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dc.contributor.authorRahim, Md. Abdur
dc.contributor.authorRahman, Md Arafatur
dc.contributor.authorRahman, Md Mustafizur
dc.contributor.authorZaman, Nafees
dc.contributor.authorMoustafa, Nour
dc.contributor.authorRazzak, Imran
dc.date.accessioned2022-06-30T09:30:14Z
dc.date.available2022-06-30T09:30:14Z
dc.date.issued2022-05-31
dc.identifier.citationRahim, M.A., Rahman, M.A., Rahman, M.M., Zaman, N., Moustafa, N. and Razzak, I. (2022) An intelligent risk management framework for monitoring vehicular engine health. IEEE Transactions on Green Communications and Networking, 6(3), pp. 1298-1306 10.1109/TGCN.2022.3179350en
dc.identifier.issn2473-2400en
dc.identifier.doi10.1109/TGCN.2022.3179350en
dc.identifier.urihttp://hdl.handle.net/2436/624812
dc.descriptionThis is an accepted manuscript of an article published by IEEE on 31/05/2022, available online: https://ieeexplore.ieee.org/document/9785863 The accepted version of the publication may differ from the final published version.en
dc.description.abstractThe unwanted vehicular engine irregularities diminish vehicular competence, hinder productivity, waste time, and sluggish personal/national economic growth. Transportation sectors are essential infrastructures that require practical vulnerability assessment to avoid unexpected consequences through risk severity assessment. Artificial intelligence would be vital in the Industry 4.0 era to eliminate these issues for seamless activity and ultimate productivity. This article presents a risk management framework that includes an efficient decision model for monitoring and diagnosing vehicular engine health and condition in real-time using vulnerable components information and advanced techniques. To do this, we used the vulnerability identification frame to identify the vulnerable objects. We created a decision model that used an infrastructure vulnerability assessment model and sensor-actuator data to diagnose and categorise engine conditions as good, minor, moderate, or critical. We used machine learning and deep learning algorithms to assess the effectiveness of the risk management system’s decision model. The stacked ensemble of the deep learning algorithm outperformed other standard machine learning and deep learning algorithms in providing 80.3% decision accuracy for the 80% training data and efficiently managing large amounts of data. Anticipating the proposed framework might assist the automotive sector in advancing with cutting-edge facilities that are up to date.en
dc.formatapplication/pdfen
dc.language.isoenen
dc.publisherIEEEen
dc.relation.urlhttps://ieeexplore.ieee.org/document/9785863en
dc.subjectVHMSen
dc.subjectvulnerable componentsen
dc.subjectfault diagnosis modelen
dc.subjectengine health managementen
dc.subjectinternet of vehiclesen
dc.subjectIoVen
dc.titleAn intelligent risk management framework for monitoring vehicular engine healthen
dc.typeJournal articleen
dc.identifier.eissn2473-2400
dc.identifier.journalIEEE Transactions on Green Communications and Networkingen
dc.date.updated2022-06-29T10:22:15Z
dc.date.accepted2022-05-19
rioxxterms.funderUniversiti Malaysia Pahang
rioxxterms.identifier.project10.13039/501100005605-Universiti Malaysia Pahang (Grant Number: RDU192203)
rioxxterms.versionAMen
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en
rioxxterms.licenseref.startdate2022-06-30en
dc.source.volume6
dc.source.issue3
dc.source.beginpage1298
dc.source.endpage1306
dc.description.versionPublished version
refterms.dateFCD2022-06-30T09:29:33Z
refterms.versionFCDAM
refterms.dateFOA2022-06-30T09:30:14Z


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