The employment of Support Vector Machine to classify high and low performance archers based on bio-physiological variables

The employment of Support Vector Machine to classify high and low performance archers based on bio-physiological variables

The present study employs a machine learning algorithm namely support vector machine (SVM) to classify high and low potential archers from a collection of bio-physiological variables trained on different SVMs. 50 youth archers with the average age and standard deviation of (17.0 ±.056) gathered from...

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Main Authors: Zahari, Taha, Musa, Rabiu Muazu, Anwar, P. P. Abdul Majeed, Mohamad Razali, Abdullah, Muhammad Amirul, Abdullah, M. H. A., Hassan, Zubair, Khalil
Format: Conference or Workshop Item
Language:English
Published: IOP Publishing 2018
Subjects:
TS Manufactures
Online Access:http://umpir.ump.edu.my/id/eprint/21233/
http://umpir.ump.edu.my/id/eprint/21233/
http://umpir.ump.edu.my/id/eprint/21233/1/employment%20of%20Support%20Vector%20Machine%20to%20classify%20high-fkp-2018.pdf
id ump-21233
recordtype eprints
spelling ump-212332018-05-28T04:05:00Z http://umpir.ump.edu.my/id/eprint/21233/ The employment of Support Vector Machine to classify high and low performance archers based on bio-physiological variables Zahari, Taha Musa, Rabiu Muazu Anwar, P. P. Abdul Majeed Mohamad Razali, Abdullah Muhammad Amirul, Abdullah M. H. A., Hassan Zubair, Khalil TS Manufactures The present study employs a machine learning algorithm namely support vector machine (SVM) to classify high and low potential archers from a collection of bio-physiological variables trained on different SVMs. 50 youth archers with the average age and standard deviation of (17.0 ±.056) gathered from various archery programmes completed a one end shooting score test. The bio-physiological variables namely resting heart rate, resting respiratory rate, resting diastolic blood pressure, resting systolic blood pressure, as well as calories intake, were measured prior to their shooting tests. k-means cluster analysis was applied to cluster the archers based on their scores on variables assessed. SVM models i.e. linear, quadratic and cubic kernel functions, were trained on the aforementioned variables. The k-means clustered the archers into high (HPA) and low potential archers (LPA), respectively. It was demonstrated that the linear SVM exhibited good accuracy with a classification accuracy of 94% in comparison the other tested models. The findings of this investigation can be valuable to coaches and sports managers to recognise high potential athletes from the selected bio-physiological variables examined. IOP Publishing 2018 Conference or Workshop Item PeerReviewed application/pdf en cc_by http://umpir.ump.edu.my/id/eprint/21233/1/employment%20of%20Support%20Vector%20Machine%20to%20classify%20high-fkp-2018.pdf Zahari, Taha and Musa, Rabiu Muazu and Anwar, P. P. Abdul Majeed and Mohamad Razali, Abdullah and Muhammad Amirul, Abdullah and M. H. A., Hassan and Zubair, Khalil (2018) The employment of Support Vector Machine to classify high and low performance archers based on bio-physiological variables. In: IOP Conference Series: Materials Science and Engineering, International Conference on Innovative Technology, Engineering and Sciences 2018 (iCITES 2018), 1-2 March 2018 , Universiti Malaysia Pahang (UMP) Pekan Campus Library, Malaysia. pp. 1-7., 342 (012020). ISSN 1757-899X https://doi.org/10.1088/1757-899X/342/1/012020
repository_type Digital Repository
institution_category Local University
institution Universiti Malaysia Pahang
building UMP Institutional Repository
collection Online Access
language English
topic TS Manufactures
spellingShingle TS Manufactures
Zahari, Taha
Musa, Rabiu Muazu
Anwar, P. P. Abdul Majeed
Mohamad Razali, Abdullah
Muhammad Amirul, Abdullah
M. H. A., Hassan
Zubair, Khalil
The employment of Support Vector Machine to classify high and low performance archers based on bio-physiological variables
description The present study employs a machine learning algorithm namely support vector machine (SVM) to classify high and low potential archers from a collection of bio-physiological variables trained on different SVMs. 50 youth archers with the average age and standard deviation of (17.0 ±.056) gathered from various archery programmes completed a one end shooting score test. The bio-physiological variables namely resting heart rate, resting respiratory rate, resting diastolic blood pressure, resting systolic blood pressure, as well as calories intake, were measured prior to their shooting tests. k-means cluster analysis was applied to cluster the archers based on their scores on variables assessed. SVM models i.e. linear, quadratic and cubic kernel functions, were trained on the aforementioned variables. The k-means clustered the archers into high (HPA) and low potential archers (LPA), respectively. It was demonstrated that the linear SVM exhibited good accuracy with a classification accuracy of 94% in comparison the other tested models. The findings of this investigation can be valuable to coaches and sports managers to recognise high potential athletes from the selected bio-physiological variables examined.
format Conference or Workshop Item
author Zahari, Taha
Musa, Rabiu Muazu
Anwar, P. P. Abdul Majeed
Mohamad Razali, Abdullah
Muhammad Amirul, Abdullah
M. H. A., Hassan
Zubair, Khalil
author_facet Zahari, Taha
Musa, Rabiu Muazu
Anwar, P. P. Abdul Majeed
Mohamad Razali, Abdullah
Muhammad Amirul, Abdullah
M. H. A., Hassan
Zubair, Khalil
author_sort Zahari, Taha
title The employment of Support Vector Machine to classify high and low performance archers based on bio-physiological variables
title_short The employment of Support Vector Machine to classify high and low performance archers based on bio-physiological variables
title_full The employment of Support Vector Machine to classify high and low performance archers based on bio-physiological variables
title_fullStr The employment of Support Vector Machine to classify high and low performance archers based on bio-physiological variables
title_full_unstemmed The employment of Support Vector Machine to classify high and low performance archers based on bio-physiological variables
title_sort employment of support vector machine to classify high and low performance archers based on bio-physiological variables
publisher IOP Publishing
publishDate 2018
url http://umpir.ump.edu.my/id/eprint/21233/
http://umpir.ump.edu.my/id/eprint/21233/
http://umpir.ump.edu.my/id/eprint/21233/1/employment%20of%20Support%20Vector%20Machine%20to%20classify%20high-fkp-2018.pdf
first_indexed 2023-09-18T22:31:03Z
last_indexed 2023-09-18T22:31:03Z
_version_ 1777416288168050688

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