| _version_ |
1860799922658344960
|
| building |
INTELEK Repository
|
| collection |
Online Access
|
| collectionurl |
https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072
|
| date |
2018-05-14 04:21:57
|
| format |
Restricted Document
|
| id |
7932
|
| institution |
UniSZA
|
| originalfilename |
3724-01-FH05-FSSG-18-13762.pdf
|
| person |
Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML
like Gecko) Chrome/65.0.3325.181 Safari/537.36
|
| recordtype |
oai_dc
|
| resourceurl |
https://intelek.unisza.edu.my/intelek/pages/view.php?ref=7932
|
| spelling |
7932 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=7932 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 04:21:57 3724-01-FH05-FSSG-18-13762.pdf UniSZA Private Access Classification of high performance archers by means of bio-physiological performance variables via k-nearest neighbour classification model 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. © 2018, Springer Nature Singapore Pte Ltd. Pleiades Publishing Pleiades Publishing 377-384 Lecture Notes in Mechanical Engineering
|
| spellingShingle |
Classification of high performance archers by means of bio-physiological performance variables via k-nearest neighbour classification model
|
| summary |
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. © 2018, Springer Nature Singapore Pte Ltd.
|
| title |
Classification of high performance archers by means of bio-physiological performance variables via k-nearest neighbour classification model
|
| title_full |
Classification of high performance archers by means of bio-physiological performance variables via k-nearest neighbour classification model
|
| title_fullStr |
Classification of high performance archers by means of bio-physiological performance variables via k-nearest neighbour classification model
|
| title_full_unstemmed |
Classification of high performance archers by means of bio-physiological performance variables via k-nearest neighbour classification model
|
| title_short |
Classification of high performance archers by means of bio-physiological performance variables via k-nearest neighbour classification model
|
| title_sort |
classification of high performance archers by means of bio-physiological performance variables via k-nearest neighbour classification model
|