Predicting the Sporting Achievement in the Pole Vault for Men Using Artificial Neural Networks
DOI:
https://doi.org/10.61841/tfxrhg56Keywords:
Artificial Neural Network, Iraqi Sport Sector, Predicating, Man, Pole VaultAbstract
The physical sports sector in Iraq suffers from the problem of achieving sports achievements in individual and team games in various Asian and international competitions for many reasons, including the lack of exploitation of modern, accurate, and flexible technologies and means, especially in the field of information technology, especially the technology of artificial neural networks. The main goal of this study is to build an intelligent mathematical model to predict sport achievement in pole vaulting for men. the methodology of the research included the use of five variables as inputs to the neural network, which are average of Speed (m/sec in Before distance 0.5 meters latest and distance 0.5 meters latest), The maximum speed achieved in the last 5 meters from the total approach distance of 30 meters. The ratio of the conversion coefficient of horizontal velocity to vertical velocity, The ratio of the conversion coefficient of horizontal velocity to vertical velocity, The height of the fist is over the full length of the pole's stick, and these are considered independent variables, while the dependent variable was the prediction of achievement (final height achieved by the jumper) as an output. The neural network architecture was represented by three layers: the first layer is the input layer with the five variables, one layer is hidden and contains one node, and the last layer is the output layer that represents the outcome of the sport achievement prediction of male weight jumping. The momentum term and learning rate were chosen as 0.95 and 0.4, respectively, and the transfer function in the hidden layer was the sigmoid function, and in the last layer was the sigmoid function. The historical data used in this model represent the Olympic achievements of a number of world champions. The results of this study were that the artificial neural network has the ability to predict sport achievement to determine the height of the jump of the pole player with a degree of accuracy of 90.10% and a correlation coefficient of 95.60%.
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