Research on Fusion of Dependent Evidence Based on Kendall Correlation Coefficient

Jie, Zhou; Su, Xiaoyan; Qian, Hong

doi:10.1145/3424978.3425140

Cited by 1 publication

(1 citation statement)

References 18 publications

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“…The correlation coefficient can be used to describe the relationship between variables, where the sign of the correlation coefficient indicates whether the direction of the relationship is positive or negative, and the magnitude of its value represents the strength of the relationship between the two variables, where the correlation coefficient is 0 when there is no correlation at all and 1 when there is a perfect correlation. There are various methods for calculating correlation coefficients in correlation analysis, including Pearson correlation coefficient [19], Spearman correlation coefficient [20], partial correlation coefficient [21], Kendall correlation coefficient [22], and so on. In this paper, the Pearson correlation coefficient is used to calculate the magnitude of correlation between the attributes.…”

Section: Data Correlation Analysismentioning

confidence: 99%

Construction of an efficient evaluation model for athletic athletes' competitive ability based on deep neural network algorithm

Niu¹

2023

3C Empresa

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This paper analyzes the data of this year's athletes' physical fitness test scores and manages the classification of different physical qualities of the farmer. In order to reduce the manual calculation and increase the prediction efficiency, as well as to unify the scoring criteria of previous years, this paper proposes a comprehensive performance prediction model based on deep neural network algorithm. First, principal component analysis is used to transform multiple attributes with strong correlation into independent attributes that are not related to each other, and to reduce the time and space for model training by eliminating redundancy. Second, a back propagation (BP) neural network algorithm is used to build a physical fitness test prediction model, and the model is applied to the test dataset for model performance evaluation. Finally, the physical fitness test model was applied to other years for comprehensive performance prediction, and the differences between the model prediction results and the actual teachers' manual calculation results were observed. The results showed very good prediction results for 2021, in which 92.95% of the data had an absolute value of error less than 2 and only 0.06% had an absolute value of error greater than 4, which indicated that the prediction performance of the model was extremely significant. At the same time, a new athletic athletic scoring standard was also developed based on the neural network BP model to provide a more scientific theoretical basis and guidance for the evaluation of athletic ability of athletes.

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Section: Data Correlation Analysismentioning

confidence: 99%

Construction of an efficient evaluation model for athletic athletes' competitive ability based on deep neural network algorithm

Niu¹

2023

3C Empresa

View full text Add to dashboard Cite

show abstract

Research on Fusion of Dependent Evidence Based on Kendall Correlation Coefficient

Cited by 1 publication

References 18 publications

Construction of an efficient evaluation model for athletic athletes' competitive ability based on deep neural network algorithm

Construction of an efficient evaluation model for athletic athletes' competitive ability based on deep neural network algorithm

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