RECURRENT CONVOLUTION NEURAL NETWORK FOR SPACE DEBRIS AND ASTEROIDS DETECTION AND CLASSIFICATION ON THE MONITORED SATELLITE INFORMATION USING DEEP SPACE NETWORK

With the proliferation of Online Social Networks (OSNs), people of all walks of life are able to have instant sharing of information and opinions. Thus they became virtual platforms for exchanging views and making friends. The news shared over social media helps in understanding various aspects. They also come in the form of reviews or product or service news that will help people in making well informed decisions. As people are increasingly relying on social media to ascertain knowledge of products or services, the news over social media needs to be genuine. Unfortunately, it is observed that there are some fake news articles or reviews over OSNs. Unless, such news is identified and eliminated, it will cause people in making wrong decisions. They influence of fake news on people will defeater the purpose of social media content. The existing machine learning algorithms used for fake news detection provide a great deal of advantages in detection of fake news. However, they suffer from deterioration of performance due to lack of multiple layers of training. This is achieved by deep learning techniques. In this paper, a deep Convolutional Neural Network (CNN) is used for prediction of fake news. An algorithm known as Deep Fake News Detection (DFND) is proposed and implemented.  A prototype application is built using Python data science platform to show the utility of the proposed system. The empirical results of the proposed system are compared with that of existing ones.