Prediction of Fraud App Detection Techniques
P. Gopinath1, V. Ariyamala2
1P. Gopinath*, Student, Computer science and Engineering, Saveetha University, Chennai, India.
2V. Ariyamala, Associate Professor, Computer science and Engineering, Saveetha University, Chennai, India.
Manuscript received on January 05, 2020. | Revised Manuscript received on January 25, 2020. | Manuscript published on January 30, 2020. | PP: 3789-3791 | Volume-8 Issue-5, January 2020. | Retrieval Number: E6517018520/2020©BEIESP | DOI: 10.35940/ijrte.E6517.018520
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The use of mobile has increased a lot in day to day life. In this advance technology new apps are been discovered a lot and these apps are available in many app markets. The app market contains a lot of similar apps in it and we confuse as which is the apt one to use. The apps some are useful and some are fraud. The detecting fraud app helps user to easily identify the real one and download it. The revelation of fraud app through analysis of review, rating and ranking using datamining technique. The analysis of the app using sentiment analysis technique i.e. a datamining technique used to analyse the sentiment of the reviews. The analysis of the review predicts the positive and negative rating apps separately. The classification score to extract rating and ranking evidences is given with fuzzy score classification. The main objective of this paper is to survey on different techniques and methodology used for detecting fraud apps.
Keywords: Evidence Aggregation, Fraud detection, Fuzzy score, Mobile app, Rating and Ranking, Sentiment analysis, stemming, User review.
Scope of the Article: Predictive Analysis.