cattle based on a spatial-and-temporal information framework.…”

Section: Introductionmentioning

confidence: 99%

AI-Enabled Animal Behavior Analysis with High Usability: A Case Study on Open-Field Experiments

Chen,

Jiao,

Song

et al. 2024

Applied Sciences

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In recent years, with the rapid development of medicine, pathology, toxicology, and neuroscience technology, animal behavior research has become essential in modern life science research. However, the current mainstream commercial animal behavior recognition tools only provide a single behavior recognition method, limiting the expansion of algorithms and how researchers interact with experimental data. To address this issue, we propose an AI-enabled, highly usable platform for analyzing experimental animal behavior, which aims to provide better flexibility, scalability, and interactivity to make the platform more usable. Researchers can flexibly select or extend different behavior recognition algorithms for automated recognition of animal behaviors or experience more convenient human-computer interaction through natural language descriptions only. A case study at a medical laboratory where the platform was used to evaluate behavioral differences between sick and healthy animals demonstrated the high usability of the platform.

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Section: Introductionmentioning

confidence: 99%

AI-Enabled Animal Behavior Analysis with High Usability: A Case Study on Open-Field Experiments

Chen,

Jiao,

Song

et al. 2024

Applied Sciences

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“…The notice time ranges from dozens of minutes to hours. 14 For learning and training purposes, conventional wild animal detection and recognition methods [14][15][16] typically use a training set of gathered wild animal photos as a test sample. These investigational samples often have complete, sharp, and low-noise picture properties, but they still need to go through a lot of screening and preprocessing stages.…”

mentioning

confidence: 99%

A novel DbneAlexnet with Gazelle Hunting Optimization Algorithm enabled wild animal detection in WMSN data communication in IoT environment

Rajaretnam,

Yesodharan

2024

Int J Communication

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SummaryThe main goal of this strategy is to detect the occurrence of wild animals in farmsteads and human habitats using an improved deep learning in order to avoid loss of life and damage to agricultural crops. This paper formulates a technique for recognizing wild animals in an Internet of Things (IoT) environment utilizing deep batch normalized exponential linear unit AlexNet (DbneAlexnet) and the Gazelle Hunting Optimization Algorithm (GHOA). The IoT‐Multimedia Sensor Networks (WMSN) network is first simulated, with the IoT nodes capturing the images needed for wild animal identification. The suggested GHOA is used for routing, which sends the captured images to the base station (BS). The input wild animal image is transmitted to image preprocessing at the BS, where it is processed using the Weiner filter (WF) to remove undesirable noise from the image. The denoised output is sent into salient map extraction, which extracts salient map to determine the regions that are conspicuous or noticeable at every place in the field of vision and guides the selection of attended location. Finally, the saliency map is forwarded to DbneAlexnet for detecting the wild animals, where the DbneAlexnet is trained using proposed GHOA. The suggested GHOA algorithm is created by combining the Gazelle Optimization Algorithm (GOA) and the deer hunting optimization algorithm (DHOA). Furthermore, detection is appraised by precision, recall, and f1‐score, which provide values of 90.2%, 89.1%, and 89.6%, respectively.

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Optimizing Livestock Feed Formulation for Sustainable Agriculture

Patel,

Upasana,

Pattanaik

et al. 2024

Mathematical Modeling in Agriculture

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Creating Alert Messages Based on Wild Animal Activity Detection Using Hybrid Deep Neural Networks

Cited by 27 publications

References 40 publications

AI-Enabled Animal Behavior Analysis with High Usability: A Case Study on Open-Field Experiments

AI-Enabled Animal Behavior Analysis with High Usability: A Case Study on Open-Field Experiments

A novel DbneAlexnet with Gazelle Hunting Optimization Algorithm enabled wild animal detection in WMSN data communication in IoT environment

Optimizing Livestock Feed Formulation for Sustainable Agriculture

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