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Current issue: Aug 2022
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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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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 in 2021 is 2.5, the same as for 2020 but better than all our previous results.

2021-Jun-30
Clarivate Analytics published the InCites Journal Citations Report for 2020. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 1.221 (1.053 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.961.

2021-Jun-06
SCOPUS published the CiteScore for 2020, computed by using an improved methodology, counting the citations received in 2017-2020 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering in 2020 is 2.5, better than all our previous results.

2021-Apr-15
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  3/2021 - 9

A Novel Approach for Knowledge Discovery from AIS Data: An Application for Transit Marine Traffic in the Sea of Marmara

DOGAN, Y. See more information about DOGAN, Y. on SCOPUS See more information about DOGAN, Y. on IEEExplore See more information about DOGAN, Y. on Web of Science, KART, O. See more information about  KART, O. on SCOPUS See more information about  KART, O. on SCOPUS See more information about KART, O. on Web of Science, KUNDAKCI, B. See more information about  KUNDAKCI, B. on SCOPUS See more information about  KUNDAKCI, B. on SCOPUS See more information about KUNDAKCI, B. on Web of Science, NAS, S. See more information about NAS, S. on SCOPUS See more information about NAS, S. on SCOPUS See more information about NAS, S. on Web of Science
 
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Download PDF pdficon (3,179 KB) | Citation | Downloads: 549 | Views: 637

Author keywords
clustering algorithms, genetic algorithms, knowledge discovery, machine learning, radar signal processing.

References keywords
data(28), maritime(12), traffic(6), icbda(6), ship(5), ocean(5), automatic(5), mining(4), marine(4), identification(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2021-08-31
Volume 21, Issue 3, Year 2021, On page(s): 73 - 80
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2021.03009
Web of Science Accession Number: 000691632000009
SCOPUS ID: 85114809235

Abstract
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This paper addresses the discovery of hidden patterns in the data of Automatic Identification Systems by a novel clustering model using data processing and data mining methods. It reveals the transit tracks and the transit vessels on these tracks in the Sea of Marmara which has a dense marine traffic. In this study, improved Density Based Spatial Clustering of Applications with Noise and KMeans++ clustering algorithms have been used together with complex database queries. This proposed approach has been compared to other clustering algorithms such as Self-Organizing Map, Hierarchical Clustering with Single-Link and Genetic Clustering. It has been observed that these alternative algorithms could not reach high accuracy values and they could not give the expected tracks. The proposed approach has five steps and experimental results demonstrate that when this novel approach has been applied step by step, the results can match the observed data by The Republic of Turkey, Ministry of Transport, Maritime and Communications by 95%. Finally, this novel approach is suggested to maritime authorities for all the seas in the world to manage the vessel traffic which has big and complex data.


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Cited-By CrossRef

[1] Research on Vessel Speed Heading and Collision Detection Method Based on AIS Data, Wang, Guoqing, Fan, En, Zheng, Guohua, Li, Kexiang, Huang, Haiguang, Tang, Yajuan, Mobile Information Systems, ISSN 1875-905X, Issue , 2022.
Digital Object Identifier: 10.1155/2022/7257075
[CrossRef]

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


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