Research on Information Processing System of Sports Combination Training Model Based on Machine Learning and Neural Network
DOI:
https://doi.org/10.12775/QS.2024.33.57176Keywords
Data Slicing, Information Processing, Neural Network, Sports Training IntroductionAbstract
Information processing systems in sports and training applications are backboned by artificial intelligence for non-human intervening and accurate analysis. The fitness, performance, etc. outcomes are delivered by the system through learning implications over the different inputs. However, the recommendation/ prediction outcomes are down-surged in analyzing similar information due to learning complexity and non-adaptable outcome. Therefore, the problem is resolved by fragmenting and processing the information using a similarity measure. Therefore, this method is named as Sliced-Information Processing with Analogous Learning (SIP-AL). In this method, a neural network is used for deciding the processing feature for better accuracy. In the contrary case of down-surges, the information slicing based on an analogous point is performed. This prevents the continuity between redundant and continuous data preventing errors.
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Copyright (c) 2024 Songmao Yu, Lei Wang, Tianwei Li
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