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JCR Impact Factor: 0.595
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Issues per year: 4
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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229

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


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ABC Algorithm based Fuzzy Modeling of Optical Glucose Detection, SARACOGLU, O. G., BAGIS, A., KONAR, M., TABARU, T. E.
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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.

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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.

We have the confirmation Advances in Electrical and Computer Engineering will be included in the Gale database.

IoT is a new emerging technology domain which will be used to connect all objects through the Internet for remote sensing and control. IoT uses a combination of WSN (Wireless Sensor Network), M2M (Machine to Machine), robotics, wireless networking, Internet technologies, and Smart Devices. We dedicate a special section of Issue 2/2017 to IoT. Prospective authors are asked to make the submissions for this section no later than the 31st of March 2017, placing "IoT - " before the paper title in OpenConf.

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  3/2014 - 6

On the Optimality of Trust Network Analysis with Subjective Logic

PARK, Y. See more information about PARK, Y. on SCOPUS See more information about PARK, Y. on IEEExplore See more information about PARK, Y. on Web of Science
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Download PDF pdficon (831 KB) | Citation | Downloads: 46 | Views: 1,488

Author keywords
trust, reputation, subjective logic, trust networks, identity management system

References keywords
trust(12), networks(6), josang(5), optimization(4), management(4), computing(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2014-08-31
Volume 14, Issue 3, Year 2014, On page(s): 49 - 54
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2014.03006
Web of Science Accession Number: 000340869800006
SCOPUS ID: 84907335535

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Building and measuring trust is one of crucial aspects in e-commerce, social networking and computer security. Trust networks are widely used to formalize trust relationships and to conduct formal reasoning of trust values. Diverse trust network analysis methods have been developed so far and one of the most widely used schemes is TNA-SL (Trust Network Analysis with Subjective Logic). Recent papers claimed that TNA-SL always finds the optimal solution by producing the least uncertainty. In this paper, we present some counter-examples, which imply that TNA-SL is not an optimal algorithm. Furthermore, we present a probabilistic algorithm in edge splitting to minimize uncertainty.

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

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[CrossRef] [SCOPUS Times Cited 54]

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[CrossRef] [SCOPUS Times Cited 80]

[3] A. Josang, R. Hayward, S. Pope, "Trust Network Analysis with Subjective Logic," in Proc. of 29th ACSC2006, Jan. 2006, pp. 85-94.

[4] L. Ding, P. Kolari, S. Ganjugunte, T. Finin, A. Joshi, "Modeling and Evaluating Trust Network Inference," In Proc. of 7th AAMASÂ’2004, July 2004, pp. 21-32.

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[CrossRef] [SCOPUS Times Cited 2140]

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[CrossRef] [SCOPUS Times Cited 6]

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[CrossRef] [Web of Science Times Cited 81] [SCOPUS Times Cited 128]

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[CrossRef] [SCOPUS Times Cited 19]

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[CrossRef] [Web of Science Times Cited 14] [SCOPUS Times Cited 57]

[12] U. Maurer, "Modeling a Public-Key Infrastructure," in Proc. of ESORICS-LNCS Vol. 1136, 1996, pp. 325-350.

[13] A. Gutscher, "A Trust Model for an Open, Decentralized Reputation System," in Proc. IFIPTM, 2007, pp. 285-300.
[CrossRef] [SCOPUS Times Cited 16]

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[CrossRef] [SCOPUS Times Cited 14]

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[18] T. Bhuiyan, A. Josang, Y. Xu, "An analysis of trust transitivity taking base rate into account," in Proc. of Ubiquitous, Autonomic and Trusted Computing, 2009, pp. 34-39.

[19] P. Flocchini, F. L. Luccio, "Routing in Series Parallel Networks," Theory of Computing Systems, Vol. 36, pp. 137-157, 2003.
[CrossRef] [Web of Science Times Cited 5] [SCOPUS Times Cited 8]

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[23] R. H. Byrd, P. Lu, J. Nocedal, C. Zhu, "A Limited Memory Algorithm for Bound Constrained Optimization," SIAM Journal on Scientific Computing, vol. 16, no. 5, pp. 1190-1208, 1995.
[CrossRef] [Web of Science Times Cited 1531]

[24] A. R. Conn, K. Scheinberg, L. N. Vicente, "Introduction to Derivative-Free Optimization (Mps-Siam Series on Optimization)," pp. 113-120, Society for Industrial and Applied Mathematics, 1st edition, Jan. 2009.

References Weight

Web of Science® Citations for all references: 1,631 TCR
SCOPUS® Citations for all references: 2,522 TCR

Web of Science® Average Citations per reference: 65 ACR
SCOPUS® Average Citations per reference: 101 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 2017-12-08 14:41 in 87 seconds.

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

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