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Talent identification of potential archers through fitness and motor ability performance variables by means of artificial neural network

Zahari, Taha and Musa, Rabiu Muazu and Anwar, P. P. Abdul Majeed and Mohamad Razali, Abdullah and M. H. A., Hassan (2018) Talent identification of potential archers through fitness and motor ability performance variables by means of artificial neural network. In: Intelligent Manufacturing & Mechatronics: Proceedings of Symposium, 29 January 2018, Pekan, Pahang, Malaysia. Lecture Notes in Mechanical Engineering . Springer Singapore, Singapore, pp. 371-376. ISBN 9789811087875

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Abstract

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.

Item Type: Book Section
Uncontrolled Keywords: Archery; Machine Learning; Classification; Artificial Neural Network
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division: Faculty of Manufacturing Engineering
Depositing User: Dr. Mohd Hasnun Arif Hassan
Date Deposited: 28 May 2018 06:52
Last Modified: 07 Aug 2018 04:25
URI: http://umpir.ump.edu.my/id/eprint/21161
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