Implementing Campus Indoor Location Tracking System

Neelakandan, S.; Muthukumaran, S.; Annamalai, R.

doi:10.18535/ijecs/v5i5.68

Cited by 2 publications

(1 citation statement)

References 7 publications

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“…It is possible for distinguishing the contents that simulates the open source however, the sources are not true. The spread patterns of the fake news on the social television are frequently investigated for identifying the features of the fake news that supports the discrimination among the legitimate and the fake news respectively [4]. The challenge in the detection of the fake news has been determined in various forms.…”

Section: Introductionmentioning

confidence: 99%

Social Networks Fake Account and Fake News Identification with Reliable Deep Learning

Kanagavalli¹,

Priya²

2022

Intelligent Automation &Amp; Soft Computing

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Recent developments of the World Wide Web (WWW) and social networking (Twitter, Instagram, etc.) paves way for data sharing which has never been observed in the human history before. A major security issue in this network is the creation of fake accounts. In addition, the automatic classification of the text article as true or fake is also a crucial process. The ineffectiveness of humans in distinguishing the true and false information exposes the fake news as a risk to credibility, democracy, logical truth, and journalism in government sectors. Besides, the automatic fake news or rumors from the social networking sites is a major research area in the field of social media analytics. With this motivation, this paper develops a new reliable deep learning (DL) based fake account and fake news detection (RDL-FAFND) model for the social networking sites. The goal of the RDL-FAFND model is to resolve the major problems involved in the social media platforms namely fake accounts, fake news/rumor identification. The presented RDL-FAFND model detects the fake account by the use of a parameter tuned deep stacked Auto encoder (DSAE) using the krill herd (KH) optimization algorithm for detecting the fake social networking accounts. Besides, the presented RDL-FAFND model involves an ensemble of the machine learning (ML) models with different linguistic features (EML-LF) for categorizing the text as true or fake. An extensive set of experiments have been carried out for highlighting the superior performance of the RDL-FAFND model. A detailed comparative results analysis has stated that the presented RDL-FAFND model is considerably better than the existing methods.

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Section: Introductionmentioning

confidence: 99%