{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T16:37:20Z","timestamp":1769186240993,"version":"3.49.0"},"reference-count":26,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2022,2,18]],"date-time":"2022-02-18T00:00:00Z","timestamp":1645142400000},"content-version":"vor","delay-in-days":48,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2022,1]]},"abstract":"<jats:p>Students\u2019 performance is an important factor for the evaluation of teaching quality in colleges. The aim of this study is to propose a novel intelligent approach to predict students\u2019 performance using support vector regression (SVR) optimized by an improved duel algorithm (IDA). To the best of our knowledge, few research studies have been developed to predict students\u2019 performance based on student behavior, and the novelty of this study is to develop a new hybrid intelligent approach in this field. According to the obtained results, the IDA\u2010SVR model clearly outperformed the other models by achieving less mean square error (MSE). In other words, IDA\u2010SVR with an MSE of 0.0089 has higher performance than DT with an MSE of 0.0326, SVR with an MSE of 0.0251, ANN with an MSE of 0.0241, and PSO\u2010SVR with an MSE of 0.0117. To investigate the efficacy of IDA, other parameter optimization methods, that is, the direct determination method, grid search method, GA, FA, and PSO, are used for a comparative study. The results show that the IDA algorithm can effectively avoid the local optima and the blindness search and can definitely improve the speed of convergence to the optimal solution.<\/jats:p>","DOI":"10.1155\/2022\/1845571","type":"journal-article","created":{"date-parts":[[2022,2,18]],"date-time":"2022-02-18T10:50:06Z","timestamp":1645181406000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Prediction of Students\u2019 Performance Based on the Hybrid IDA\u2010SVR Model"],"prefix":"10.1155","volume":"2022","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1674-3557","authenticated-orcid":false,"given":"Huan","family":"Xu","sequence":"first","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2022,2,18]]},"reference":[{"key":"e_1_2_7_1_2","doi-asserted-by":"crossref","unstructured":"SravaniB.andBalaM. M. Prediction of student performance using linear regression Proceedings of the 2020 International Conference for Emerging Technology (INCET) June 2020 Belgaum India IEEE 1\u20135.","DOI":"10.1109\/INCET49848.2020.9154067"},{"key":"e_1_2_7_2_2","doi-asserted-by":"publisher","DOI":"10.1177\/0272989x05275154"},{"key":"e_1_2_7_3_2","article-title":"An empirical study of the naive bayes classifier","volume":"1","author":"Rish I.","year":"2001","journal-title":"Journal of Universal Computer Science"},{"key":"e_1_2_7_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/bf00116251"},{"key":"e_1_2_7_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/s0731-7085(99)00272-1"},{"key":"e_1_2_7_6_2","doi-asserted-by":"publisher","DOI":"10.5120\/15022-3310"},{"key":"e_1_2_7_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2020.2986809"},{"key":"e_1_2_7_8_2","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/5871684"},{"key":"e_1_2_7_9_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-021-03630-w"},{"key":"e_1_2_7_10_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11280-020-00820-z"},{"key":"e_1_2_7_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.114162"},{"key":"e_1_2_7_12_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-021-00770-8"},{"key":"e_1_2_7_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107674"},{"key":"e_1_2_7_14_2","doi-asserted-by":"crossref","unstructured":"SchidlerA.andSzeiderS. SAT-based decision tree learning for large data sets Proceedings of the AAAI 21 the Thirty-Fifth AAAI Conference on Artificial Intelligence 2021 Vancouver Canada.","DOI":"10.1609\/aaai.v35i5.16509"},{"key":"e_1_2_7_15_2","doi-asserted-by":"publisher","DOI":"10.1017\/s0956792520000182"},{"key":"e_1_2_7_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2020.107286"},{"key":"e_1_2_7_17_2","doi-asserted-by":"crossref","unstructured":"BunkarK. SinghU. K. PandyaB. andBunkarR. Data mining: prediction for performance improvement of graduate students using classification Proceedings of the 2012 Ninth International Conference on Wireless and Optical Communications Networks (WOCN) September 2012 Indore India IEEE 1\u20135.","DOI":"10.1109\/WOCN.2012.6335530"},{"key":"e_1_2_7_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2013.07.003"},{"key":"e_1_2_7_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2010.07.003"},{"key":"e_1_2_7_20_2","doi-asserted-by":"crossref","unstructured":"AlliasN. IsmailM. N. andde SilvaK. A hybrid gini PSO-SVM feature selection: an empirical study of population sizes on different classifier Proceedings of the International Conference on Artificial Intelligence December 2013 Kota Kinabalu Malaysia.","DOI":"10.1109\/AIMS.2013.24"},{"key":"e_1_2_7_21_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00366-019-00858-2"},{"key":"e_1_2_7_22_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.conbuildmat.2020.120457"},{"key":"e_1_2_7_23_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ultras.2021.106511"},{"key":"e_1_2_7_24_2","unstructured":"BiyantoT. FibriantoH. ListijoriniE. andBudiatiT. Duelist algorithm: an algorithm inspired by how duelist improve their capabilities in a duel Proceedings of the Seventh International Conference on Swarm Intelegence 2015 Bali Indonesia."},{"key":"e_1_2_7_25_2","doi-asserted-by":"publisher","DOI":"10.1016\/s0893-6080(03)00169-2"},{"key":"e_1_2_7_26_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-020-05560-w"}],"container-title":["Complexity"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2022\/1845571","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1155\/2022\/1845571","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2022\/1845571","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T21:11:20Z","timestamp":1769116280000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2022\/1845571"}},"subtitle":[],"editor":[{"given":"Murari","family":"Andrea","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2022,1]]},"references-count":26,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,1]]}},"alternative-id":["10.1155\/2022\/1845571"],"URL":"https:\/\/doi.org\/10.1155\/2022\/1845571","archive":["Portico"],"relation":{},"ISSN":["1076-2787","1099-0526"],"issn-type":[{"value":"1076-2787","type":"print"},{"value":"1099-0526","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1]]},"assertion":[{"value":"2021-06-28","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-10-26","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-02-18","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"1845571"}}