{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T23:16:55Z","timestamp":1782515815461,"version":"3.54.5"},"reference-count":23,"publisher":"Wiley","license":[{"start":{"date-parts":[[2013,1,1]],"date-time":"2013-01-01T00:00:00Z","timestamp":1356998400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"funder":[{"DOI":"10.13039\/501100001868","name":"National Science Council","doi-asserted-by":"publisher","award":["NSC 101-2221-E-035-034"],"award-info":[{"award-number":["NSC 101-2221-E-035-034"]}],"id":[{"id":"10.13039\/501100001868","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2013]]},"abstract":"<jats:p>Classifying the student academic performance with high accuracy facilitates admission decisions and enhances educational services at educational institutions. The purpose of this paper is to present a neuro-fuzzy approach for classifying students into different groups. The neuro-fuzzy classifier used previous exam results and other related factors as input variables and labeled students based on their expected academic performance. The results showed that the proposed approach achieved a high accuracy. The results were also compared with those obtained from other well-known classification approaches, including support vector machine, Naive Bayes, neural network, and decision tree approaches. The comparative analysis indicated that the neuro-fuzzy approach performed better than the others. It is expected that this work may be used to support student admission procedures and to strengthen the services of educational institutions.<\/jats:p>","DOI":"10.1155\/2013\/179097","type":"journal-article","created":{"date-parts":[[2013,11,4]],"date-time":"2013-11-04T16:00:51Z","timestamp":1383580851000},"page":"1-7","source":"Crossref","is-referenced-by-count":40,"title":["A Neuro-Fuzzy Approach in the Classification of Students\u2019 Academic Performance"],"prefix":"10.1155","volume":"2013","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8937-5102","authenticated-orcid":true,"given":"Quang Hung","family":"Do","sequence":"first","affiliation":[{"name":"Department of Industrial Engineering and Systems Management, Feng Chia University, No. 100, Wenhwa Road, Taichung 40724, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2100-3182","authenticated-orcid":true,"given":"Jeng-Fung","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering and Systems Management, Feng Chia University, No. 100, Wenhwa Road, Taichung 40724, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","reference":[{"key":"2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2006.04.005"},{"issue":"3","key":"4","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1007\/s12539-011-0102-9","volume":"3","year":"2011","journal-title":"Interdisciplinary Sciences, Computational Life Sciences"},{"issue":"4","key":"5","first-page":"1","volume":"9","year":"2011","journal-title":"International Journal of Computer Science and Information Security"},{"key":"7","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.compedu.2012.08.015","volume":"61","year":"2013","journal-title":"Computers & Education"},{"key":"8","volume-title":"A concise fuzzy rule base to reason student performance based on rough-fuzzy approach","year":"2010"},{"key":"9","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2010.02.068"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.1109\/21.256541"},{"key":"11","year":"1999"},{"key":"12","year":"1997"},{"key":"13","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2011.09.010"},{"key":"14","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2007.03.006"},{"key":"15","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2005.02.005"},{"key":"16","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-009-0410-8"},{"key":"17","year":"2010"},{"key":"18","year":"2006"},{"key":"19","year":"2003"},{"key":"20","doi-asserted-by":"publisher","DOI":"10.1002\/cem.873"},{"key":"21","year":"2011"},{"key":"23","year":"1997"},{"key":"24","year":"2003"},{"key":"25","year":"2001"},{"key":"27","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1061\/(ASCE)HE.1943-5584.0000599","volume":"18","year":"2013","journal-title":"Journal of Hydrologic Engineering"},{"issue":"4","key":"28","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1016\/S0893-6080(05)80056-5","volume":"6","year":"1993","journal-title":"Neural Networks"}],"container-title":["Computational Intelligence and Neuroscience"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2013\/179097.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2013\/179097.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2013\/179097.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2017,6,21]],"date-time":"2017-06-21T21:23:21Z","timestamp":1498080201000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.hindawi.com\/journals\/cin\/2013\/179097\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2013]]},"references-count":23,"alternative-id":["179097","179097"],"URL":"https:\/\/doi.org\/10.1155\/2013\/179097","relation":{},"ISSN":["1687-5265","1687-5273"],"issn-type":[{"value":"1687-5265","type":"print"},{"value":"1687-5273","type":"electronic"}],"subject":[],"published":{"date-parts":[[2013]]}}}