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Issue title: Complex evolutionary artificial intelligence in cognitive digital twinning
Guest editors: Neal Wagner, Sundhararajan, Le Hoang Son and Meng Joo
Article type: Research Article
Authors: Mao, Lia; b; * | Zhang, Wanhonga
Affiliations: [a] China University of Mining and Technology, Xuzhou, Jiangsu, China | [b] Jiangsu Agri-animal Husbandry Vocational College, Taizhou, China
Correspondence: [*] Corresponding author. Li Mao, E-mail: jsmaoli@126.com.
Abstract: In order to improve the performance of entrepreneurship and innovation education in colleges and universities, this study attempts to build an evaluation system and model of innovation and entrepreneurship in colleges and universities to provide a complete and practical tool for government education authorities and universities to evaluate the implementation of innovation and entrepreneurship education. In this research, decision tree and fuzzy mathematics are used as the basis of the model algorithm, and the algorithm is improved based on the analysis of traditional algorithms. Moreover, based on the improved decision tree algorithm, an evaluation index system for university innovation and entrepreneurship education is constructed. After determining the evaluation indicators of innovation and entrepreneurship education in colleges and universities, this study uses several universities as examples to analyze and define the definitions of various indicators. In addition, this study statistically analyzes the results of entrepreneurship and innovation education in colleges and universities through simulation. The research shows that the model proposed in this paper has a certain practical effect, and based on the simulation results, this study makes several suggestions.
Keywords: Neural network, improved algorithm, path ranking, network education, knowledge recommendation
DOI: 10.3233/JIFS-189210
Journal: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 2, pp. 2095-2107, 2021
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