A Proposed False Report Identification Algorithm for a Mobile Application in the IoT Environment

Sharmiladevi Rajoo; Pritheega Magalingam; Norbik Bashah  Idris; Ganthan Narayana Samy; Nurazean Maarop; Sundaresan Perumal

doi:10.1166/asl.2018.11789

A Proposed False Report Identification Algorithm for a Mobile Application in the IoT Environment

Sharmiladevi Rajoo, Pritheega Magalingam, Norbik Bashah Idris, Ganthan Narayana Samy, Nurazean Maarop, Sundaresan Perumal

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper published in Proceedings

Abstract

In this research, a false report identification algorithm for mobile application is developed using a text classification technique. This algorithm is proposed to be applied to a reporting service application in an IoT environment. The algorithm is aimed to distinguish reports into true and false information. Support Vector Machine (SVM) is used as the text classifier because it has proven to be the most popularly used due to its good performance and higher accuracy compared to the other techniques such as Naïve Bayes, Decision Tree and K-Nearest Neighbours. The algorithm is designed and developed in R Studio and we built a framework to show how the algorithms can be adapted into a reporting service application. The results show that the algorithm has successfully classified the reports.

Original language	English
Title of host publication	American Scientific Publishers
Publisher	American Scientific Publishers
Pages	690-694
Number of pages	5
Volume	24
Edition	1
DOIs	https://doi.org/10.1166/asl.2018.11789
Publication status	Published - 1 Jan 2018
Externally published	Yes

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Cite this

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title = "A Proposed False Report Identification Algorithm for a Mobile Application in the IoT Environment",

abstract = "In this research, a false report identification algorithm for mobile application is developed using a text classification technique. This algorithm is proposed to be applied to a reporting service application in an IoT environment. The algorithm is aimed to distinguish reports into true and false information. Support Vector Machine (SVM) is used as the text classifier because it has proven to be the most popularly used due to its good performance and higher accuracy compared to the other techniques such as Na{\"i}ve Bayes, Decision Tree and K-Nearest Neighbours. The algorithm is designed and developed in R Studio and we built a framework to show how the algorithms can be adapted into a reporting service application. The results show that the algorithm has successfully classified the reports.",

author = "Sharmiladevi Rajoo and Pritheega Magalingam and Idris, {Norbik Bashah} and {Narayana Samy}, Ganthan and Nurazean Maarop and Sundaresan Perumal",

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A Proposed False Report Identification Algorithm for a Mobile Application in the IoT Environment. / Rajoo, Sharmiladevi; Magalingam, Pritheega; Idris, Norbik Bashah ; Narayana Samy, Ganthan; Maarop, Nurazean; Perumal, Sundaresan.

American Scientific Publishers. Vol. 24 1. ed. American Scientific Publishers, 2018. p. 690-694.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper published in Proceedings

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AU - Idris, Norbik Bashah

AU - Narayana Samy, Ganthan

AU - Maarop, Nurazean

AU - Perumal, Sundaresan

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AB - In this research, a false report identification algorithm for mobile application is developed using a text classification technique. This algorithm is proposed to be applied to a reporting service application in an IoT environment. The algorithm is aimed to distinguish reports into true and false information. Support Vector Machine (SVM) is used as the text classifier because it has proven to be the most popularly used due to its good performance and higher accuracy compared to the other techniques such as Naïve Bayes, Decision Tree and K-Nearest Neighbours. The algorithm is designed and developed in R Studio and we built a framework to show how the algorithms can be adapted into a reporting service application. The results show that the algorithm has successfully classified the reports.

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