Fake news, or fabric which appeared to be untrue with point of deceiving the open, has developed in ubiquity in current a long time. Spreading this kind of data undermines societal cohesiveness and well by cultivating political division and doubt in government. Since of the sheer volume of news being disseminated through social media, human confirmation has ended up incomprehensible, driving to the improvement and arrangement of robotized strategies for the recognizable proof of wrong news. Fake news publishers use a variety of stylistic techniques to boost the popularity of their works, one of which is to arouse the readers’ emotions. Due to this, text analytics’ sentiment analysis, which determines the polarity and intensity of feelings conveyed in a text, is now being utilized in false news detection methods, as either the system’s foundation or as a supplementary component. This assessment analyzes the full explanation of false news identification. The study also emphasizes characteristics, features, taxonomy, different sorts of data in the news, categories of false news, and detection approaches for spotting fake news. This research recognized fake news using the probabilistic latent semantic analysis approach. In particular, the research describes the fundamental theory of the related work to provide a deep comparative analysis of various literature works that has contributed to this topic. Besides this, a comparison of different machine learning and deep learning techniques is done to assess the performance for fake news detection. For this purpose, three datasets have been used.

No abstract

In ad hoc networks, Clustering provides a hierarchical structure in which certain nodes are assigned the extra task (such as routing) of the network. Ordinary nodes do not participate in the routing instead they rely on coordinators of the clusters (clusterheads) for packet delivery. If a suitable tap is not applied on the number of nodes that join a clusterhead as its members, formation of bottleneck can takes place at the overloaded clusterheads. The performance of the network may get affected due to the bottleneck. This paper proposes a cluster formation algorithm in which, if the number of members of a clusterhead exceeds the predefined threshold value, a procedure of cluster division is executed. This relieves the clusterheads from the burden of excessive members. Simulation study of the proposed algorithm justifies the facts by observing an improvement in the performance in terms of E2E delay, PDF and throughput.

No abstract

No abstract