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dc.contributor.authorMaristela, Reinier-
dc.date.accessioned2019-08-16T17:50:08Z-
dc.date.available2019-08-16T17:50:08Z-
dc.date.issued2017-05-
dc.identifier.urihttp://dspace.cas.upm.edu.ph:8080/xmlui/handle/123456789/456-
dc.description.abstractIn predicting a website's visual appeal, hand-crafted feature extractors may not be able to determine all the relevant features to extract from a screenshot of a website's homepage. Also, since it is hand-crafted, there is a need to determine what features to extract. This study aims to use a hybrid of convolutional neural network and support vector machine to predict a website's visual appeal. Using the hybrid CNN-SVM model, the AI system would extract features from an image of a website's homepage and determine its visual appeal with respect to the age, gender, country, and educational level of the target users.en_US
dc.language.isoenen_US
dc.subjectWebsite Visual Appealen_US
dc.subjectDeep Learningen_US
dc.subjectConvolutional Neural Networken_US
dc.subjectSupport Vector Machineen_US
dc.titlePredicting Website's Visual Appeal using a Hybrid of Convolutional Neural Network and Support Vector Machineen_US
dc.typeThesisen_US
Appears in Collections:Computer Science SP

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