Click to open the HelpDesk interface
AECE - Front page banner

Menu:


FACTS & FIGURES

JCR Impact Factor: 0.800
JCR 5-Year IF: 1.000
SCOPUS CiteScore: 2.0
Issues per year: 4
Current issue: May 2024
Next issue: Aug 2024
Avg review time: 55 days
Avg accept to publ: 60 days
APC: 300 EUR


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


TRAFFIC STATS

2,628,764 unique visits
1,044,085 downloads
Since November 1, 2009



Robots online now
bingbot
Googlebot


SCOPUS CiteScore

SCOPUS CiteScore


SJR SCImago RANK

SCImago Journal & Country Rank




TEXT LINKS

Anycast DNS Hosting
MOST RECENT ISSUES

 Volume 24 (2024)
 
     »   Issue 2 / 2024
 
     »   Issue 1 / 2024
 
 
 Volume 23 (2023)
 
     »   Issue 4 / 2023
 
     »   Issue 3 / 2023
 
     »   Issue 2 / 2023
 
     »   Issue 1 / 2023
 
 
 Volume 22 (2022)
 
     »   Issue 4 / 2022
 
     »   Issue 3 / 2022
 
     »   Issue 2 / 2022
 
     »   Issue 1 / 2022
 
 
 Volume 21 (2021)
 
     »   Issue 4 / 2021
 
     »   Issue 3 / 2021
 
     »   Issue 2 / 2021
 
     »   Issue 1 / 2021
 
 
  View all issues  


FEATURED ARTICLE

Analysis of the Hybrid PSO-InC MPPT for Different Partial Shading Conditions, LEOPOLDINO, A. L. M., FREITAS, C. M., MONTEIRO, L. F. C.
Issue 2/2022

AbstractPlus


SAMPLE ARTICLES

Structural Wall Facade Reconstruction of Scanned Scene in Point Clouds, NING, X., WANG, M., TANG, J., ZHANG, H., WANG, Y.
Issue 4/2021

AbstractPlus

Continuous Student Knowledge Tracing Using SVD and Concept Maps, TEODORESCU, O. M., POPESCU, P. S., MOCANU, L. M., MIHAESCU, M. C.
Issue 1/2021

AbstractPlus

Design and Development of Modified Hybrid Resonant Converter with Valley-fill for LED Lighting, BALAKRISHNAN, L. P., RAMALINGAM, S.
Issue 4/2023

AbstractPlus

Step towards Enriching Frequency Support from Wind-Driven Permanent-Magnet Synchronous Generator for Power System Stability, ALI, M. A. S.
Issue 1/2022

AbstractPlus

On Board Neuro Fuzzy Inverse Optimal Control for Type 1 Diabetes Mellitus Treatment: In-Silico Testing, RIOS, Y., GARCIA-RODRIGUEZ, J., SANCHEZ, E., ALANIS, A., RUIZ-VELAZQUEZ, E., PARDO-GARCIA, A.
Issue 3/2022

AbstractPlus

On Proposing a Novel SDN-Caching Mechanism for Optimizing Distribution in ICN Networks, NASCIMENTO, E. B., MORENO, E. D., MACEDO, D. D. J., CARLOS ERPEN de BONA, L., RIGHI, R. R., MESSINA, F.
Issue 1/2023

AbstractPlus




LATEST NEWS

2023-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2022. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.800 (0.700 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 1.000.

2023-Jun-05
SCOPUS published the CiteScore for 2022, computed by using an improved methodology, counting the citations received in 2019-2022 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2022 is 2.0. For "General Computer Science" we rank #134/233 and for "Electrical and Electronic Engineering" we rank #478/738.

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 for 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.

Read More »


    
 

  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
 
View the paper record and citations in View the paper record and citations in Google Scholar
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 (3,179 KB) | Citation | Downloads: 817 | Views: 737

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
Quick view
Full text preview
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.


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

[1] J. Hoffmann, "Review of maritime transport 2016," in Proc. United Nations Conf. on Trade and Development, Geneva, Switzerland, pp. 1-118, 2016.

[2] IMO, SOLAS I. "International Convention for the Safety of Life at Sea," Consolidated Edition, London, GBP, 2014, pp. 1-10.

[3] P. R. Lei, "Mining maritime traffic conflict trajectories from a massive AIS data," Knowledge and Information Systems, vol. 62, no. 1, pp. 259-285, 2020.
[CrossRef] [Web of Science Times Cited 23] [SCOPUS Times Cited 29]


[4] M. Liang, R. W. Liu, Q. Zhong, J. Liu, J. Zhang, "Neural network-based automatic reconstruction of missing vessel trajectory data," in Proc. IEEE 4th International Conf. on Big Data Analytics (ICBDA); Suzhou, China, pp. 426-430, 2019.
[CrossRef] [Web of Science Times Cited 23] [SCOPUS Times Cited 25]


[5] L. Westerdijk, "Classifying vessel types based on AIS data," MSc, Vrije University, Amsterdam, Holland, pp. 49-61, 2019.

[6] Y. Zhou, W. Daamen, T. Vellinga, S. P. Hoogendoorn, "Ship classification based on ship behavior clustering from AIS data," Ocean Engineering, vol. 175, pp. 176-187, 2019.
[CrossRef] [Web of Science Times Cited 67] [SCOPUS Times Cited 86]


[7] Z. Hanyang, S. Xin, Y. Zhenguo, "Vessel sailing patterns analysis from S-AIS data dased on K-means clustering algorithm," in Proc. IEEE 4th International Conference on Big Data Analytics (ICBDA), Suzhou, China, pp. 10-13, 2019.
[CrossRef] [Web of Science Times Cited 13] [SCOPUS Times Cited 11]


[8] M. Mustaffa, S. Ahmad, A. M. Ali, N. Ahmad, M. H. Mohd Jais, "Data mining analysis on Ships collision risk and marine traffic characteristic of Port Klang Malaysia waterways from automatic identification system (AIS)," in Proc. International MultiConference of Engineers and Computer Scientists; Hong Kong, pp. 242-246, 2019.

[9] D. Yang, L. Wu, S. Wang, H. Jia, K. X. Li, "How big data enriches maritime research-a critical review of automatic identification system (AIS) data applications," Transport Reviews, vol. 39, no. 6, pp. 755-773, 2019.
[CrossRef] [Web of Science Times Cited 193] [SCOPUS Times Cited 239]


[10] R. J. Bye, P. G. Almklov, "Normalization of maritime accident data using AIS," Marine Policy, vol. 109, 103675, 2019.
[CrossRef] [Web of Science Times Cited 36] [SCOPUS Times Cited 46]


[11] K. Wang, M. Liang, Y. Li, J. Liu, R. W. Liu, "Maritime traffic data visualization: A brief review," in Proc. IEEE 4th International Conference on Big Data Analytics (ICBDA); Suzhou, China, pp. 67-72, 2019.
[CrossRef] [Web of Science Times Cited 2] [SCOPUS Times Cited 18]


[12] M. Fujii, H. Hashimoto, Y. Taniguchi, E. Kobayashi, "Statistical validation of a voyage simulation model for ocean-going ships using satellite AIS data," Journal of Marine Science and Technology, vol. 24, no. 4, pp. 1297-1307, 2019.
[CrossRef] [Web of Science Times Cited 7] [SCOPUS Times Cited 8]


[13] Y. Liu, R. Song, R. Bucknall, "Intelligent tracking of moving ships in constrained maritime environments using AIS," Cybernetics and Systems, vol. 50, no. 6, pp. 539-555, 2019.
[CrossRef] [Web of Science Times Cited 6] [SCOPUS Times Cited 10]


[14] Z. Liu, Z. Wu, Z. Zheng, "A novel framework for regional collision risk identification based on AIS data," Applied Ocean Research, vol. 89, pp. 261-272, 2019.
[CrossRef] [Web of Science Times Cited 46] [SCOPUS Times Cited 56]


[15] L. Wu, Y. Xu, Q. Wang, F. Wang, Z. Xu, "Mapping global shipping density from AIS data," The Journal of Navigation, vol. 70, no. 1, pp. 67-81, 2017.
[CrossRef] [Web of Science Times Cited 116] [SCOPUS Times Cited 143]


[16] F. Natale, M. Gibin, A. Alessandrini, M. Vespe, A. Paulrud, "Mapping fishing effort through AIS data," PloS one, vol. 10, no. 6, e0130746, 2015.
[CrossRef] [Web of Science Times Cited 177] [SCOPUS Times Cited 195]


[17] M. Mustaffa, M. Abas, S. Ahmad, N. Ahmad Aini, W. F. Abbas, S. A. Che Abdullah, M. Y. Darus, "Marine traffic density over Port Klang, Malaysia using statistical analysis Of AIS data: A preliminary study," Journal of ETA Maritime Science, vol. 4, no. 4, pp. 333-341, 2016.
[CrossRef]


[18] F. Xiao, H. Ligteringen, C. Van Gulijk, B. Ale, "Comparison study on AIS data of ship traffic behavior," Ocean Engineering; vol. 95, pp. 84-93, 2015.
[CrossRef] [Web of Science Times Cited 135] [SCOPUS Times Cited 171]


[19] S. A. Breithaupt, A. Copping, J. Tagestad, J. Whiting, "Maritime route delineation using AIS data from the Atlantic Coast of the US," The Journal of Navigation, vol. 70, pp. 379-394, 2017.
[CrossRef] [Web of Science Times Cited 31] [SCOPUS Times Cited 39]


[20] W. Zhang, F. Goerlandt, J. Montewka, P. Kujala, "A method for detecting possible near miss ship collisions from AIS data," Ocean Engineering, vol. 107, pp. 60-69, 2015.
[CrossRef] [Web of Science Times Cited 181] [SCOPUS Times Cited 214]


[21] J. Li, H. Wang, W. Zhao, Y. Xue, "Ship's trajectory planning based on improved multiobjective algorithm for collision avoidance," Journal of Advanced Transportation, 4068783, 2019.
[CrossRef] [Web of Science Times Cited 23] [SCOPUS Times Cited 36]


[22] Z. Jiang, D. Chen, Z. Yang, "A synchronous optimization model for multiship shuttle tanker fleet design and scheduling considering hard time window constraint," Journal of Advanced Transportation, 1904340, 2018.
[CrossRef] [Web of Science Times Cited 2] [SCOPUS Times Cited 1]


[23] P. Jiacai, J. Qingshan, H. Jinxing, S. Zheping, "An AIS data visualization model for assessing maritime traffic situation and its applications," Procedia Engineering, vol. 29, pp. 365-369, 2012.
[CrossRef] [Web of Science Times Cited 13] [SCOPUS Times Cited 28]


[24] Z. Huang, Z. Shao, J. Pan, X. Ji, Q. Zhao, "Berthing speed control law for large vessels based on AIS data," in Proc. International Conference on Transportation Engineering; Dalian, China, pp. 1322-1330, 2015.
[CrossRef] [SCOPUS Times Cited 1]


[25] V. F. Arguedas, F. Mazzarella, M. Vespe, "Spatio-temporal data mining for maritime situational awareness," in Proc. OCEANS, Genova, Italy, pp. 1-8, 2015.
[CrossRef] [Web of Science Times Cited 14] [SCOPUS Times Cited 22]


[26] A. W. Isenor, M. O. St-Hilaire, S. Webb, M. Mayrand, "MSARI: A database for large volume storage and utilisation of maritime data," The Journal of Navigation, vol. 70, no. 2, pp. 276-290, 2017.
[CrossRef] [Web of Science Times Cited 7] [SCOPUS Times Cited 12]


[27] ITU-R. "Technical characteristics for an automatic identification system using time division multiple access in the VHF maritime mobile frequency band," Recommendation ITU- R M.1371-5. Geneva, Switzerland: Electronic Publication, 2014.

[28] D. Arthur, S. Vassilvitskii, "K-means++: The advantages of careful seeding," in Proc. SODA'07: 18th Annual ACM-Society for Industrial and Applied Mathematics Symposium on Discrete Algorithms; Philadelphia, USA, pp. 1027-1035, 2007.

[29] G. F. Jenks, F. C. Caspall, "Error on choroplethic maps: definition, measurement, reduction," Annals of the Association of American Geographers, vol. 61, no. 2, pp. 217-244, 1971.
[CrossRef] [SCOPUS Times Cited 503]


[30] M. Kantardzic, "Data mining: concepts, models, methods, and algorithms," USA: John Wiley & Sons, pp. 289-296, 2011.

[31] H. Zou, Y. Yu, W. Tang, H. M. Chen, "Improving I/O performance with adaptive data compression for big data applications," In: IEEE International Parallel & Distributed Processing Symposium Workshops; Phoenix, Arizona, USA, pp. 1228-1237, 2014.
[CrossRef] [Web of Science Times Cited 13] [SCOPUS Times Cited 33]


[32] S. H. Jung, K. J. Kim, E. C. Lim, C. B. Sim, "A novel on automatic K value for efficiency improvement of KMeans clustering," in Proc. Advanced Multimedia and Ubiquitous Engineering; Singapore, pp. 181-186, 2017.
[CrossRef] [SCOPUS Times Cited 11]


[33] D. H. Stolfi, E. Alba, X. Yao, "Predicting car park occupancy rates in smart cities," in Proc. International Conference on Smart Cities, Cham, Germany, pp. 107-117, 2017.
[CrossRef] [Web of Science Times Cited 54] [SCOPUS Times Cited 63]


[34] X. Wang, C. Wang, Z. Chaobiao, "Early warning of debris flow using optimized self-organizing feature mapping network," Water Supply, ws2020142, 2020.
[CrossRef] [Web of Science Times Cited 6] [SCOPUS Times Cited 6]


[35] P. Vasant, "Handbook of research on modern optimization algorithms and applications in engineering and economics," USA: IGI Global, pp. 682-690, 2016.

[36] Z. Halim, J. H. Khattak, "Density-based clustering of big probabilistic graphs," Evolving systems, vol. 10, no. 3, pp. 333-350, 2019.
[CrossRef] [Web of Science Times Cited 15] [SCOPUS Times Cited 18]




References Weight

Web of Science® Citations for all references: 1,203 TCR
SCOPUS® Citations for all references: 2,024 TCR

Web of Science® Average Citations per reference: 33 ACR
SCOPUS® Average Citations per reference: 55 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 2024-06-11 16:29 in 188 seconds.




Note1: Web of Science® is a registered trademark of Clarivate Analytics.
Note2: SCOPUS® is a registered trademark of Elsevier B.V.
Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site.

Copyright ©2001-2024
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.

Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.

Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.




Website loading speed and performance optimization powered by: 


DNS Made Easy