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JCR Impact Factor: 0.699
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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

2018-Jun-27
Clarivate Analytics published the InCites Journal Citations Report for 2017. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.699, and the JCR 5-Year Impact Factor is 0.674.

2017-Jun-14
Thomson Reuters published the Journal Citations Report for 2016. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.595, and the JCR 5-Year Impact Factor is 0.661.

2017-Feb-16
With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "BigData - " before the paper title in OpenConf.

Read More »


    
 

  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
 
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (889 KB) | Citation | Downloads: 150 | Views: 196

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.


References | Cited By  «-- Click to see who has cited this paper

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References Weight

Web of Science® Citations for all references: 223 TCR
SCOPUS® Citations for all references: 473 TCR

Web of Science® Average Citations per reference: 7 ACR
SCOPUS® Average Citations per reference: 14 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2018-12-11 08:08 in 140 seconds.




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Stefan cel Mare University of Suceava, Romania


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