Management model of higher education based on innovative using fuzzy sets
Education management models have recently emerged with plans to make school administration more effective and efficient. Higher Education (HE), a postsecondary education, leads to academic degrees. An object class having membership grades that run along a continuum is called a Fuzzy Set (FS). When t...
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| Format: | Article |
| Language: | English |
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Ayandegan Institute of Higher Education,
2024-07-01
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| Series: | Journal of Fuzzy Extension and Applications |
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| Online Access: | https://www.journal-fea.com/article_195087_b3ee7dffc97f02bdf6be93e6526872e2.pdf |
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| _version_ | 1832577845325463552 |
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| author | Chunyan Xing |
| author_facet | Chunyan Xing |
| author_sort | Chunyan Xing |
| collection | DOAJ |
| description | Education management models have recently emerged with plans to make school administration more effective and efficient. Higher Education (HE), a postsecondary education, leads to academic degrees. An object class having membership grades that run along a continuum is called a Fuzzy Set (FS). When tested in online classrooms with abnormal data, this method's effectiveness exceeded that of the intelligent education system. The challenging characteristics of such HE using FSs are the students' low family income, a complicated network, and skill development due to the low quality of education. Block structure has been developed based on HE in a FS system for students in terms of low family income, complicated networks, and skill development due to the low quality of education. Hence, in this research, Double Deep Q-Learning Network-enabled Multi-Criteria Decision-Making (D2QLN-MCDM) technologies have improved students' HE with FS . It has been used to design, develop, and verify students' HE in FS. The workforce tasked with integrating digital technology into HE has a profound effect on students' learning experiences. HE institutions will need experienced individuals with varied digital knowledge to manage and integrate these technologies effectively. The experimental analysis of D2QLN-MCDM outperforms FS using the student's HE regarding precision (99.4), accuracy (90.4%), Recall ratio (97.5%), and specificity (93.9%). |
| format | Article |
| id | doaj-art-def48bbdb31e497495a0eef53a2e7036 |
| institution | Kabale University |
| issn | 2783-1442 2717-3453 |
| language | English |
| publishDate | 2024-07-01 |
| publisher | Ayandegan Institute of Higher Education, |
| record_format | Article |
| series | Journal of Fuzzy Extension and Applications |
| spelling | doaj-art-def48bbdb31e497495a0eef53a2e70362025-01-30T15:07:12ZengAyandegan Institute of Higher Education,Journal of Fuzzy Extension and Applications2783-14422717-34532024-07-015346949310.22105/jfea.2024.448707.1416195087Management model of higher education based on innovative using fuzzy setsChunyan Xing0Seoul School of Integrated Sciences and Technologies, Seoul 03767, Korea.Education management models have recently emerged with plans to make school administration more effective and efficient. Higher Education (HE), a postsecondary education, leads to academic degrees. An object class having membership grades that run along a continuum is called a Fuzzy Set (FS). When tested in online classrooms with abnormal data, this method's effectiveness exceeded that of the intelligent education system. The challenging characteristics of such HE using FSs are the students' low family income, a complicated network, and skill development due to the low quality of education. Block structure has been developed based on HE in a FS system for students in terms of low family income, complicated networks, and skill development due to the low quality of education. Hence, in this research, Double Deep Q-Learning Network-enabled Multi-Criteria Decision-Making (D2QLN-MCDM) technologies have improved students' HE with FS . It has been used to design, develop, and verify students' HE in FS. The workforce tasked with integrating digital technology into HE has a profound effect on students' learning experiences. HE institutions will need experienced individuals with varied digital knowledge to manage and integrate these technologies effectively. The experimental analysis of D2QLN-MCDM outperforms FS using the student's HE regarding precision (99.4), accuracy (90.4%), Recall ratio (97.5%), and specificity (93.9%).https://www.journal-fea.com/article_195087_b3ee7dffc97f02bdf6be93e6526872e2.pdffuzzy sethigher educationstudentsdouble deep q-learning networkmulti-criteria decision-making |
| spellingShingle | Chunyan Xing Management model of higher education based on innovative using fuzzy sets Journal of Fuzzy Extension and Applications fuzzy set higher education students double deep q-learning network multi-criteria decision-making |
| title | Management model of higher education based on innovative using fuzzy sets |
| title_full | Management model of higher education based on innovative using fuzzy sets |
| title_fullStr | Management model of higher education based on innovative using fuzzy sets |
| title_full_unstemmed | Management model of higher education based on innovative using fuzzy sets |
| title_short | Management model of higher education based on innovative using fuzzy sets |
| title_sort | management model of higher education based on innovative using fuzzy sets |
| topic | fuzzy set higher education students double deep q-learning network multi-criteria decision-making |
| url | https://www.journal-fea.com/article_195087_b3ee7dffc97f02bdf6be93e6526872e2.pdf |
| work_keys_str_mv | AT chunyanxing managementmodelofhighereducationbasedoninnovativeusingfuzzysets |