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AN AGENT-BASED DOCUMENT CLASSIFICATION MODEL TO IMPROVE THE EFFICIENCY OF THE AUTOMATED SYSTEMATIC REVIEW PROCESS

This paper proposes an Agent-based Document Classification (AbDC) model that computerizes the systematic literature review (SLR) process by imitating what a researcher is supposed to perform during the literature review process manually. The AbDC model comprises three main components that perform th...

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Main Authors: Khashfeh M., Mahmoud M.A., Mahdi M.N.
Other Authors: 57202812898
Format: Review
Published: Little Lion Scientific 2023
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spelling my.uniten.dspace-269682023-05-29T17:38:14Z AN AGENT-BASED DOCUMENT CLASSIFICATION MODEL TO IMPROVE THE EFFICIENCY OF THE AUTOMATED SYSTEMATIC REVIEW PROCESS Khashfeh M. Mahmoud M.A. Mahdi M.N. 57202812898 55247787300 56727803900 This paper proposes an Agent-based Document Classification (AbDC) model that computerizes the systematic literature review (SLR) process by imitating what a researcher is supposed to perform during the literature review process manually. The AbDC model comprises three main components that perform the SLR. Firstly, the document classification algorithm analyses a full text of research articles and evaluates relevancy. Secondly, the multi-agent architecture accelerates the mining process and handles the performance issues. Finally, the web-based systematic review tool tests and validates the functionality of the proposed AbDC model. The first testing was conducted to assess the performance of the proposed AbDC. Result shows that the required processing time was reduced by 33.5% using four agents to achieve the mining process. Meanwhile, the second testing was performed to validate the mining process results. The text extraction method was run on 200 documents from various studies to conduct the review process. The parsing process yielded valid results with 98.5% accuracy. The testing results showed that the proposed AbDC model is significant in providing researchers and postgraduate students with new means to perform SLR. � 2022 Little Lion Scientific Final 2023-05-29T09:38:14Z 2023-05-29T09:38:14Z 2022 Review 2-s2.0-85125420494 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125420494&partnerID=40&md5=32eb14a931b701f4322fdbf70b0e637e https://irepository.uniten.edu.my/handle/123456789/26968 100 3 756 775 Little Lion Scientific Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description This paper proposes an Agent-based Document Classification (AbDC) model that computerizes the systematic literature review (SLR) process by imitating what a researcher is supposed to perform during the literature review process manually. The AbDC model comprises three main components that perform the SLR. Firstly, the document classification algorithm analyses a full text of research articles and evaluates relevancy. Secondly, the multi-agent architecture accelerates the mining process and handles the performance issues. Finally, the web-based systematic review tool tests and validates the functionality of the proposed AbDC model. The first testing was conducted to assess the performance of the proposed AbDC. Result shows that the required processing time was reduced by 33.5% using four agents to achieve the mining process. Meanwhile, the second testing was performed to validate the mining process results. The text extraction method was run on 200 documents from various studies to conduct the review process. The parsing process yielded valid results with 98.5% accuracy. The testing results showed that the proposed AbDC model is significant in providing researchers and postgraduate students with new means to perform SLR. � 2022 Little Lion Scientific
author2 57202812898
author_facet 57202812898
Khashfeh M.
Mahmoud M.A.
Mahdi M.N.
format Review
author Khashfeh M.
Mahmoud M.A.
Mahdi M.N.
spellingShingle Khashfeh M.
Mahmoud M.A.
Mahdi M.N.
AN AGENT-BASED DOCUMENT CLASSIFICATION MODEL TO IMPROVE THE EFFICIENCY OF THE AUTOMATED SYSTEMATIC REVIEW PROCESS
author_sort Khashfeh M.
title AN AGENT-BASED DOCUMENT CLASSIFICATION MODEL TO IMPROVE THE EFFICIENCY OF THE AUTOMATED SYSTEMATIC REVIEW PROCESS
title_short AN AGENT-BASED DOCUMENT CLASSIFICATION MODEL TO IMPROVE THE EFFICIENCY OF THE AUTOMATED SYSTEMATIC REVIEW PROCESS
title_full AN AGENT-BASED DOCUMENT CLASSIFICATION MODEL TO IMPROVE THE EFFICIENCY OF THE AUTOMATED SYSTEMATIC REVIEW PROCESS
title_fullStr AN AGENT-BASED DOCUMENT CLASSIFICATION MODEL TO IMPROVE THE EFFICIENCY OF THE AUTOMATED SYSTEMATIC REVIEW PROCESS
title_full_unstemmed AN AGENT-BASED DOCUMENT CLASSIFICATION MODEL TO IMPROVE THE EFFICIENCY OF THE AUTOMATED SYSTEMATIC REVIEW PROCESS
title_sort agent-based document classification model to improve the efficiency of the automated systematic review process
publisher Little Lion Scientific
publishDate 2023
_version_ 1806426467582607360
score 13.211869

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