Sports Economic Operation Index Prediction Model Based on Deep Learning and Ensemble Learning.
Journal
Computational intelligence and neuroscience
ISSN: 1687-5273
Titre abrégé: Comput Intell Neurosci
Pays: United States
ID NLM: 101279357
Informations de publication
Date de publication:
2022
2022
Historique:
received:
13
01
2022
revised:
09
02
2022
accepted:
21
02
2022
entrez:
7
4
2022
pubmed:
8
4
2022
medline:
9
4
2022
Statut:
epublish
Résumé
In order to construct a prediction model of sports economic operation indicators, this paper combines deep learning and ensemble learning algorithms to integrate and improve the algorithms and analyzes the principles of the LightGBM ensemble learning model and the hyperparameters of the model. Moreover, this paper obtains appropriate intelligent algorithms according to the data analysis requirements of sports economic operation. The break-even analysis method of sports event operation is to find the critical point of the program's profit and loss by analyzing the relationship between the operating cost and profit of the sports event. In addition, this paper uses deep learning and ensemble learning to comprehensively evaluate sports events, constructs a summary evaluation structure of sports items, and evaluates the model in this paper combined with experimental research. The test results verify the reliability of the model in this paper.
Identifiants
pubmed: 35387248
doi: 10.1155/2022/9085349
pmc: PMC8979742
doi:
Types de publication
Journal Article
Retracted Publication
Langues
eng
Sous-ensembles de citation
IM
Pagination
9085349Commentaires et corrections
Type : RetractionIn
Informations de copyright
Copyright © 2022 Chuangjian Yang and Junmeng Chen.
Déclaration de conflit d'intérêts
The authors declare no competing interests.
Références
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pubmed: 26176200
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pubmed: 28390863
Waste Manag. 2019 Feb 1;84:129-140
pubmed: 30691884