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

Augmented reality; Learning algorithms; Machine learning; Usability engineering; Between clusters; Mobile augmented reality; Prioritization; Prioritization techniques; Unsupervised machine learning; Usability; K-means clustering

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Main Authors: Lim K.C., Selamat A., Mohamed Zabil M.H., Selamat M.H., Alias R.A., Puteh F., Mohamed F., Krejcar O.
Other Authors: 57188850203
Format: Conference Paper
Published: IOS Press 2023
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author Lim K.C.
Selamat A.
Mohamed Zabil M.H.
Selamat M.H.
Alias R.A.
Puteh F.
Mohamed F.
Krejcar O.
author2 57188850203
author_facet 57188850203
Lim K.C.
Selamat A.
Mohamed Zabil M.H.
Selamat M.H.
Alias R.A.
Puteh F.
Mohamed F.
Krejcar O.
author_sort Lim K.C.
building UNITEN Library
collection Institutional Repository
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
continent Asia
country Malaysia
description Augmented reality; Learning algorithms; Machine learning; Usability engineering; Between clusters; Mobile augmented reality; Prioritization; Prioritization techniques; Unsupervised machine learning; Usability; K-means clustering
format Conference Paper
id my.uniten.dspace-24492
institution Universiti Tenaga Nasional
publishDate 2023
publisher IOS Press
record_format dspace
spelling my.uniten.dspace-244922023-05-29T15:23:58Z Quantifying usability prioritization using K-means clustering algorithm on hybrid metric features for MAR learning Lim K.C. Selamat A. Mohamed Zabil M.H. Selamat M.H. Alias R.A. Puteh F. Mohamed F. Krejcar O. 57188850203 24468984100 35185866500 57215520379 25928253600 57202529348 55416008900 14719632500 Augmented reality; Learning algorithms; Machine learning; Usability engineering; Between clusters; Mobile augmented reality; Prioritization; Prioritization techniques; Unsupervised machine learning; Usability; K-means clustering This 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. Final 2023-05-29T07:23:58Z 2023-05-29T07:23:58Z 2019 Conference Paper 10.3233/FAIA190049 2-s2.0-85080966555 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85080966555&doi=10.3233%2fFAIA190049&partnerID=40&md5=cd7704e7c851e65163b73f89bc1adc19 https://irepository.uniten.edu.my/handle/123456789/24492 318 190 204 IOS Press Scopus
spellingShingle Lim K.C.
Selamat A.
Mohamed Zabil M.H.
Selamat M.H.
Alias R.A.
Puteh F.
Mohamed F.
Krejcar O.
Quantifying usability prioritization using K-means clustering algorithm on hybrid metric features for MAR learning
title Quantifying usability prioritization using K-means clustering algorithm on hybrid metric features for MAR learning
title_full Quantifying usability prioritization using K-means clustering algorithm on hybrid metric features for MAR learning
title_fullStr Quantifying usability prioritization using K-means clustering algorithm on hybrid metric features for MAR learning
title_full_unstemmed Quantifying usability prioritization using K-means clustering algorithm on hybrid metric features for MAR learning
title_short Quantifying usability prioritization using K-means clustering algorithm on hybrid metric features for MAR learning
title_sort quantifying usability prioritization using k-means clustering algorithm on hybrid metric features for mar learning
url_provider http://dspace.uniten.edu.my/