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![The mortality of snake bites in the countries of South Asian [11]](publication/374259235/figure/fig2/AS:11431281233737850@1712110400240/The-mortality-of-snake-bites-in-the-countries-of-South-Asian-11_Q320.jpg)
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
... 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). ...
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. ...
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.
