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Full metadata record
DC Field | Value | Language |
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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 |
Appears in Collections: | Computer Science SP |
Files in This Item:
File | Description | Size | Format | |
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MARISTELA, Reinier T.pdf | SP Document | 3.67 MB | Adobe PDF | ![]() View/Open |
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