A development of snake bite identification system (N'viteR) using Neuro-GA

Halim, Shamimi A.; Ahmad, Azlin; Noh, Norzaidah Md; Ali, Azliza Mohd; Hamid, Nurzeatul Hamimah Abdul; Yusof, S.; Osman, Rozianawaty; Ahmad, Rashid

doi:10.1109/itime.2012.6291349

Cited by 7 publications

(2 citation statements)

References 4 publications

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“…The authors propose a model that combines both image and location information for improved accuracy in snake species classification. The study demonstrates the effectiveness of the proposed approach on a snake species identification task and provides insights into leveraging state‐of‐the‐art deep learning architectures for snake recognition [34]. This paper focuses on the development of a snake detection and classification system using deep learning techniques.…”

Section: Related Workmentioning

confidence: 99%

Health evaluation and dangerous reptile detection using a novel framework powered by the YOLO algorithm to design high‐content cellular imaging systems

Pandey,

Kumar,

Yadav

et al. 2023

The Journal of Engineering

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This novel approach in animal biology could revolutionize identifying endangered species, addressing the issue of misclassifying potentially harmful animals based solely on visual characteristics. Particularly impactful for farmers in agricultural fields, it aims to reduce the heightened risk of venomous animal attacks, ultimately improving safety. Due to a lack of accessible education, illiterate farmers are more susceptible to adopting superstitious beliefs, which tragically leads to fatal snakebites even when medical treatment is readily available. Furthermore, environmental factors can unexpectedly hold typically non‐threatening animals responsible for a large number of human deaths each year. However, the complexity of human recognition of these hazards has prompted the development of a novel design approach aimed at simplifying the process. Integration of the ResNet learning algorithm in conjunction with You Only Look Once (YOLOv5) within the framework is recommended to facilitate real‐time processing and improve accuracy. This combined approach not only speeds up animal recognition but also takes advantage of ResNet's deep learning capabilities. The first phase entails deploying YOLOv5 to detect the presence of snakes in the proposed study, achieving a remarkable 87% precision in snake detection thanks to the synergistic fusion of ResNet and YOLOv5.

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Section: Related Workmentioning

confidence: 99%

Health evaluation and dangerous reptile detection using a novel framework powered by the YOLO algorithm to design high‐content cellular imaging systems

Pandey,

Kumar,

Yadav

et al. 2023

The Journal of Engineering

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“…The injected polyvalent anti-venom contains antibodies raised against two or more species of snake that can neutralize the venom injected by a single snake bite (Calvete et al, 2007;Halim et al, 2011). The part of anit-venom that remain non-neutralized creates a further risk to the human health, making the correct identification of snakes an important problem (Halim et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Snake classification from images

James

2017

Preprint

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Incorrect snake identification from the observable visual traits is a major reason of death resulting from snake bites. So far no automatic classification method has been proposed to distinguish snakes by deciphering the taxonomy features of snake for the two major species of snakes i.e. Elapidae and Viperidae. We present a parallel processed inter-feature product similarity fusion based automatic classification of Spectacled Cobra, Russel's Viper, King Cobra, Common Krait, Saw Scaled Viper, Hump nosed Pit Viper. We identify 31 different taxonomically relevant features from snake images for automated snake classification studies. The scalability and real-time implementation of the classifier is analyzed through GPU enabled parallel computing environment. The developed systems finds application in wild life studies, analysis of snake bites and in management of snake population.

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