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JCR Impact Factor: 0.700
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Current issue: Nov 2024
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PUBLISHER

Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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LATEST NEWS

2024-Jun-20
Clarivate Analytics published the InCites Journal Citations Report for 2023. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.700 (0.700 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.600.

2023-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2022. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.800 (0.700 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 1.000.

2023-Jun-05
SCOPUS published the CiteScore for 2022, computed by using an improved methodology, counting the citations received in 2019-2022 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2022 is 2.0. For "General Computer Science" we rank #134/233 and for "Electrical and Electronic Engineering" we rank #478/738.

2022-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2021. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.825 (0.722 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.752.

2022-Jun-16
SCOPUS published the CiteScore for 2021, computed by using an improved methodology, counting the citations received in 2018-2021 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2021 is 2.5, the same as for 2020 but better than all our previous results.

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  3/2018 - 14

Mobile Subscriber Profiling and Personal Service Generation using Location Awareness

OZTOPRAK, K. See more information about OZTOPRAK, K. on SCOPUS See more information about OZTOPRAK, K. on IEEExplore See more information about OZTOPRAK, K. on Web of Science
 
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Download PDF pdficon (889 KB) | Citation | Downloads: 897 | Views: 2,257

Author keywords
social network services, artificial neural networks, data mining, real-time systems, cooperative communication

References keywords
mobile(12), profiling(11), user(9), communications(7), prediction(6), networks(6), mobility(5), machine(5), computing(5), telecommunications(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2018-08-31
Volume 18, Issue 3, Year 2018, On page(s): 105 - 112
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2018.03014
Web of Science Accession Number: 000442420900014
SCOPUS ID: 85052113265

Abstract
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In the mobile environment, the location and the next move of subscribers are important. In this study, a method to detect the next move of the subscribers is proposed. In addition to the categorization of subscribers by using their Internet usage history, the knowledge of the next move pattern of subscribers will provide the flexibility to guide them to decide the next move. During the tracking of subscribers, the mobile devices of the subscribers are used as sensors to get in-depth knowledge about their preferences in their social life. The method presented here is the first in the literature to estimate the next move without connecting to any social networks. It combines the geographic locations and the Internet usage of the subscribers in order to predict their movement. In addition, most of the IoT studies either concentrate on network topologies or power consumption, while in this study, dynamicity and exact location estimation are utilized to handle the challenges and attain the required results. The results of the experiments show that the proposed system predicts the next move of a subscriber with a precision of more than 90 percent.


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Cited-By SCOPUS

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Cited-By CrossRef

[1] An Entropy-based Method for Social Apps Privacy Assessment Using the Android Permissions Architecture, SANDOR, A., SIMION, E., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 3, Volume 22, 2022.
Digital Object Identifier: 10.4316/AECE.2022.03009
[CrossRef] [Full text]

[2] Academic Graph: A Literature Review System, Cataltas, Mustafa, Yumusak, Semih, Oztoprak, Kasim, 2022 IEEE International Conference on Big Data (Big Data), ISBN 978-1-6654-8045-1, 2022.
Digital Object Identifier: 10.1109/BigData55660.2022.10020376
[CrossRef]

[3] Detecting Dangerous Maritime Refugee Migration Paths through Cell Phone Activities, Coban, Mustafa, Yumusak, Semih, Yilmaz, Yasin, Altun, Huseyin Oktay, 2022 IEEE International Conference on Big Data (Big Data), ISBN 978-1-6654-8045-1, 2022.
Digital Object Identifier: 10.1109/BigData55660.2022.10021131
[CrossRef]

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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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