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

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dc.contributor.authorKhashfeh M.en_US
dc.contributor.authorMahmoud M.A.en_US
dc.contributor.authorMahdi M.N.en_US
dc.contributor.authorid57202812898en_US
dc.contributor.authorid55247787300en_US
dc.contributor.authorid56727803900en_US
dc.date.accessioned2023-05-29T09:38:14Z
dc.date.available2023-05-29T09:38:14Z
dc.date.issued2022
dc.description.abstractThis 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 Scientificen_US
dc.description.natureFinalen_US
dc.identifier.epage775
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85125420494
dc.identifier.spage756
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85125420494&partnerID=40&md5=32eb14a931b701f4322fdbf70b0e637e
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26968
dc.identifier.volume100
dc.publisherLittle Lion Scientificen_US
dc.sourceScopus
dc.sourcetitleJournal of Theoretical and Applied Information Technology
dc.titleAN AGENT-BASED DOCUMENT CLASSIFICATION MODEL TO IMPROVE THE EFFICIENCY OF THE AUTOMATED SYSTEMATIC REVIEW PROCESSen_US
dc.typeReviewen_US
dspace.entity.typePublication
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