Neuro-fuzzy analytics in athlete development (NueroFATH): a machine learning approach

Abstract Athletes represent the apex of physical capacity filling in a social picture of performance and build. In light of the fundamental contrasts in athletic capacities required for different games, each game demands an alternate body type standard. Because of the decent variety of these body ty...
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Autor*in:	Rathore, Heena [verfasserIn] Mohamed, Amr Guizani, Mohsen Rathore, Shailendra

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Neuro-fuzzy analytics Machine learning Multilayer perceptron model Fuzzy c-means Athletes

Anmerkung:	© The Author(s), under exclusive licence to Springer-Verlag London Ltd. part of Springer Nature 2021

Übergeordnetes Werk:	Enthalten in: Neural computing & applications - London : Springer, 1993, 35(2021), 33 vom: 09. Feb., Seite 23697-23710
Übergeordnetes Werk:	volume:35 ; year:2021 ; number:33 ; day:09 ; month:02 ; pages:23697-23710

Links:	Volltext

DOI / URN:	10.1007/s00521-021-05704-5

Katalog-ID:	SPR053457021

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allfields	10.1007/s00521-021-05704-5 doi (DE-627)SPR053457021 (SPR)s00521-021-05704-5-e DE-627 ger DE-627 rakwb eng Rathore, Heena verfasserin (orcid)0000-0002-9403-8071 aut Neuro-fuzzy analytics in athlete development (NueroFATH): a machine learning approach 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd. part of Springer Nature 2021 Abstract Athletes represent the apex of physical capacity filling in a social picture of performance and build. In light of the fundamental contrasts in athletic capacities required for different games, each game demands an alternate body type standard. Because of the decent variety of these body types, each can have an altogether different body standard. Nowadays, a large number of athletes participate in assessments and a large number of human hours are spent on playing out these assessments every year. These assessments are performed to check the physical strength of athletes and evaluate them for different games. This paper presents a machine learning approach to the physical assessment of athletes known as NueroFATH. The proposed NueroFATH approach relies on neuro-fuzzy analytics that involves the deployment of neural networks and fuzzy c-means techniques to predict the athletes for the potential of winning medals. This can be achieved using athletes’ physical assessment parameters. The goal of this study is not only to identify the athletes based on which group they fall into (gold/silver/bronze), but also to understand which physical characteristic is important to identify them and categorize them in a medal group. It was determined that features, namely height, body mass, body mass index, 40 m and vertical jump are the most important for achieving 98.40% accuracy for athletes to classify them in the gold category when they are in the bronze category. Unsupervised learning showed that features, namely body mass, body mass index, vertical jump, med ball, 40 m, peak oxygen content, peak height velocity have the highest variability. We can achieve upto 97.06% accuracy when features, i.e., body mass, body mass index, vertical jump, med ball, 40 m, peak oxygen content, peak height velocity were used. Neuro-fuzzy analytics (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Multilayer perceptron model (dpeaa)DE-He213 Fuzzy c-means (dpeaa)DE-He213 Athletes (dpeaa)DE-He213 Mohamed, Amr aut Guizani, Mohsen aut Rathore, Shailendra aut Enthalten in Neural computing & applications London : Springer, 1993 35(2021), 33 vom: 09. Feb., Seite 23697-23710 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:35 year:2021 number:33 day:09 month:02 pages:23697-23710 https://dx.doi.org/10.1007/s00521-021-05704-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 35 2021 33 09 02 23697-23710
spelling	10.1007/s00521-021-05704-5 doi (DE-627)SPR053457021 (SPR)s00521-021-05704-5-e DE-627 ger DE-627 rakwb eng Rathore, Heena verfasserin (orcid)0000-0002-9403-8071 aut Neuro-fuzzy analytics in athlete development (NueroFATH): a machine learning approach 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd. part of Springer Nature 2021 Abstract Athletes represent the apex of physical capacity filling in a social picture of performance and build. In light of the fundamental contrasts in athletic capacities required for different games, each game demands an alternate body type standard. Because of the decent variety of these body types, each can have an altogether different body standard. Nowadays, a large number of athletes participate in assessments and a large number of human hours are spent on playing out these assessments every year. These assessments are performed to check the physical strength of athletes and evaluate them for different games. This paper presents a machine learning approach to the physical assessment of athletes known as NueroFATH. The proposed NueroFATH approach relies on neuro-fuzzy analytics that involves the deployment of neural networks and fuzzy c-means techniques to predict the athletes for the potential of winning medals. This can be achieved using athletes’ physical assessment parameters. The goal of this study is not only to identify the athletes based on which group they fall into (gold/silver/bronze), but also to understand which physical characteristic is important to identify them and categorize them in a medal group. It was determined that features, namely height, body mass, body mass index, 40 m and vertical jump are the most important for achieving 98.40% accuracy for athletes to classify them in the gold category when they are in the bronze category. Unsupervised learning showed that features, namely body mass, body mass index, vertical jump, med ball, 40 m, peak oxygen content, peak height velocity have the highest variability. We can achieve upto 97.06% accuracy when features, i.e., body mass, body mass index, vertical jump, med ball, 40 m, peak oxygen content, peak height velocity were used. Neuro-fuzzy analytics (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Multilayer perceptron model (dpeaa)DE-He213 Fuzzy c-means (dpeaa)DE-He213 Athletes (dpeaa)DE-He213 Mohamed, Amr aut Guizani, Mohsen aut Rathore, Shailendra aut Enthalten in Neural computing & applications London : Springer, 1993 35(2021), 33 vom: 09. Feb., Seite 23697-23710 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:35 year:2021 number:33 day:09 month:02 pages:23697-23710 https://dx.doi.org/10.1007/s00521-021-05704-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 35 2021 33 09 02 23697-23710
allfields_unstemmed	10.1007/s00521-021-05704-5 doi (DE-627)SPR053457021 (SPR)s00521-021-05704-5-e DE-627 ger DE-627 rakwb eng Rathore, Heena verfasserin (orcid)0000-0002-9403-8071 aut Neuro-fuzzy analytics in athlete development (NueroFATH): a machine learning approach 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd. part of Springer Nature 2021 Abstract Athletes represent the apex of physical capacity filling in a social picture of performance and build. In light of the fundamental contrasts in athletic capacities required for different games, each game demands an alternate body type standard. Because of the decent variety of these body types, each can have an altogether different body standard. Nowadays, a large number of athletes participate in assessments and a large number of human hours are spent on playing out these assessments every year. These assessments are performed to check the physical strength of athletes and evaluate them for different games. This paper presents a machine learning approach to the physical assessment of athletes known as NueroFATH. The proposed NueroFATH approach relies on neuro-fuzzy analytics that involves the deployment of neural networks and fuzzy c-means techniques to predict the athletes for the potential of winning medals. This can be achieved using athletes’ physical assessment parameters. The goal of this study is not only to identify the athletes based on which group they fall into (gold/silver/bronze), but also to understand which physical characteristic is important to identify them and categorize them in a medal group. It was determined that features, namely height, body mass, body mass index, 40 m and vertical jump are the most important for achieving 98.40% accuracy for athletes to classify them in the gold category when they are in the bronze category. Unsupervised learning showed that features, namely body mass, body mass index, vertical jump, med ball, 40 m, peak oxygen content, peak height velocity have the highest variability. We can achieve upto 97.06% accuracy when features, i.e., body mass, body mass index, vertical jump, med ball, 40 m, peak oxygen content, peak height velocity were used. Neuro-fuzzy analytics (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Multilayer perceptron model (dpeaa)DE-He213 Fuzzy c-means (dpeaa)DE-He213 Athletes (dpeaa)DE-He213 Mohamed, Amr aut Guizani, Mohsen aut Rathore, Shailendra aut Enthalten in Neural computing & applications London : Springer, 1993 35(2021), 33 vom: 09. Feb., Seite 23697-23710 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:35 year:2021 number:33 day:09 month:02 pages:23697-23710 https://dx.doi.org/10.1007/s00521-021-05704-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 35 2021 33 09 02 23697-23710
allfieldsGer	10.1007/s00521-021-05704-5 doi (DE-627)SPR053457021 (SPR)s00521-021-05704-5-e DE-627 ger DE-627 rakwb eng Rathore, Heena verfasserin (orcid)0000-0002-9403-8071 aut Neuro-fuzzy analytics in athlete development (NueroFATH): a machine learning approach 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd. part of Springer Nature 2021 Abstract Athletes represent the apex of physical capacity filling in a social picture of performance and build. In light of the fundamental contrasts in athletic capacities required for different games, each game demands an alternate body type standard. Because of the decent variety of these body types, each can have an altogether different body standard. Nowadays, a large number of athletes participate in assessments and a large number of human hours are spent on playing out these assessments every year. These assessments are performed to check the physical strength of athletes and evaluate them for different games. This paper presents a machine learning approach to the physical assessment of athletes known as NueroFATH. The proposed NueroFATH approach relies on neuro-fuzzy analytics that involves the deployment of neural networks and fuzzy c-means techniques to predict the athletes for the potential of winning medals. This can be achieved using athletes’ physical assessment parameters. The goal of this study is not only to identify the athletes based on which group they fall into (gold/silver/bronze), but also to understand which physical characteristic is important to identify them and categorize them in a medal group. It was determined that features, namely height, body mass, body mass index, 40 m and vertical jump are the most important for achieving 98.40% accuracy for athletes to classify them in the gold category when they are in the bronze category. Unsupervised learning showed that features, namely body mass, body mass index, vertical jump, med ball, 40 m, peak oxygen content, peak height velocity have the highest variability. We can achieve upto 97.06% accuracy when features, i.e., body mass, body mass index, vertical jump, med ball, 40 m, peak oxygen content, peak height velocity were used. Neuro-fuzzy analytics (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Multilayer perceptron model (dpeaa)DE-He213 Fuzzy c-means (dpeaa)DE-He213 Athletes (dpeaa)DE-He213 Mohamed, Amr aut Guizani, Mohsen aut Rathore, Shailendra aut Enthalten in Neural computing & applications London : Springer, 1993 35(2021), 33 vom: 09. Feb., Seite 23697-23710 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:35 year:2021 number:33 day:09 month:02 pages:23697-23710 https://dx.doi.org/10.1007/s00521-021-05704-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 35 2021 33 09 02 23697-23710
allfieldsSound	10.1007/s00521-021-05704-5 doi (DE-627)SPR053457021 (SPR)s00521-021-05704-5-e DE-627 ger DE-627 rakwb eng Rathore, Heena verfasserin (orcid)0000-0002-9403-8071 aut Neuro-fuzzy analytics in athlete development (NueroFATH): a machine learning approach 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd. part of Springer Nature 2021 Abstract Athletes represent the apex of physical capacity filling in a social picture of performance and build. In light of the fundamental contrasts in athletic capacities required for different games, each game demands an alternate body type standard. Because of the decent variety of these body types, each can have an altogether different body standard. Nowadays, a large number of athletes participate in assessments and a large number of human hours are spent on playing out these assessments every year. These assessments are performed to check the physical strength of athletes and evaluate them for different games. This paper presents a machine learning approach to the physical assessment of athletes known as NueroFATH. The proposed NueroFATH approach relies on neuro-fuzzy analytics that involves the deployment of neural networks and fuzzy c-means techniques to predict the athletes for the potential of winning medals. This can be achieved using athletes’ physical assessment parameters. The goal of this study is not only to identify the athletes based on which group they fall into (gold/silver/bronze), but also to understand which physical characteristic is important to identify them and categorize them in a medal group. It was determined that features, namely height, body mass, body mass index, 40 m and vertical jump are the most important for achieving 98.40% accuracy for athletes to classify them in the gold category when they are in the bronze category. Unsupervised learning showed that features, namely body mass, body mass index, vertical jump, med ball, 40 m, peak oxygen content, peak height velocity have the highest variability. We can achieve upto 97.06% accuracy when features, i.e., body mass, body mass index, vertical jump, med ball, 40 m, peak oxygen content, peak height velocity were used. Neuro-fuzzy analytics (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Multilayer perceptron model (dpeaa)DE-He213 Fuzzy c-means (dpeaa)DE-He213 Athletes (dpeaa)DE-He213 Mohamed, Amr aut Guizani, Mohsen aut Rathore, Shailendra aut Enthalten in Neural computing & applications London : Springer, 1993 35(2021), 33 vom: 09. Feb., Seite 23697-23710 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:35 year:2021 number:33 day:09 month:02 pages:23697-23710 https://dx.doi.org/10.1007/s00521-021-05704-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 35 2021 33 09 02 23697-23710
language	English
source	Enthalten in Neural computing & applications 35(2021), 33 vom: 09. Feb., Seite 23697-23710 volume:35 year:2021 number:33 day:09 month:02 pages:23697-23710
sourceStr	Enthalten in Neural computing & applications 35(2021), 33 vom: 09. Feb., Seite 23697-23710 volume:35 year:2021 number:33 day:09 month:02 pages:23697-23710
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Neuro-fuzzy analytics Machine learning Multilayer perceptron model Fuzzy c-means Athletes
isfreeaccess_bool	false
container_title	Neural computing & applications
authorswithroles_txt_mv	Rathore, Heena @@aut@@ Mohamed, Amr @@aut@@ Guizani, Mohsen @@aut@@ Rathore, Shailendra @@aut@@
publishDateDaySort_date	2021-02-09T00:00:00Z
hierarchy_top_id	271595574
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language_de	englisch
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author	Rathore, Heena
spellingShingle	Rathore, Heena misc Neuro-fuzzy analytics misc Machine learning misc Multilayer perceptron model misc Fuzzy c-means misc Athletes Neuro-fuzzy analytics in athlete development (NueroFATH): a machine learning approach
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topic_title	Neuro-fuzzy analytics in athlete development (NueroFATH): a machine learning approach Neuro-fuzzy analytics (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Multilayer perceptron model (dpeaa)DE-He213 Fuzzy c-means (dpeaa)DE-He213 Athletes (dpeaa)DE-He213
topic	misc Neuro-fuzzy analytics misc Machine learning misc Multilayer perceptron model misc Fuzzy c-means misc Athletes
topic_unstemmed	misc Neuro-fuzzy analytics misc Machine learning misc Multilayer perceptron model misc Fuzzy c-means misc Athletes
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title	Neuro-fuzzy analytics in athlete development (NueroFATH): a machine learning approach
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title_full	Neuro-fuzzy analytics in athlete development (NueroFATH): a machine learning approach
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title_sort	neuro-fuzzy analytics in athlete development (nuerofath): a machine learning approach
title_auth	Neuro-fuzzy analytics in athlete development (NueroFATH): a machine learning approach
abstract	Abstract Athletes represent the apex of physical capacity filling in a social picture of performance and build. In light of the fundamental contrasts in athletic capacities required for different games, each game demands an alternate body type standard. Because of the decent variety of these body types, each can have an altogether different body standard. Nowadays, a large number of athletes participate in assessments and a large number of human hours are spent on playing out these assessments every year. These assessments are performed to check the physical strength of athletes and evaluate them for different games. This paper presents a machine learning approach to the physical assessment of athletes known as NueroFATH. The proposed NueroFATH approach relies on neuro-fuzzy analytics that involves the deployment of neural networks and fuzzy c-means techniques to predict the athletes for the potential of winning medals. This can be achieved using athletes’ physical assessment parameters. The goal of this study is not only to identify the athletes based on which group they fall into (gold/silver/bronze), but also to understand which physical characteristic is important to identify them and categorize them in a medal group. It was determined that features, namely height, body mass, body mass index, 40 m and vertical jump are the most important for achieving 98.40% accuracy for athletes to classify them in the gold category when they are in the bronze category. Unsupervised learning showed that features, namely body mass, body mass index, vertical jump, med ball, 40 m, peak oxygen content, peak height velocity have the highest variability. We can achieve upto 97.06% accuracy when features, i.e., body mass, body mass index, vertical jump, med ball, 40 m, peak oxygen content, peak height velocity were used. © The Author(s), under exclusive licence to Springer-Verlag London Ltd. part of Springer Nature 2021
abstractGer	Abstract Athletes represent the apex of physical capacity filling in a social picture of performance and build. In light of the fundamental contrasts in athletic capacities required for different games, each game demands an alternate body type standard. Because of the decent variety of these body types, each can have an altogether different body standard. Nowadays, a large number of athletes participate in assessments and a large number of human hours are spent on playing out these assessments every year. These assessments are performed to check the physical strength of athletes and evaluate them for different games. This paper presents a machine learning approach to the physical assessment of athletes known as NueroFATH. The proposed NueroFATH approach relies on neuro-fuzzy analytics that involves the deployment of neural networks and fuzzy c-means techniques to predict the athletes for the potential of winning medals. This can be achieved using athletes’ physical assessment parameters. The goal of this study is not only to identify the athletes based on which group they fall into (gold/silver/bronze), but also to understand which physical characteristic is important to identify them and categorize them in a medal group. It was determined that features, namely height, body mass, body mass index, 40 m and vertical jump are the most important for achieving 98.40% accuracy for athletes to classify them in the gold category when they are in the bronze category. Unsupervised learning showed that features, namely body mass, body mass index, vertical jump, med ball, 40 m, peak oxygen content, peak height velocity have the highest variability. We can achieve upto 97.06% accuracy when features, i.e., body mass, body mass index, vertical jump, med ball, 40 m, peak oxygen content, peak height velocity were used. © The Author(s), under exclusive licence to Springer-Verlag London Ltd. part of Springer Nature 2021
abstract_unstemmed	Abstract Athletes represent the apex of physical capacity filling in a social picture of performance and build. In light of the fundamental contrasts in athletic capacities required for different games, each game demands an alternate body type standard. Because of the decent variety of these body types, each can have an altogether different body standard. Nowadays, a large number of athletes participate in assessments and a large number of human hours are spent on playing out these assessments every year. These assessments are performed to check the physical strength of athletes and evaluate them for different games. This paper presents a machine learning approach to the physical assessment of athletes known as NueroFATH. The proposed NueroFATH approach relies on neuro-fuzzy analytics that involves the deployment of neural networks and fuzzy c-means techniques to predict the athletes for the potential of winning medals. This can be achieved using athletes’ physical assessment parameters. The goal of this study is not only to identify the athletes based on which group they fall into (gold/silver/bronze), but also to understand which physical characteristic is important to identify them and categorize them in a medal group. It was determined that features, namely height, body mass, body mass index, 40 m and vertical jump are the most important for achieving 98.40% accuracy for athletes to classify them in the gold category when they are in the bronze category. Unsupervised learning showed that features, namely body mass, body mass index, vertical jump, med ball, 40 m, peak oxygen content, peak height velocity have the highest variability. We can achieve upto 97.06% accuracy when features, i.e., body mass, body mass index, vertical jump, med ball, 40 m, peak oxygen content, peak height velocity were used. © The Author(s), under exclusive licence to Springer-Verlag London Ltd. part of Springer Nature 2021
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container_issue	33
title_short	Neuro-fuzzy analytics in athlete development (NueroFATH): a machine learning approach
url	https://dx.doi.org/10.1007/s00521-021-05704-5
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author2	Mohamed, Amr Guizani, Mohsen Rathore, Shailendra
author2Str	Mohamed, Amr Guizani, Mohsen Rathore, Shailendra
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doi_str	10.1007/s00521-021-05704-5
up_date	2024-07-03T19:40:00.950Z
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Neuro-fuzzy analytics in athlete development (NueroFATH): a machine learning approach

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