Classification Of Mushrooms Using Artificial Neural Network

ABSTRACT The artificial neural network (ANN) has had remarkable success in pattern recognition in recent years. It stands for a new learning paradigm in artificial intelligence (AI) and machine learning and has been applied to problems ranging from speech recognition to the prediction of protein secondary structure, cancers, and gene prediction. Recent breakthrough results in image analysis and speech recognition have generated massive interest in this field. However, the mathematical and computational methodology underlying deep learning models is very challenging, especially for interdisciplinary scientists. In this manuscript, a Neural Network model is used to classify whether a given mushroom is edible or poisonous using Tensorflow in Python based on the attributes present in the dataset. The dataset includes descriptions of hypothetical samples corresponding to 23 species of gilled mushrooms in the Agaricus and Lepiota Family Mushroom, drawn from The Audubon Society Field Guide to North American Mushrooms (1981).