Abalone life phase classification with deep learning

Abstract

In this paper, we present algorithmic and architectural comparison of deep learning models for predicting abalone age range. While abalone age can be determined through very detailed steps in a laboratory, we present an efficient method for determining its age through machine learning models. We present a precise and an efficient method for converting data to a computable version through binary encoding and normalization. We experiment with various topological variances in neural network architectures, convolutional approach to the task at hand and recently succeeding residual neural network architecture for finding the optimal prediction accuracy and efficiency. Although the conventional machine learning methods showed success in this field, our deep learning model tests yield an accuracy of 79.09% accuracy, surpassing the conventional machine learning algorithms as we incorporated methods for preventing over-fitting and methods for normalizing the output throughout the network.