The detailed process of pattern mining to apply SOD

The detailed process of pattern mining to apply SOD

Source publication
Article
Full-text available
Incorrect snake identification from the observable visual traits is a major reason of death resulting from snake bites. The classification of snake species has a significant role in determining the appropriate treatment without any delay, the delay may cause dangerous complications or lead to the death of the victim. The difficulty of classifying s...

Citations

... Deep learning models, particularly CNNs like VGG16, ResNet50, MobileNetV2, and DenseNet121, have shown promise in accurate snake classification. Among these, VGG16 achieved the highest accuracy of 97.09% when classifying 45 snake species, demonstrating the potential for deep learning in aiding rapid, life-saving identification in herpetology (Ahmed et al., 2023). ...
Article
Full-text available
The threat of snakebites to public health, particularly in tropical and subtropical regions, requires effective mitigation strategies to avoid human-snake interactions. With the development of an IoT-based smart snake-trapping device, an innovative non-invasive solution for preventing snakebites is presented, autonomously capturing and identifying snakes. Using artificial intelligence (AI) and Internet of Things (IoT) technologies, the entire system is designed to improve the safety and efficiency of snake capture, both in rural and urban areas. A camera and sensors are installed in the device to detect heat and vibration signatures, mimicking the natural prey of snakes using tungsten wire and vibration motors to attract them into the trap. A real-time classification algorithm based on deep learning determines whether a snake is venomous or non-venomous as soon as the device detects it. This algorithm utilizes a transfer learning approach using a convolutional neural network (CNN) and has been trained using snake images, achieving an accuracy of 91.3%. As a result of this identification process, appropriate actions are taken, such as alerting authorities or releasing non-venomous snakes into the environment in a safe manner. Through the integration of IoT technology, users can receive real-time notifications and data regarding the trap via a smartphone application. The system’s connectivity allows for timely intervention in case of venomous species, reducing snakebite risks. Additionally, the system provides information regarding snake movement patterns and species distribution, contributing to the study of broader ecological issues. An automated and efficient method of managing snakes could be implemented in snakebite-prone regions with the smart trapping device.
... Wang & Deng, 2021). This study aims to overcome this problem by applying the Nearest Neighbour Interpolation method and Naive Bayes Classifier in the identification of bespectacled faces (Ahmed et al., 2023). ...
Article
Full-text available
Facial recognition technology has rapidly advanced, but identifying individuals wearing glasses remains challenging due to altered or obscured facial features. This study addresses this issue by combining the Nearest Neighbor Interpolation Method and Naive Bayes Classification for bespectacled face identification. The method applies interpolation to enhance facial image quality, preserving critical features before classification by Naive Bayes into spectacle and non-spectacle classes. Using the Kaggle MeGlass dataset for training and testing, the approach achieved a training accuracy of 78%, a testing accuracy of 76%, and a cross-validation value of 0.70. These results indicate a significant improvement in recognizing bespectacled faces, contributing to enhanced accuracy in facial recognition systems. Despite these advancements, further improvements are possible, such as integrating more advanced models and expanding the dataset, which could lead to even greater accuracy and reliability in practical applications. This research provides a novel solution to a persistent challenge in facial recognition technology
... Laborde (2021) suggests that the final model is "a transfer learning version of MobileNet, PoseNet, or some other practical model that fits your needs." Ahmed et al. (2023) tested several base models on snake classification, and MobieNet was one of the top algorithms, the one we are using as base model for transfer learning. ...
Article
Full-text available
Introduction: deep learning emerged in 2012 as one of themost important machine learning technologies, reducing image identification error from25% to 5%. This article has two goals: 1) to demonstrate to the general public the ease of building state-of-the-art machine learningmodels without coding expertise; 2) to present a basicmodel adaptable to any biological image identification, such as species identification. Method: We present three test-of-conceptmodels thatshowcase distinct perspectives of the app. Themodels aim at separating images into classes such as genus, species, and subspecies, and the input images can be easily adapted for different cases. We have applied deep learning and transfer learning using TeachableMachine. Results: Our basicmodels demonstrate high accuracy in identifying different speciesbased on images, highlighting the potential for thismethod to be applied in biology. Discussions: the presentedmodels showcase the ease of using machine learning nowadays for image identification. Furthermore, the adaptability of this method to various species and genuses emphasizes its importance in the biological fields, as root for inspiring collaborations with computer science. On our, future collaborations could lead to increasingly accurate and efficientmodels in this arena using well-curated datasets.