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1860796910032388096
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INTELEK Repository
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Online Access
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https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072
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| date |
2021-03-22 00:26:09
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Restricted Document
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| id |
10648
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UniSZA
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4715-01-FH05-ESERI-21-51617.pdf
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Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML
like Gecko) Chrome/88.0.4324.190 Safari/537.36
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oai_dc
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| resourceurl |
https://intelek.unisza.edu.my/intelek/pages/view.php?ref=10648
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| spelling |
10648 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=10648 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072 Restricted Document Book Chapter application/pdf 3 1.6 Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML like Gecko) Chrome/88.0.4324.190 Safari/537.36 Skia/PDF m88 2021-03-22 00:26:09 4715-01-FH05-ESERI-21-51617.pdf UniSZA Private Access Identifying talent in sepak takraw via anthropometry indexes This chapter evaluates the importance of different anthropometric indexes towards the categorisation of the ability of sepak takraw players. To discriminate between high-performance players (HPP), medium performance players (MPP) and low performance players (LPP), the Louvain clustering algorithm was employed. Different SVM models were also developed by varying the hyperparameters of the models. It is evident from the present investigation that anthropometric indexes, particularly standing height, sitting height, leg length, waist circumference, thigh circumference, calf circumference and four-site skinfold measurements evaluated do affect performance in sepak takraw players. It was also demonstrated that the best polynomial-based SVM architecture is capable of discriminating the players with an average classification accuracy of 96% on the validation and test dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020. Springer Springer 29-39 SpringerBriefs in Applied Sciences and Technology
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| spellingShingle |
Identifying talent in sepak takraw via anthropometry indexes
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| summary |
This chapter evaluates the importance of different anthropometric indexes towards the categorisation of the ability of sepak takraw players. To discriminate between high-performance players (HPP), medium performance players (MPP) and low performance players (LPP), the Louvain clustering algorithm was employed. Different SVM models were also developed by varying the hyperparameters of the models. It is evident from the present investigation that anthropometric indexes, particularly standing height, sitting height, leg length, waist circumference, thigh circumference, calf circumference and four-site skinfold measurements evaluated do affect performance in sepak takraw players. It was also demonstrated that the best polynomial-based SVM architecture is capable of discriminating the players with an average classification accuracy of 96% on the validation and test dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020.
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| title |
Identifying talent in sepak takraw via anthropometry indexes
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| title_full |
Identifying talent in sepak takraw via anthropometry indexes
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| title_fullStr |
Identifying talent in sepak takraw via anthropometry indexes
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| title_full_unstemmed |
Identifying talent in sepak takraw via anthropometry indexes
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| title_short |
Identifying talent in sepak takraw via anthropometry indexes
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| title_sort |
identifying talent in sepak takraw via anthropometry indexes
|