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. Although this is a newly approach recently it became very popular and effective. In many applications deep learning become 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 determine melting point of chemical substances this apparatus generally used in pharmaceutical and chemical industries. Proposed deep learning model classify images of chemical’s state (Solid or Liquid) by deep neural network (DNN) 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. This use python as a programming language, TensorFlow framework and keras library. The input image data mainly focuses on chemical’s  state, there are 2 categories of chemical’s  state either solid or liquid.  The Deep Neural Network (DNN) chosen as the best practice for the training process because it provides high accuracy. The results discussed in terms of the image classification accuracy in percentage. The images from two class label gets 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.