Convolutional Neural Network Classification of Coleoptera through Keras and TensorFlow
Authors: 
Randy Echavez Angelia, John Louie B. Torres, Cristelle Ann G. Cagadas, Annalee ManigoAuthors Info & Claims
Randy Echavez Angelia, John Louie B. Torres, Cristelle Ann G. Cagadas, Annalee ManigoAuthors Info & ClaimsPages 129 - 135
Abstract
An application was developed to address the time-consuming and subjective nature of physical examination and human judgment to classify the diverse families of Coleoptera more efficiently. Specifically focusing on the Coleoptera families in the Philippines, the application utilizes a Convolutional Neural Network (CNN)-based model as its classification architecture. To enhance the accuracy of the CNN model, researchers gathered images of Coleoptera from various families. The program achieves an impressive 94.44% accuracy rate and has demonstrated successful results in classifying Coleoptera families. This application offers a more streamlined and reliable approach to Coleoptera classification, benefiting researchers and enthusiasts.
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- Convolutional Neural Network Classification of Coleoptera through Keras and TensorFlow
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April 2024
491 pages
ISBN:9798400717055
DOI:10.1145/3669754
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Published: 30 August 2024
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ICCAI 2024
ICCAI 2024: 2024 10th International Conference on Computing and Artificial Intelligence
April 26 - 29, 2024
Bali Island, Indonesia
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