Optimization Algorithm for Ideological and Political Curriculum Environment in Colleges Using Data Analysis and Neighborhood Search Operator.
In the context of the new era, the distinctive function of BD (big data) analysis and prediction also introduces a new way of thinking to university IPE (ideological and political education), broadens the domain of university IPE, and enhances the curricular offerings of IPE universities. In order t...
| Publicado en: | Journal of Environmental & Public Health Vol. 2022; pp. 1 - 10 |
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| Autor principal: | |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
9/16/2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=159425104&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 159425104 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 16879805 9034 jtl: Journal of Environmental & Public Health issn: 16879805 maglogo: N pubinfo: dt: 9/16/2022 vid: 2022 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 159425104 159425104 NLM36159750 159425104 10.1155/2022/5833589 NLM36159750 159425104 ppf: 1 ppct: 9 formats: tig: atl: Optimization Algorithm for Ideological and Political Curriculum Environment in Colleges Using Data Analysis and Neighborhood Search Operator. aug: au: Luo, Chaoyuan affil: School of Public Administration, South China University of Technology, Guangzhou 510640, Guangdong, China sug: subj: Curriculum Algorithms Human Colleges and Universities Learning Comparative Studies Multicenter Studies Evaluation Research ab: In the context of the new era, the distinctive function of BD (big data) analysis and prediction also introduces a new way of thinking to university IPE (ideological and political education), broadens the domain of university IPE, and enhances the curricular offerings of IPE universities. In order to enhance the intelligence and personalization of the intelligent teaching system, this paper describes in detail the design and implementation processes for each component of the system. It also uses the association mining rule algorithm of data mining. To maintain population diversity, a population initialization method and a neighborhood-based search operator are used, both of which are based on a thorough consideration of the characteristics of complex networks. The neighborhood search strategy enhances the local search capability of the TLBO (Teaching-Learning Based Optimization) algorithm. The optimized TLBO algorithm presented in this paper achieves the highest average modularity value of 0.5238 through testing on real-world data sets. The outcomes demonstrate that the algorithm performs well and is successful in identifying problems in the community. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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