Climate Change Effects on Venomous Snakes

Bouazza, Abdellah; Hidan, Moulay Abdelmonaim El; Aimrane, Abdelmohcine; Kahime, Kholoud; Lansari, Aziza; Laaradia, Mehdi Ait; Lahouaoui, Hasna; Moukrim, Abdellatif

doi:10.4018/978-1-6684-5678-1.ch066

Cited by 3 publications

(2 citation statements)

References 67 publications

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“…k‐Nearest Neighbour, Support Vector Machine, Decision Trees J48, and Naive Bayes were the four types of machine learning techniques that were used throughout the training and classification processes. The J48 algorithm achieved the highest possible classification accuracy of 71.6% accurate prediction for the NLP‐Snake dataset [14]. This was accomplished thanks to the algorithm's excellent precision and recall.…”

Section: Methodsmentioning

confidence: 99%

“…Four machine learning techniques (Naive Bayes, k‐Nearest Neighbour, Support Vector Machine, and Decision Trees J48) were used for training and classification. For the NLP‐Snake dataset, the J48 algorithm's maximum classification accuracy was 71.6% correct prediction, with good precision and recall [14]. In automatic image categorization systems, CNN is widely employed.…”

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: Methodsmentioning

confidence: 99%

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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A Novel Framework for Detection of Harmful Snakes Using YOLO Algorithm

Naresh,

Babu,

Darshan

et al. 2023

SN COMPUT. SCI.

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The spotlight of our discussion here is on developing a model for detecting snakes present in the agricultural fields using cameras and machine learning algorithms. The idea is to construct a machine learning model to identify the snakes, which will be trained on a vast set of images of snakes. These images will be carefully selected to include the snake patterns, skin color of different species of snakes (extraction of the unique features). This is done to train the model properly to identify different types of snakes that the farmer encounters in the fields. Most famers are unaware and have a need for a way to detect these snakes so that they can either avoid them or get rid of them. Hence, this system can be implemented to prevent thousands of deaths and can help famers alleviate the repercussions of snake bites. Mistakes in recognizing potentially harmful animal species based only on visual cues are major contributors to the high death toll from venomous animal attacks. Since they spend so much time in the fields, where rice and wheat are cultivated, farmers are at a higher risk of being bitten by a snake than the general population. Because of their ignorance, illiterate farmers are more inclined to believe in superstitions, which can lead to their untimely deaths from snakebites despite medical intervention. Animals that would normally pose no threat to humans are responsible for the deaths of thousands of people every year because of environmental factors. However, because it is hard for humans to recognize these dangers, a new design paradigm has been developed to make it simpler. Researchers in the field of animal biology can use it to search for endangered species. Predators may enter gardens and green areas like tea and coffee plantations. There is not yet a plan in place to implement automated sorting for discovering distinctions. By applying the suggested framework to photographs of potentially dangerous animals, several factors useful for studying animal organization may be easily identified. For the classifier to run in real-time, it is suggested that YOLO be utilized for double processing within the framework. Furthermore, the YOLO technique enables quick animal recognition in a similar fashion. First, the YOLO method will be used to determine whether a snake is present in the proposed work. Here, 87 percent precision is also reached in the detection of snakes.

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