Találati lista | Tudóstér

001-es BibID:	BIBFORM117947
035-os BibID:	(Scopus)85063425048
Első szerző:	Nsaif, Mohammed (informatics)
Cím:	Reliable Compression Route Protocol for Mobile Crowd Sensing (RCR-MSC) / Mohammed Ridha Nsaif, Ali S. A. Al-Haboobi, Furkan Rabee, Farah A. Alasadi
Dátum:	2019
ISSN:	2374-4367
Megjegyzések:	According to previous research in Mobile Crowd Sensing (MCS), we have two main challenges to attracting subscribers, the first is energy consumption and the second is the cost of data transmission. In this paper, we suggested framework to control on the this problem and reduce it as soon as possible, the proposed work consisted of aggregator users and target users, where the tasks performed by the aggregator user is to sense the data from the environment ( using Wi-Fi, Bluetooth and IR, compress data?et), compress the data, select secure level, and create a transfer path protocol called "Multi-three way handshake protocol" , where this protocol responsible for transferring the data to the target user without uploading it to the data center server. When the data reached the target users, he will uploade it to the data center server by using Wi-Fi or 3G communication (via piggyback) with free cost. This paper presented a novel Reliable Compression Route protocol for Mobile Crowd Sensing (RCR-MSC), which reduces power consumption and cost of data transmission with multiple level of processes. As well as the present an equation which exposed the energy consumption by this Protocol. After many calculation and experiments procedures ,the result proved improvement performance of work compared to other articles.
Tárgyszavak:	Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk Wireless sensor network Mobile crowd sensing three-way handshaking
Megjelenés:	Journal of Communications. - 14 : 3 (2019), p. 170-178. -
További szerzők:	Al-Haboobi, Ali S. A. Rabee, Furkan Alasadi, Farah A.
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
Borító:
Saját polcon:

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
Borító:
Saját polcon: