Qualitative Analysisof Online Higher Education Websites Using Support Vector Machine
European Journal of Molecular & Clinical Medicine,
2020, Volume 7, Issue 8, Pages 130-141
AbstractThe Quality analysis of online higher education websites is very important now a day. As such most of the quality parameters are defined to measure the quality of these websites. The paper is an attempt to make proper analysis of higher education websites. The research involves the creation of a database that includes well-defined parameters. Details are provided as an input to a learning model based on a vector support machine for comparison and ultimately geographical location. Testing the efficiency and accuracy of the database prepared for this task. This data was developed for seven different training, testing and verification on websites using support systems (SVM) algorithm. Algorithms trained with this database have proven to work well in site testing and level. The parameters provided are included in the model that includes the key keywords obtained (KH), average rating (AS), global standard (GR), wrap rate (BR) and daily page views per visitor (DPVPV). Thereafter a quality matrix is produced based on the quality of the extracted material. Pair pairing analysis was performed to determine the interaction between websites using scores on a quality matrix. The University of Kent (KENT) has been found to have very high standards (13212.43) and in terms of quality content.
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