| _version_ |
1860799922451775488
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| building |
INTELEK Repository
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| collection |
Online Access
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https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072
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| date |
2018-05-14 04:19:52
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| format |
Restricted Document
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| id |
7931
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| institution |
UniSZA
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| originalfilename |
3723-01-FH05-FSSG-18-13761.pdf
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Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML
like Gecko) Chrome/65.0.3325.181 Safari/537.36
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oai_dc
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https://intelek.unisza.edu.my/intelek/pages/view.php?ref=7931
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| spelling |
7931 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=7931 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072 Restricted Document Book Chapter application/pdf 2 1.6 Adobe Acrobat Pro DC 20 Paper Capture Plug-in Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML like Gecko) Chrome/65.0.3325.181 Safari/537.36 2018-05-14 04:19:52 3723-01-FH05-FSSG-18-13761.pdf UniSZA Private Access Talent identification of potential archers through fitness and motor ability performance variables by means of artificial neural network The utilisation of artificial intelligence for prediction and classification in the sport of archery is still in its infancy. The present study classified and predicted high and low potential archers from a set of fitness and motor ability variables trained on artificial neural network (ANN). 50 youth archers with the mean age and standard deviation of (17.00 ± 0.56) drawn from various archery programmes completed a one end archery shooting score test. Standard fitness and ability measurements of hand grip, vertical jump, standing broad jump, static balance, upper muscle strength and the core muscle were conducted. The cluster analysis was used to cluster the archers based on the performance variables tested to high performing archers (HPA) and low performing archers (LPA), respectively. ANN was used to train the measured performance variables. The five-fold cross-validation technique was utilised in the study. It was established that the ANN model is able to demonstrate a reasonably excellent classification on the evaluated indicators with a classification accuracy of 94% in classifying the HPA and the LPA. © 2018, Springer Nature Singapore Pte Ltd. Pleiades Publishing Pleiades Publishing 371-376 Lecture Notes in Mechanical Engineering
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| spellingShingle |
Talent identification of potential archers through fitness and motor ability performance variables by means of artificial neural network
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| summary |
The utilisation of artificial intelligence for prediction and classification in the sport of archery is still in its infancy. The present study classified and predicted high and low potential archers from a set of fitness and motor ability variables trained on artificial neural network (ANN). 50 youth archers with the mean age and standard deviation of (17.00 ± 0.56) drawn from various archery programmes completed a one end archery shooting score test. Standard fitness and ability measurements of hand grip, vertical jump, standing broad jump, static balance, upper muscle strength and the core muscle were conducted. The cluster analysis was used to cluster the archers based on the performance variables tested to high performing archers (HPA) and low performing archers (LPA), respectively. ANN was used to train the measured performance variables. The five-fold cross-validation technique was utilised in the study. It was established that the ANN model is able to demonstrate a reasonably excellent classification on the evaluated indicators with a classification accuracy of 94% in classifying the HPA and the LPA. © 2018, Springer Nature Singapore Pte Ltd.
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| title |
Talent identification of potential archers through fitness and motor ability performance variables by means of artificial neural network
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| title_full |
Talent identification of potential archers through fitness and motor ability performance variables by means of artificial neural network
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| title_fullStr |
Talent identification of potential archers through fitness and motor ability performance variables by means of artificial neural network
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| title_full_unstemmed |
Talent identification of potential archers through fitness and motor ability performance variables by means of artificial neural network
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| title_short |
Talent identification of potential archers through fitness and motor ability performance variables by means of artificial neural network
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| title_sort |
talent identification of potential archers through fitness and motor ability performance variables by means of artificial neural network
|