{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T08:50:20Z","timestamp":1770281420693,"version":"3.49.0"},"reference-count":22,"publisher":"SAGE Publications","issue":"2","license":[{"start":{"date-parts":[[2018,12,13]],"date-time":"2018-12-13T00:00:00Z","timestamp":1544659200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2019,3,16]]},"abstract":"<jats:p>Allocating university resources, especially defining the number of necessary student groups and laboratory classes is a hard task without knowing the exact number of students who will enroll in the given courses. This number usually depends on the exam results of the prerequisite courses. However, the planning of the next term has to be done some months before the end of the actual term. This paper presents the creation of a fuzzy model that can predict the student results in case of the Visual Programming course with an acceptable accuracy based on nine input factors describing the relevant history of the student. The model has a low complexity rule base containing only 28 rules and predicts the exam result using fuzzy rule interpolation based inference. The position of the rule consequent sets as well as the rule weights were tuned by particle swarm optimization. The root mean squared error expressed in percentage of the output range was less than 13% in case of all the training, validation and test datasets, which gives a satisfactory level of information for the planning of the number of student groups and laboratory classes in the next term in case of the next course that follows the examined Visual Programming course.<\/jats:p>","DOI":"10.3233\/jifs-169875","type":"journal-article","created":{"date-parts":[[2018,12,14]],"date-time":"2018-12-14T11:24:04Z","timestamp":1544786644000},"page":"999-1008","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":8,"title":["Fuzzy rule interpolation based model for student result prediction"],"prefix":"10.1177","volume":"36","author":[{"given":"Zsolt Csaba","family":"Johany\u00e1k","sequence":"first","affiliation":[{"name":"Department of Information Technology, John von Neumann University, Izs\u00e1ki \u00fat, Kecskem\u00e9t, Hungary"}]}],"member":"179","published-online":{"date-parts":[[2018,12,13]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TFUZZ.2004.836085"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/TFUZZ.2008.2008412"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10846-010-9420-0"},{"key":"e_1_3_2_5_2","first-page":"161","article-title":"Fuzzy interpolation with generalized representative values","author":"Huang Z.H.","year":"2004","unstructured":"Z.H.Huang and Q.Shen, Fuzzy interpolation with generalized representative values, Proceedings of the UK Workshop on Computational Intelligence, 2004, pp. 161\u2013171.","journal-title":"Proceedings of the UK Workshop on Computational Intelligence"},{"issue":"2","key":"e_1_3_2_6_2","first-page":"295","article-title":"A modified particle swarm optimization algorithm for the optimization of a fuzzy classification subsystem in a series hybrid electric vehicle","volume":"24","author":"Johanyak Z.C.","year":"2017","unstructured":"Z.C.Johanyak, A modified particle swarm optimization algorithm for the optimization of a fuzzy classification subsystem in a series hybrid electric vehicle, in: Technicki Vjesnik - Technical Gazette 24(Suppl. 2) (2017), 295\u2013301.","journal-title":"Technicki Vjesnik - Technical Gazette"},{"key":"e_1_3_2_7_2","first-page":"29","article-title":"New Initial Fuzzy System Generation Features in the SFMI Toolbox","author":"Johanyak Z.C.","year":"2013","unstructured":"Z.C.Johanyak, New Initial Fuzzy System Generation Features in the SFMI Toolbox, Proceedings of the 5th IEEE International Symposium on Logistics and Industrial Informatics (LINDI), 2013, pp. 29\u201334.","journal-title":"Proceedings of the 5th IEEE International Symposium on Logistics and Industrial Informatics (LINDI)"},{"key":"e_1_3_2_8_2","first-page":"147","article-title":"Performance Enhancement of the Fuzzy Rule Interpolation Method FRISUV by Rule Weights","author":"Johanyak Z.C.","year":"2014","unstructured":"Z.C.Johanyak, Performance Enhancement of the Fuzzy Rule Interpolation Method FRISUV by Rule Weights, Proceedings of the 6th Gyor Symposium and 3rd Hungarian-Polish and 1st Hungarian-Romanian Joint Conference on Computational Intelligence, 2014, pp. 147\u2013151.","journal-title":"Proceedings of the 6th Gyor Symposium and 3rd Hungarian-Polish and 1st Hungarian-Romanian Joint Conference on Computational Intelligence,"},{"key":"e_1_3_2_9_2","first-page":"495","article-title":"Fuzzy Rule Interpolation by the Least Squares Method","author":"Johanyak Z.C.","year":"2006","unstructured":"Z.C.Johanyak and S.Kovacs, Fuzzy Rule Interpolation by the Least Squares Method, Proceedings of the 7th International Symposium of Hungarian Researchers on Computational Intelligence (HUCI), 2006, pp. 495\u2013506.","journal-title":"Proceedings of the 7th International Symposium of Hungarian Researchers on Computational Intelligence (HUCI),"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICNN.1995.488968"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/0888-613X(93)90010-B"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-34783-6_48"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0020-7373(75)80002-2"},{"issue":"2","key":"e_1_3_2_14_2","first-page":"73","article-title":"Fuzzy inference system optimized by genetic algorithm for robust face and pose detection","volume":"13","author":"Moallem P.","year":"2015","unstructured":"P.Moallem, B.S.Mousavi and S.Sh.Naghibzadeh, Fuzzy inference system optimized by genetic algorithm for robust face and pose detection, in: International Journal of Artificial Intelligence 13(2) (2015), 73\u201388.","journal-title":"International Journal of Artificial Intelligence"},{"key":"e_1_3_2_15_2","first-page":"361","article-title":"Electric Vehicles' Battery Parameter Tolerances Analysis by Fuzzy Logic","author":"Pokoradi L.","year":"2016","unstructured":"L.Pokoradi and J.Menyhart, Electric Vehicles' Battery Parameter Tolerances Analysis by Fuzzy Logic, Proceedings of thellth IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI), 2016, pp. 361\u2013364.","journal-title":"Proceedings of thellth IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI),"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2014.07.004"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-014-1644-7"},{"key":"e_1_3_2_18_2","first-page":"517","article-title":"A two dimensional interpolation function for irregularly spaced data","author":"Shepard D.","year":"1968","unstructured":"D.Shepard, A two dimensional interpolation function for irregularly spaced data, Proceedings of the 23rd ACM International Conference, 1968, pp. 517\u2013524.","journal-title":"Proceedings of the 23rd ACM International Conference,"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.1985.6313399"},{"issue":"12","key":"e_1_3_2_20_2","first-page":"20","article-title":"Adaptation of Fuzzy Cognitive Maps for Navigation Purposes by Migration Algorithms","volume":"8","author":"Vascakand J.","year":"2012","unstructured":"J.Vascakand M.Pal'a, Adaptation of Fuzzy Cognitive Maps for Navigation Purposes by Migration Algorithms, in: International Journal of Artificial Intelligence 8(S12) (2012), 20\u201337.","journal-title":"International Journal of Artificial Intelligence"},{"key":"e_1_3_2_21_2","first-page":"173","article-title":"Fuzzy Rule Interpolation and Reinforcement Learning","author":"Vincze D.","year":"2017","unstructured":"D.Vincze, Fuzzy Rule Interpolation and Reinforcement Learning, Proceedings of the 15th IEEE International Symposium on Applied Machine Intelligence and Informatics, 2017, pp. 173\u2013178.","journal-title":"Proceedings of the 15th IEEE International Symposium on Applied Machine Intelligence and Informatics"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/TFUZZ.2005.859316"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0019-9958(65)90241-X"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-169875","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.3233\/JIFS-169875","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-169875","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T19:22:48Z","timestamp":1770232968000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/JIFS-169875"}},"subtitle":[],"editor":[{"given":"Wen-Hsiang","family":"Hsieh","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2018,12,13]]},"references-count":22,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2019,3,16]]}},"alternative-id":["10.3233\/JIFS-169875"],"URL":"https:\/\/doi.org\/10.3233\/jifs-169875","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,12,13]]}}}