Igneous rock classification using Convolutional neural networks (CNN)

Abstract

This paper describes how convolutional neural networks are used to identify and classify igneous rocks (CNN). Igneous rocks are formed while still hot, hot magma crystallises and solidifies. Melt originates deep beneath the Earth's surface, amid active plate borders or hot zones, and then rises to the surface. There are also various kinds of igneous rocks, which are addressed throughout this work, distinguishing each one is a difficult feat in and of itself. Machine learning, which is fundamentally a three-layer neural network, is a subset of deep learning. These neural networks attempt to mimic conscious brain function of humans by letting it to "learn"from massive volumes of data, but they continue to fail. A Convolutional Neural Network (ConvNet/CNN) is a Deep Learning approach for assigning priority (learnable weights and biases) to various aspects/objects in a picture while also identifying them. Classification of images, audio and video segmentation, decision support systems, speech recognition, and image analysis are just a few of the applications for CNNs.