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JCR Impact Factor: 0.699
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Next issue: Feb 2019
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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

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.

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  2/2018 - 15
View TOC | « Previous Article | Next Article »

Method for Efficiency Increasing of Distributed Classification of the Images based on the Proactive Parallel Computing Approach

MUKHIN, V. See more information about MUKHIN, V. on SCOPUS See more information about MUKHIN, V. on IEEExplore See more information about MUKHIN, V. on Web of Science, VOLOKYTA, A. See more information about  VOLOKYTA, A. on SCOPUS See more information about  VOLOKYTA, A. on SCOPUS See more information about VOLOKYTA, A. on Web of Science, HERIATOVYCH, Y. See more information about  HERIATOVYCH, Y. on SCOPUS See more information about  HERIATOVYCH, Y. on SCOPUS See more information about HERIATOVYCH, Y. on Web of Science, REHIDA, P. See more information about REHIDA, P. on SCOPUS See more information about REHIDA, P. on SCOPUS See more information about REHIDA, P. on Web of Science
 
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Download PDF pdficon (1,160 KB) | Citation | Downloads: 174 | Views: 331

Author keywords
parallel programming, distributed computing, pattern recognition, performance analysis, mobile agents

References keywords
data(7), analysis(5), lange(4), computing(4), classification(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2018-05-31
Volume 18, Issue 2, Year 2018, On page(s): 117 - 122
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2018.02015
Web of Science Accession Number: 000434245000015
SCOPUS ID: 85047874772

Abstract
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In this paper we propose a new clustering method based on alpha-procedure that differs with proactive computing of graphs and other possible factors superposition of classification. This approach does not increase the calculation time but obtains a potentially more accurate result for object classification. The description of the method alpha- procedure parallelization is performed. The paper presents a method of parallel computing of the independent parts in distributed systems using a model of actors. The paper describes a modified method of classification of multidimensional objects designed for computing on distributed computer systems. It is shown, that in case if the number of nodes is increased, the performance decreases linearly. This method allows realizing the choosing by n-factors despite the classical method. This change is acceptable as the ways of the calculations are independent from each other and can be calculated in parallel on a distributed system. Therefore, in modern distributed systems, in case if the amount of resources is sufficient, we may use the redundant calculations that may improve the clustering results. For this method, we propose to use the resources for parallel calculation in several independent ways, and further comparing them, to choose the best result.


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

[1] T. Lange, P. Mozharovskyi,"The Alpha-Procedure: A Nonparametric Invariant Method for Automatic Classification of Multi-Dimensional Objects", Data Analysis, Machine Learning and Knowledge Discovery. Springer International Publishing, pp. 79-86, 2014.
[CrossRef] [Web of Science Times Cited 1]


[2] T. Lange, K. Mosler, P. Mozharovskyi, "Fast nonparametric classification based on data depth", Statistical Papers, pp. 1-21, 2014.
[CrossRef] [Web of Science Times Cited 26]


[3] P. Mozharovskyi, K. Mosler, T. Lange, "Classifying real-world data with the {DD}\ alpha-procedure", Advances in Data Analysis and Classification 9.3, pp. 287-314, 2015.
[CrossRef] [Web of Science Times Cited 8]


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[CrossRef]


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[CrossRef]


[6] S. Stijven, W. Minnebo, E. Vladislavleva, "Separating the wheat from the chaff: on feature selection and feature importance in regression random forests and symbolic regression", Proceedings of the 13th Annual Conference Companion on Genetic and Evolutionary Computation GECCO'11, Dublin, Ireland, pp. 623-630, 2011.
[CrossRef]


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[CrossRef]


[9] T. Lange, P. Mozharovskyi, G. Barath. "Two approaches for solving tasks of pattern recognition and reconstruction of functional dependencies", XIV International conference on applied stochastic models and data analysis, 2011.

[10] A. Pakonen, T. Pirttioja, I. Seilonen "Proactive computing in process monitoring: Information agents for operator support", Emerging Technologies and Factory Automation, ETFA'06. IEEE Conference on. IEEE, pp. 153-158, 2006.
[CrossRef]


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[CrossRef]


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[CrossRef]


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[CrossRef]


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[CrossRef] [Web of Science Times Cited 15]


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[CrossRef]


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[CrossRef]


[19] H. Loutskii, A. Volokyta, O. Yakushev, P. Rehida, Vu Duc Thinh, "Development of real time method of detecting attacks based on artificial intelligence.", Technology audit and production reserves, vol. 29, pp. 40-46, 2016.
[CrossRef]


[20] A. Volokyta, P. Rehida, V. Shyrochyn, A. Nikitiuk, Vu Duc Thinh "The Effective Method of Distributed Data Storage That Provides High Access Speed and Level of Safety", Bulgarian journal for Engineering Design, vol. 29, pp. 67-75, April 2016.



References Weight

Web of Science® Citations for all references: 1,108 TCR
SCOPUS® Citations for all references: 0

Web of Science® Average Citations per reference: 53 ACR
SCOPUS® Average Citations per reference: 0

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 2019-01-15 03:03 in 120 seconds.




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


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