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Quantifying usability prioritization using K-means clustering algorithm on hybrid metric features for MAR learning

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dc.citedby3
dc.contributor.authorLim K.C.en_US
dc.contributor.authorSelamat A.en_US
dc.contributor.authorMohamed Zabil M.H.en_US
dc.contributor.authorSelamat M.H.en_US
dc.contributor.authorAlias R.A.en_US
dc.contributor.authorPuteh F.en_US
dc.contributor.authorMohamed F.en_US
dc.contributor.authorKrejcar O.en_US
dc.contributor.authorid57188850203en_US
dc.contributor.authorid24468984100en_US
dc.contributor.authorid35185866500en_US
dc.contributor.authorid57215520379en_US
dc.contributor.authorid25928253600en_US
dc.contributor.authorid57202529348en_US
dc.contributor.authorid55416008900en_US
dc.contributor.authorid14719632500en_US
dc.date.accessioned2023-05-29T07:23:58Z
dc.date.available2023-05-29T07:23:58Z
dc.date.issued2019
dc.descriptionAugmented reality; Learning algorithms; Machine learning; Usability engineering; Between clusters; Mobile augmented reality; Prioritization; Prioritization techniques; Unsupervised machine learning; Usability; K-means clusteringen_US
dc.description.abstractThis paper presents and discusses an empirical work of using machine learning K-means clustering algorithm in analyzing and processing Mobile Augmented Reality (MAR) learning usability data. This paper first discusses the issues within usability and machine learning spectrum, then explain in detail a proposed methodology approaching the experiments conducted in this research. This contributes in providing empirical evidence on the feasibility of K-means algorithm through the discreet display of preliminary outcomes and performance results. This paper also proposes a new usability prioritization technique that can be quantified objectively through the calculation of negative differences between cluster centroids. Towards the end, this paper will discourse important research insights, impartial discussions and future works. � 2019 The authors and IOS Press. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.3233/FAIA190049
dc.identifier.epage204
dc.identifier.scopus2-s2.0-85080966555
dc.identifier.spage190
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85080966555&doi=10.3233%2fFAIA190049&partnerID=40&md5=cd7704e7c851e65163b73f89bc1adc19
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24492
dc.identifier.volume318
dc.publisherIOS Pressen_US
dc.sourceScopus
dc.sourcetitleFrontiers in Artificial Intelligence and Applications
dc.titleQuantifying usability prioritization using K-means clustering algorithm on hybrid metric features for MAR learningen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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