A Deep Learning Model Based on CNN Using Keras and TensorFlow to Determine Real-Time Melting Point of Chemical Substances

Abstract

Deep learning is a subset of machine learning that uses artificial neural networks inspired by human cognitive systems. In many applications, deep learning becomes most successful approach where machine learning has been successful at certain rates. In the succession of these, the proposed deep learning model is suitable for melting-point detection apparatus which determines melting point of chemical substances this apparatus generally used in pharmaceutical and chemical industries. Proposed deep learning model classifies images of chemical’s state (solid or liquid) by deep neural network (DNN), and it consists of TensorFlow framework, libraries like Keras and activation function like ReLu, sigmoid, maxpool and flatten to determine melting point of chemical substances. The proposed model enables to TensorFlow architecture, which can determine the melting point of chemicals in real time on a single board computer. The input image data mainly focuses on chemical’s state, and there are two categories of chemical’s state either solid or liquid. The results discussed in terms of the image classification accuracy in percentage. The images from two class label get maximum accuracy is 99.72% and maximum validation accuracy is 99.37% same as liquid’s image and the average value of accuracy 84.17% or higher after certain epochs.