Awareness, Attitude and Approach of Rural Public to Snakes &amp; Snake Bites

Chidananda, PS; Kumar, Lakshya; Raksha, R; Yadav, Abhishek

doi:10.5958/0974-0848.2018.00016.7

Cited by 2 publications

(2 citation statements)

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“…Every year, more than 20,000 deaths are caused by high intervention in epidemiological and clinical settings around the world [16]. One of the most pressing challenges in e-commerce and intranets is security.…”

Section: Related Workmentioning

confidence: 99%

“…As a result, this information can be used as a foundation for any preventive action in the face of snake toxins and their physiopathological and clinical impacts. Every year, more than 20,000 deaths are caused by high intervention in epidemiological and clinical settings around the world [16]. One of the most pressing challenges in e‐commerce and intranets is security.…”

Section: Related Workmentioning

confidence: 99%

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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%

Section: Related Workmentioning

confidence: 99%