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001-es BibID:	BIBFORM055708
Első szerző:	Nagy Zsolt (programtervező matematikus)
Cím:	Social media risks from forensic point of view / Zsolt Nagy
Dátum:	2012
ISSN:	2074-1294
Megjegyzések:	In the age of Facebook children are better computer or mobile phone users than their parents; we do not know any teenagers who do not have a social media profile, an email or an instant messenger account. It became the part of their everyday life; however they do not care and even do not know much about the other side of the social life. The Internet gives freedom for everyone, but in this big freedom we forget to teach our children and ourselves how to handle and protect sensitive information properly. In this article we focus on the risk of cyber crime against a single user who is not sufficiently careful to protect his or her information, we are going to show the way in which a forensic expert or even a cyber criminal can use internet activity reconstruction tools. We undertook this research using a real criminal investigation example, find out the kind of information that has been collected and stored about a user by a client computer and give some useful advices how to protect ourselves against cyber criminals.
Tárgyszavak:	Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban Cyber crime Forensic tools Internet activity web 2.0 Social media
Megjelenés:	International Journal of Computers and Communications. - 6 : 4 (2012), p. 245-253. -
Internet cím:	Szerző által megadott URL Intézményi repozitóriumban (DEA) tárolt változat
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001-es BibID:	BIBFORM067291
035-os BibID:	(WoS)000390587600009 (Scopus)85010507895
Első szerző:	Sütő József (programtervező informatikus)
Cím:	Feature analysis to human activity recognition / J. Suto, S. Oniga, P. Pop-Sitar
Dátum:	2017
ISSN:	1841-9836 1841-9844
Megjegyzések:	Human activity recognition (HAR) is one of those research areas whose importance and popularity have notably increased in recent years. HAR can be seen as a general machine learning problem which requires feature extraction and feature selection. In previous articles different features were extracted from time, frequency and wavelet domains for HAR but it is not clear that, how to determine the best feature combination which maximizes the performance of a machine learning algorithm. The aim of this paper is to present the most relevant feature extraction methods in HAR and to compare them with widely-used filter and wrapper feature selection algorithms. This work is an extended version of [1]a where we tested the efficiency of filter and wrapper feature selection algorithms in combination with artificial neural networks. In this paper the efficiency of selected features has been investigated on more machine learning algorithms (feed-forward artificial neural network, k-nearest neighbor and decision tree) where an independent database was the data source. The result demonstrates that machine learning in combination with feature selection can overcome other classification approaches.
Tárgyszavak:	Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk human activity recognition feature extraction feature selection machine learning
Megjelenés:	International Journal of Computers, Communications and Control. - 12 : 1 (2017), p. 116-130. -
További szerzők:	Oniga István László (1960-) (villamosmérnök) Pop-Sitar, Petrica
Internet cím:	Szerző által megadott URL Intézményi repozitóriumban (DEA) tárolt változat
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