Please use this identifier to cite or link to this item: http://repository.futminna.edu.ng:8080/jspui/handle/123456789/28020
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dc.contributor.authorJibrin, A-
dc.contributor.authorAlhassan, J.K.-
dc.contributor.authorAdepoju, Solomon Adelowo-
dc.date.accessioned2024-05-06T15:26:05Z-
dc.date.available2024-05-06T15:26:05Z-
dc.date.issued2022-11-
dc.identifier.urihttp://repository.futminna.edu.ng:8080/jspui/handle/123456789/28020-
dc.description.abstractThe current academic system consists of various mental struggles ranging from family, peers, lecturers and the academic system generally. However, high level of stress on university students negatively affect their academic performance. In this paper, we describe how to efficiently select the best parameters to develop the proposed model. The Grid-Search-CV techniques is adopted to fine-tune the Support Vector Machine(SVM) classifier with different parametric combination, the best parameter configuration that provides the highest prediction accuracy is selected for training our model. Hence, the proposed student stress prediction model has shown a high degree of prediction accuracy (99%).en_US
dc.language.isoenen_US
dc.subjectstreesen_US
dc.subjectFinetuningen_US
dc.subjectGrid-Search-CVen_US
dc.subjectMental health stressen_US
dc.titleA Stress Based Prediction Model for University Student Using Support Vector Machine and Grid-Search-CV for Parameter Turningen_US
dc.typeArticleen_US
Appears in Collections:Computer Science

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