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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/52313

Title: An intelligent system for spam detection and identification of the most relevant features based on evolutionary Random Weight Networks
Authors: Kok Cheng Lim
Ali Selamat
Rose Alinda Alias
Ondrej Krejcar
Hamido Fujita
Contributors: 國立臺灣海洋大學:資訊工程學系
Keywords: Mobile augmented reality
usability metrics
systematic process
research domains
Date: 2019-07-05
Issue Date: 2019-11-18T01:47:42Z
Publisher: Applied Sciences
Abstract: Abstract: The implementation of usability in mobile augmented reality (MAR) learning applications has been utilized in a myriad of standards, methodologies, and techniques. The usage and combination of techniques within research approaches are important in determining the quality of usability data collection. The purpose of this study is to identify, study, and analyze existing usability metrics, methods, techniques, and areas in MAR learning. This study adapts systematic literature review techniques by utilizing research questions and Boolean search strings to identify prospective studies from six established databases that are related to the research context area. Seventy-two articles, consisting of 45 journals, 25 conference proceedings, and two book chapters, were selected through a systematic process. All articles underwent a rigorous selection protocol to ensure content quality according to formulated research questions. Post-synthesis and analysis, the output of this article discusses significant factors in usability-based MAR learning applications. This paper presents five identified gaps in the domain of study, modes of contributions, issues within usability metrics, technique approaches, and hybrid technique combinations. This paper concludes five recommendations based on identified gaps concealing potential of usability-based MAR learning research domains, varieties of unexplored research types, validation of emerging usability metrics, potential of performance metrics, and untapped correlational areas to be discovered.
Relation: 9(13)
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/52313
Appears in Collections:[資訊工程學系] 期刊論文

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