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; Bharanidharan Shanmugam; 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, Bharanidharan Shanmugam, Sundaresan Perumal

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

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	Advanced Science Letters
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

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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 Bharanidharan Shanmugam 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 et al.
Advanced Science Letters. Vol. 24 1. ed. American Scientific Publishers, 2018. p. 690-694.

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

TY - GEN

T1 - A Proposed False Report Identification Algorithm for a Mobile Application in the IoT Environment

AU - Rajoo, Sharmiladevi

AU - Magalingam, Pritheega

AU - Idris, Norbik Bashah

AU - Narayana Samy, Ganthan

AU - Maarop, Nurazean

AU - Shanmugam, Bharanidharan

AU - Perumal, Sundaresan

PY - 2018/1/1

Y1 - 2018/1/1

N2 - 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.

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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DO - 10.1166/asl.2018.11789

M3 - Conference Paper published in Proceedings

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SP - 690

EP - 694

BT - Advanced Science Letters

PB - American Scientific Publishers

ER -

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

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