{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T07:50:01Z","timestamp":1767167401832,"version":"build-2238731810"},"reference-count":21,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,4,9]],"date-time":"2021-04-09T00:00:00Z","timestamp":1617926400000},"content-version":"vor","delay-in-days":98,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>\n                    Sports injury is a common problem in athletes\n                    <jats:sup>\u2019<\/jats:sup>\n                    training. The sports injury assessment model is a physical method to determine the sports injury attributes of specific parts by predicting and evaluating the risk of sports injury. In this paper, we use a neural network to realize big data analysis of sports injury data. Big data network is a method of capturing Internet information by means of cloud computing, which is usually used in the construction of Wan and LAN. This paper analyzes the source of sports risk and the main injury factors, designs the sports injury estimation model based on big data analysis, establishes a new assessment model based on RBF neural network, and builds the big data network environment required for the model operation by improving the topological structure, combining big data and deep neural network. In the built environment, the risk assessment of sports injury can be completed by determining the risk source and identifying the risk factors. The realization of the model can be constrained by the uncertainty conditions so that it can achieve a good operation state.\n                  <\/jats:p>","DOI":"10.1155\/2021\/6622367","type":"journal-article","created":{"date-parts":[[2021,4,9]],"date-time":"2021-04-09T19:35:10Z","timestamp":1617996910000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["[Retracted] Early Warning Model of Sports Injury Based on RBF Neural Network Algorithm"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2113-226X","authenticated-orcid":false,"given":"Fuxing","family":"He","sequence":"first","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,4,9]]},"reference":[{"key":"e_1_2_8_1_2","first-page":"1872","article-title":"A modeling approach for early-warning of water bloom risk in urban lake based on neural network","volume":"37","author":"Zheng J. 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