Analysis of entrepreneurship education in colleges and based on improved decision tree algorithm and fuzzy mathematics

Mao, Li; Zhang, Wanhong

doi:10.3233/jifs-189210

Cited by 28 publications

(16 citation statements)

References 13 publications

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“…In order to improve the effectiveness of entrepreneurship and innovation education in colleges and universities, Mao L adopts decision tree and fuzzy mathematics as the basis of model algorithm and built an evaluation system and model for innovation and entrepreneurship education in colleges and universities. He provides a complete and practical tool for government education departments and colleges and universities to evaluate the implementation of innovation and entrepreneurship education and builds an evaluation index system for innovation and entrepreneurship education in colleges and universities [ 6 ]. The materials and methods adopted by Dinesh T are to consider two groups of decision tree algorithm and naive Bayes algorithm.…”

Section: Related Workmentioning

confidence: 99%

Identification Method of Influencing Factors of Hospital Catering Service Satisfaction Based on Decision Tree Algorithm

Liu

et al. 2022

Applied Bionics and Biomechanics

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Entering the 21st century, material abundance has been greatly enriched, and living standards have been continuously improved. Now society is gradually moving towards the era of experience economy. From the perspective of experience economy, patients’ demands for hospitals are not only the satisfaction of medical technology, but their catering consumption also has begun to change to the pursuit of higher requirements. Decision tree algorithm is a kind of data mining algorithm. Data mining technology is a young technology for data analysis. It can simulate mathematical models or algorithms through data analysis, which greatly improves the prediction accuracy. This paper aims to study how to identify the influencing factors of hospital catering service satisfaction, and proposes the application of decision tree algorithm to the hospital catering service satisfaction research, and proposes decision tree-related algorithms, such as ID3, C4.5, and C5.0. Based on the analysis of patients’ satisfaction with the hospital catering service in a certain hospital, the results of the model study based on the decision tree algorithm show that the risk estimation value of the training set is 0.064, and the total correct percentage is 93.6%. The risk estimate for the test set was 0.065, for a total correct percentage of 93.5%. It can be seen that the effect of the model is good and can be effectively predicted.

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Section: Related Workmentioning

confidence: 99%

Identification Method of Influencing Factors of Hospital Catering Service Satisfaction Based on Decision Tree Algorithm

Liu

et al. 2022

Applied Bionics and Biomechanics

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show abstract

“…The fuzzy evaluation method has a strong comprehensive judgment ability, and the index weights determined by using hierarchical analysis method make the fuzzy evaluation more scientific [23,24]. Fuzzy mathematics is a mathematical tool to study many problems with unclear boundaries in reality [25]. One of its basic concepts is fuzzy set, which can be used to comprehensively evaluate the problem [26,27].…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Evaluation of Teaching Quality on IP Environment Driven by Multiple Values Theory Based on Big Data

2022

Journal of Environmental and Public Health

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Despite the fact that big data technology has been applied in education, there are no studies and cases that combine big data with ideological and political (IP) teaching quality. At the same time, the existing methods of IP teaching quality evaluation lack the consideration of multiple values, and the system is not complete and systematic. The use of big data analysis technology can improve the rigor of teaching quality assessment and make the data analysis more scientific, so as to improve the management system of universities and enhance the education quality. Therefore, this paper fully considers the background conditions of large data at this stage, on the basis of studying the methods of evaluating the quality of IP teaching in colleges. The big data about teaching quality is obtained by distributed algorithm, and multiple value indicators are drawn into the quality evaluation system as a main driver to emphasize the multiple value theory. Hierarchical analysis (AHP) method and fuzzy comprehensive evaluation (FCE) method are selected as the data analysis methods to provide evaluation basis for the proposed model. This model can further test the evaluation index system of education and further verify the rationality of the distribution of the weight of indicators at all levels. The evaluation results based on the large educational data and research data of a university show that the IP teaching quality of the university is excellent. The comprehensive evaluation model overcomes the limitations of traditional evaluation methods and provides a more comprehensive analysis about the teaching quality of IP teaching in colleges. Meanwhile, the conclusions obtained by the proposed evaluation model can be used for both the overall comprehensive evaluation of teachers' teaching quality and a single comprehensive evaluation of the single factor affecting teaching quality. Using the evaluation results obtained by the model, we can set up advanced models and encourage backward students to have evidence. With the single-index evaluation, we can know what advantages the IP teaching or a certain teacher has and what aspects need to be strengthened. Therefore, we can put forward reasonable suggestions to progress instructing strategies and educating quality.

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“…Among the above algorithms, the ID3 algorithm is the representative of the decision tree algorithm, and most decision tree algorithms are improved on its basis. It adopts the “divide and conquer” strategy [ 28 ]. When selecting attributes on all levels of nodes in the decision tree, it uses information gain as the selection standard of attributes, so that when testing on each nonleaf node, it can obtain the largest category information about the tested records [ 29 ].…”

Section: Methodsmentioning

confidence: 99%

Clothing Design Style Recommendation Using Decision Tree Algorithm Combined with Deep Learning

Yang¹

2022

Computational Intelligence and Neuroscience

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Most clothing recommendation methods have problems such as high resource consumption and inconsistent subjectively labeled clothing labels. Based on this, a multilabel classification algorithm based on deep learning (DL) theory is introduced, based on which the clothing style recognition model is constructed. Next, the concept of the decision tree algorithm is given, and the clothing recommendation model is built based on this algorithm. Moreover, the clothing style recognition model based on a multilabel classification algorithm and the clothing recommendation system based on a decision tree algorithm are tested by building simulation experiments and combining neural network technology. Finally, the application of the decision tree algorithm and DL theory in clothing recommendation design is studied through the literature collection method. The research focus is to realize the recognition of clothing through decision tree algorithm and DL method to achieve the intelligent recommendation of clothing style. The results show that: (1) the neural network technology in DL theory can realize efficient recognition and classification of clothing style by automatically extracting image features and combining with a multilabel classification algorithm. (2) The decision tree algorithm can make an initial recommendation according to users’ style preferences, then make implicit recommendations through user retrieval, browsing, and other operations, and make dynamic clothing style recommendations to users. (3) When the neural network based on a multilabel classification algorithm is trained, the precision, recall rate, and F1 values are 0.73, 0.43, and 0.55, respectively. (4) After using the clothing recommendation system based on the decision tree algorithm, the subjects’ average satisfaction is 86.25%, indicating that this system can give users a better clothing recommendation experience. This exploration aims to provide a crucial reference for further improving the quality of clothing recommendation services. It has important theoretical significance and practical value for the development of artificial intelligence in the field of fashion design, and is expected to provide a reference for the development of bionics.

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Analysis of entrepreneurship education in colleges and based on improved decision tree algorithm and fuzzy mathematics

Cited by 28 publications

References 13 publications

Identification Method of Influencing Factors of Hospital Catering Service Satisfaction Based on Decision Tree Algorithm

Identification Method of Influencing Factors of Hospital Catering Service Satisfaction Based on Decision Tree Algorithm

[Retracted] Evaluation of Teaching Quality on IP Environment Driven by Multiple Values Theory Based on Big Data

Clothing Design Style Recommendation Using Decision Tree Algorithm Combined with Deep Learning

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