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Predicting Educational Relevance For an Efficient Classification of Talent

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dc.contributor.author Uddin, Muhammad Fahim
dc.contributor.author Lee, Jeongkyu
dc.date.accessioned 2017-04-07T19:28:37Z
dc.date.available 2017-04-07T19:28:37Z
dc.date.issued 2017-03-24
dc.identifier.uri https://scholarworks.bridgeport.edu/xmlui/handle/123456789/1936
dc.description.abstract This research work utilizes machine learning approach to build a predictive model for the prediction of the students and the job seekers’ to quantify their fitness's for the courses and jobs they plan to pursue, respectively. Some of the existing research utilizes GPA for academic prediction and use personality prediction and computing in social domains for various industrial goals. On the other hand, this research work advances the state of the art to correlate and blend the personality features with the academic attributes to identify and classify the relevant talent of the individuals for the academic and real world success with improved predictive modeling. This work incorporates three algorithms to quantify a talent in the relevance, and then predict good fit students and good fit candidates, based on supervised learning, stochastic probability distribution and classification rules, etc. This work opens many opportunities for future research towards Genomics data mining to mine individuals for various areas. en_US
dc.language.iso en_US en_US
dc.subject Course scheduling en_US
dc.subject Job seeking en_US
dc.subject Machine learning en_US
dc.subject Predictive analysis en_US
dc.title Predicting Educational Relevance For an Efficient Classification of Talent en_US
dc.type Presentation en_US
dc.institute.department School of Engineering en_US
dc.institute.name University of Bridgeport en_US
dc.event.location Bridgeport, CT en_US
dc.event.name Faculty Research Day en_US

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