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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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2020-Jun-29
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  2/2018 - 15
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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
 
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 (1,166 KB) | Citation | Downloads: 286 | Views: 1,093

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

Cited-By ISI Web of Science

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Cited-By CrossRef

SCOPUS® Times Cited: 4
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Cited-By CrossRef

[1] USING EXCESS CODE TO DESIGN FAULT-TOLERANT TOPOLOGIES, Loutskii, Heorhii, Volokyta, Artem, Rehida, Pavlo, Goncharenko, Olexandr, TECHNICAL SCIENCES AND TECHNOLOGIES, ISSN 2519-4569, Volume , 2019.
Digital Object Identifier: 10.25140/2411-5363-2019-1(15)-134-144
[CrossRef]

[2] Increasing the fault tolerance of distributed systems for the Hyper de Bruijn topology with excess code, Loutskii, Heorhii, Volokyta, Artem, Rehida, Pavlo, Honcharenko, Oleksandr, Ivanishchev, Bohdan, Kaplunov, Artem, 2019 IEEE International Conference on Advanced Trends in Information Theory (ATIT), ISBN 978-1-7281-6144-0, 2019.
Digital Object Identifier: 10.1109/ATIT49449.2019.9030487
[CrossRef]

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


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