Predicting the Age of Abalone from Physical Measurements Using Artificial Neural Network

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Abstract
Abalones have long been a valuable food source for humans in every area of the world where a species is abundant. Predicting the age of abalone is done using physical measurements. The age of abalone is determined by cutting the shell through the cone, staining it, and counting the number of rings through a microscope -- a boring and time-consuming task. Other measurements, which are easier to obtain, are used to predict the age of abalone is using Artificial Neural Network (ANN) which is a branch of Artificial Intelligence. The dataset was collected form UCI Machine learning Repository. To predict the age of abalone using physical measurements, an ANN with multi-layer model using JustNN (JNN) tool is proposed. The proposed model was trained and tested and the accuracy was obtained. The best accuracy rate was 92.22%.
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Archival date: 2020-12-02
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2020-12-02

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