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Classification of high performance archers by means of bio-physiological performance variables via k-nearest neighbour classification model

Zahari, Taha and Musa, Rabiu Muazu and Anwar, P. P. Abdul Majeed and Mohamad Razali, Abdullah and Ahmad Fakhri, Ab. Nasir and M. H. A., Hassan (2018) Classification of high performance archers by means of bio-physiological performance variables via k-nearest neighbour classification model. In: Intelligent Manufacturing & Mechatronics: Proceedings of Symposium, 29 January 2018, Pekan, Pahang, Malaysia. Lecture Notes in Mechanical Engineering . Springer Singapore, Singapore, pp. 377-384. ISBN 9789811087875

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Abstract

The present study classified and predicted high and low potential archers from a set of bio-physiological variables trained via a machine learning technique namely k-Nearest Neighbour (k-NN). 50 youth archers drawn from various archery programmes completed a one end archery shooting score test. Bio-physiological measurements of systolic blood pressure, diastolic blood pressure, resting respiratory rate, resting heart rate and dietary intake were taken. Multiherachical agglomerative cluster analysis was used to cluster the archers based on the variables tested into low, medium and high potential archers. Three different k-NN models namely fine, medium and coarse were trained based on the measured variables. The five-fold cross-validation technique was utilised in the present investigation. It was shown from the present study, that the utilisation of k-NN is non-trivial in the classification of the performance of the archers.

Item Type: Book Section
Uncontrolled Keywords: Bio-physiological variables; Artificial intelligence; Classification; k-Nearest neighbour
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 05:32
Last Modified: 07 Aug 2018 04:23
URI: http://umpir.ump.edu.my/id/eprint/21164
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