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001-es BibID:	BIBFORM076372
035-os BibID:	(WoS)000455740800003 (Scopus)85057815675
Első szerző:	Sütő József (programtervező informatikus)
Cím:	Efficiency investigation from shallow to deep neural network techniques in human activity recognition / Jozsef Suto, Stefan Oniga
Dátum:	2019
ISSN:	1389-0417
Megjegyzések:	In the last years, several researchers measured different recognition rates with different artificial neural network (ANN) techniques on public data sets in the human activity recognition (HAR) problem. However an overall investigation does not exist in the literature and the efficiency of complex and deeper ANNs over shallow networks is not clear. The purpose of this paper is to investigate the recognition rate and time requirement of different kinds of ANN approaches in HAR. This work examines the performance of shallow ANN architectures with different hyper-parameters, ANN ensembles, binary ANN classifier groups, and convolutional neural networks on two public databases. Although the popularity of binary classifiers, classifier ensembles and deep learning have been significantly increasing, this study shows that shallow ANNs with appropriate hyper-parameters in combination with extracted features can reach similar or higher recognition rate in less time than other artificial neural network methods in HAR. With a well-tuned ANN we outperformed all previous results on two public databases. Consequently, instead of the more complex ANN techniques, the usage of simple ANN with two or three layers can be an appropriate choice for activity recognition.
Tárgyszavak:	Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk Artificial neural networks Binary classifiers Convolutional networks Ensembles Feature extraction Human activity recognition
Megjelenés:	Cognitive Systems Research. - 54 (2019), p. 37-49. -
További szerzők:	Oniga István László (1960-) (villamosmérnök)
Pályázati támogatás:	EFOP-3.6.3-VEKOP-16-2017-00002 EFOP
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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