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Predicting Website's Visual Appeal using a Hybrid of Convolutional Neural Network and Support Vector Machine

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dc.contributor.author Maristela, Reinier
dc.date.accessioned 2019-08-16T17:50:08Z
dc.date.available 2019-08-16T17:50:08Z
dc.date.issued 2017-05
dc.identifier.uri http://dspace.cas.upm.edu.ph:8080/xmlui/handle/123456789/456
dc.description.abstract In 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.iso en en_US
dc.subject Website Visual Appeal en_US
dc.subject Deep Learning en_US
dc.subject Convolutional Neural Network en_US
dc.subject Support Vector Machine en_US
dc.title Predicting Website's Visual Appeal using a Hybrid of Convolutional Neural Network and Support Vector Machine en_US
dc.type Thesis en_US


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