Improved Fuzzy Algorithm for College Students’ Academic Early Warning
The existing fuzzy clustering algorithms are mostly fuzzy comprehensive evaluation algorithms based on specific elements, but the main problem of such fuzzy algorithms is the lack of overall research on the responsible individuals and the lack of hierarchy in the algorithms. It is suitable for data...
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| Vydáno v: | Mathematical problems in engineering Ročník 2022; s. 1 - 9 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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New York
Hindawi
15.04.2022
John Wiley & Sons, Inc |
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| ISSN: | 1024-123X, 1563-5147 |
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| Abstract | The existing fuzzy clustering algorithms are mostly fuzzy comprehensive evaluation algorithms based on specific elements, but the main problem of such fuzzy algorithms is the lack of overall research on the responsible individuals and the lack of hierarchy in the algorithms. It is suitable for data mining of academic early warning systems. Therefore, an improved fuzzy algorithm based on fuzzy performance evaluation based on composite elements is proposed, and it is applied to the performance evaluation system to solve the complex problems in performance evaluation. In the process of building smart campuses in colleges and universities, academic prewarning, as the main part of smart campuses, mainly uses data mining technology to ensure students complete their studies smoothly and at the same time provides certain decision-making support for colleges and universities. Based on the research topic of the relevant departments of a certain school, this paper aims to build an academic early warning system suitable for the school to ensure that students can successfully complete their studies. The main research contents are divided into two parts: “study early warning model research” and “design and implementation of an academic early warning system.” Through analysis and experiments, it is proved that the model evaluation effect based on the algorithm improvement is the best, with recall reaching 85%, precision reaching 78.96%, and AUC reaching 80.25%. |
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| AbstractList | The existing fuzzy clustering algorithms are mostly fuzzy comprehensive evaluation algorithms based on specific elements, but the main problem of such fuzzy algorithms is the lack of overall research on the responsible individuals and the lack of hierarchy in the algorithms. It is suitable for data mining of academic early warning systems. Therefore, an improved fuzzy algorithm based on fuzzy performance evaluation based on composite elements is proposed, and it is applied to the performance evaluation system to solve the complex problems in performance evaluation. In the process of building smart campuses in colleges and universities, academic prewarning, as the main part of smart campuses, mainly uses data mining technology to ensure students complete their studies smoothly and at the same time provides certain decision-making support for colleges and universities. Based on the research topic of the relevant departments of a certain school, this paper aims to build an academic early warning system suitable for the school to ensure that students can successfully complete their studies. The main research contents are divided into two parts: “study early warning model research” and “design and implementation of an academic early warning system.” Through analysis and experiments, it is proved that the model evaluation effect based on the algorithm improvement is the best, with recall reaching 85%, precision reaching 78.96%, and AUC reaching 80.25%. |
| Author | Chen, Siyu |
| Author_xml | – sequence: 1 givenname: Siyu orcidid: 0000-0001-7106-8975 surname: Chen fullname: Chen, Siyu organization: School of EconomicsAnhui University of Finance and EconomicsBengbu 233000Chinaaufe.edu.cn |
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| Cites_doi | 10.1007/s11277-020-07266-6 10.1177/1550147718810692 10.3788/yjyxs20183301.0055 10.1177/1550147720920478 10.1016/j.eswa.2016.06.005 10.3233/jifs-189246 10.1109/mnet.011.1900251 10.1177/0165551515613226 10.1109/jsac.2020.2986615 10.1007/s11276-020-02359-3 10.1109/AEMCSE50948.2020.00128 10.3390/s18010153 10.1504/ijvs.2017.087180 10.1177/0165551516677911 10.1109/MWC.2019.1800419 10.4218/etrij.2020-0112 10.3390/s18051539 10.1109/ACCESS.2020.2990974 10.1016/j.actbio.2014.05.012 10.1049/iet-com.2018.0171 10.1109/JIOT.2020.3039359 10.4028/www.scientific.net/amr.886.552 10.1016/j.eswa.2015.05.006 10.1088/1755-1315/660/1/012140 |
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| Copyright | Copyright © 2022 Siyu Chen. Copyright © 2022 Siyu Chen. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0 |
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| References | 22 B. J. Fan (19) 2019; 36 24 26 27 28 29 Q. Liu (23) 2020; 18 A. Onan (14) 30 10 11 12 13 15 16 17 18 B. H. Xiao (21) 2018; 031 Z. Ying (25) 2018; 18 1 2 3 4 6 X. Hong (5) 2019; 32 7 8 9 20 |
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| SubjectTerms | Algorithms Clustering College students Colleges & universities Data mining Decision making Early warning systems Extracurricular activities Mathematical problems Performance evaluation Students Teaching |
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| Title | Improved Fuzzy Algorithm for College Students’ Academic Early Warning |
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