Radial Restricted Boltzmann Machines with Functional Neural Network for Classification of the Fake and Real News Analysis

Abstract

News methods are the main source of news for people around the world. However, there was a surprising rise in social media spread in later periods due to the significant increase in technology and updates. Fake news is as old as the news industry - misinformation, hoax, propaganda and satire have been around for a long time. So fake news is untrue and false information without proper evidence. Auto-detection technology is being built and studied on artificial intelligence and deep learning to address fake news’s rise and spread. This method’s efforts have been made to automate the process of fake news detection. This existing machine learning method has less classification accuracy, and multiple combination Machine learning methods have not consumed memory usage; it provides a higher false rate. Overcome this problem by introducing a deep learning method, namely Radial Restricted Boltzmann Machines with Functional Neural Network (R2BM-FNN), to reduce memory usage from a large set of training data. Initially, to apply the Count Victories distribution for data preprocessing to remove the stop words and unrelated text from data sets. In this proposed method neural network to train the neurons with the help of stochastic gradient descent. This proposed GRU-RLU method results in classification accuracy, precision, sensitivity, specificity, and execution time to evaluate system performance.