Application of neural networks for the classification of gender from kick force profile - A small scale study

Lapková, Dora; Pluháček, Michal; Komínková Oplatková, Zuzana; Šenkeřík, Roman; Adámek, Milan

dc.title	Application of neural networks for the classification of gender from kick force profile - A small scale study	en
dc.contributor.author	Lapková, Dora
dc.contributor.author	Pluháček, Michal
dc.contributor.author	Komínková Oplatková, Zuzana
dc.contributor.author	Šenkeřík, Roman
dc.contributor.author	Adámek, Milan
dc.relation.ispartof	Proceedings of the Fifth International Conference on Innovations in Bio-inspired Computing and Applications (IBICA 2014)
dc.identifier.issn	2194-5357 Scopus Sources, Sherpa/RoMEO, JCR
dc.identifier.isbn	978-3-319-08155-7
dc.identifier.isbn	978-3-319-08156-4
dc.date.issued	2014
utb.relation.volume	303
dc.citation.spage	429
dc.citation.epage	438
dc.event.title	5th International Conference on Innovations in Bio-Inspired Computing and Applications (IBICA)
dc.event.location	Ostrava
utb.event.state-en	Czech Republic
utb.event.state-cs	Česká republika
dc.event.sdate	2014-06-23
dc.event.edate	2014-06-25
dc.type	conferenceObject
dc.language.iso	en
dc.publisher	Springer-Verlag Berlin
dc.identifier.doi	10.1007/978-3-319-08156-4_43
dc.relation.uri	https://link.springer.com/chapter/10.1007/978-3-319-08156-4_43
dc.subject	Professional defense	en
dc.subject	Kick techniques	en
dc.subject	Direct kick	en
dc.subject	Round kick gender	en
dc.subject	classification	en
dc.subject	neural network	en
dc.subject	DCT	en
dc.description.abstract	The possibility of using artificial neural network for person gender classification based on kick force profile is investigated in this paper. The input data are transformed using discrete cosine transformation for easier classification. Extensive tuning is performed on the proposed artificial neural network to obtain better results. This preliminary study sums up foundations for future large-scale studies.	en
utb.faculty	Faculty of Applied Informatics
dc.identifier.uri	http://hdl.handle.net/10563/1003922
utb.identifier.obdid	43871694
utb.identifier.scopus	2-s2.0-84906679272
utb.identifier.wok	000342841800043
utb.source	d-wok
dc.date.accessioned	2014-11-25T08:53:31Z
dc.date.available	2014-11-25T08:53:31Z
utb.contributor.internalauthor	Lapková, Dora
utb.contributor.internalauthor	Pluháček, Michal
utb.contributor.internalauthor	Komínková Oplatková, Zuzana
utb.contributor.internalauthor	Šenkeřík, Roman
utb.contributor.internalauthor	Adámek, Milan
utb.fulltext.affiliation	Dora Lapkova, Michal Pluhacek, Zuzana Komínková Oplatková, Roman Senkerik, and Milan Adamek Tomas Bata University in Zlin, Faculty of Applied Informatics, Nam T.G. Masaryka 5555, 760 01 Zlin, Czech Republic {dlapkova,pluhacek,senkerik,oplatkova,adamek}@fai.utb.cz
utb.fulltext.dates	-
utb.fulltext.sponsorship	This work was supported by the Internal Grant Agency at TBU in Zlín, project No. IGA/FAI/2014/036, IGA/FAI/2014/10 and by the European Regional Development Fund under the project CEBIA-Tech No. CZ.1.05/2.1.00/03.0089 and was supported by the Bio-Inspired Methods: research, development and knowledge transfer project, reg. no. CZ.1.07/2.3.00/20.0073 funded by Operational Programme Education for Competitiveness, co- financed by ESF and state budget of the Czech Republic.
utb.fulltext.projects	IGA/FAI/2014/036
utb.fulltext.projects	IGA/FAI/2014/10
utb.fulltext.projects	CZ.1.05/2.1.00/03.0089
utb.fulltext.projects	CZ.1.07/2.3.00/20.0073