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Predicting Web Interface Quality Assessment Using Support Vector Machine

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dc.contributor.author Wah, Naw Lay
dc.date.accessioned 2019-11-14T05:42:29Z
dc.date.available 2019-11-14T05:42:29Z
dc.date.issued 2012-02-28
dc.identifier.uri http://onlineresource.ucsy.edu.mm/handle/123456789/2402
dc.description.abstract Internet websites are promising as the products for providing services. The key issues related to website engineering are very essential. These websites need to measure and evaluate for quality and for better understanding. Several Metrics were proposed to correspond with items that Web usability guideline associate with good design. In this paper, we investigate empirically the web page quality on the basis of the 16 assessed metrics. We also propose Support Vector Machine (SVM) prediction model to predict the classification of the good web pages and not good web pages. We collect web sites from Webby awards data (2001-2010) and Top Ten PC Magazines. We express the findings of quantitative analysis of web page attributes and how these attributes are calculated. The metrics captured in SVM model can be used to predict the good and the bad of website design. en_US
dc.language.iso en_US en_US
dc.publisher Tenth International Conference On Computer Applications (ICCA 2012) en_US
dc.title Predicting Web Interface Quality Assessment Using Support Vector Machine en_US
dc.type Article en_US


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