Faultless Decision Making for False Information in Online: A Systematic Approach

Abstract

An identifying the news are real or fake instantly with high accuracy is a challenging work. The deep learning algorithm is implementing here to acquire very accurate separation of real and fake news rather than other methods. This research work constructs naïve bayes and CNN classifiers with Q-learning decision making. The two different approaches detect fake news in online and it gives to decision making section which is designed at tail in our research. The deep decision making section compares the input and make the decision wisely and it provides the more accurate output rather than single classifiers in deep learning. This research work comprises compare between our proposed works with single classifiers.