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JCR Impact Factor: 0.800
JCR 5-Year IF: 1.000
SCOPUS CiteScore: 2.0
Issues per year: 4
Current issue: Feb 2024
Next issue: May 2024
Avg review time: 75 days
Avg accept to publ: 48 days
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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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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.

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  3/2009 - 6

 HIGHLY CITED PAPER 

Information Structuring and Retrieval with Topic Maps for Medical E-Learning

STANESCU, L. See more information about STANESCU, L. on SCOPUS See more information about STANESCU, L. on IEEExplore See more information about STANESCU, L. on Web of Science, BURDESCU, D. See more information about BURDESCU, D. on SCOPUS See more information about BURDESCU, D. on SCOPUS See more information about BURDESCU, D. 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 (478 KB) | Citation | Downloads: 2,000 | Views: 6,929

Author keywords
information filtering, information visualization, multimedia databases, search process, thesauri

References keywords
topic(6), medical(6), learning(6), maps(5), information(5), science(4), retrieval(4), mesh(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2009-10-26
Volume 9, Issue 3, Year 2009, On page(s): 27 - 33
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2009.03006
Web of Science Accession Number: 000271872000006
SCOPUS ID: 77954635399

Abstract
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The paper presents original ways of using topic maps for information structuring and retrieval in medical e-learning domain. The topic map is mainly used for graphical visualization of the MeSH thesaurus containing medical terms. The hierarchical structure of the descriptors from MeSH thesaurus that has also multiple associative and equivalence relationships between medical terms can be properly visualized in this way. The topic map is built and populated using an original algorithm, by mapping an xml file that can be downloaded for free to an xtm file that contains the topic map. The paper also presents how to use the topic map for semantic querying of a multimedia database with medical images that are accompanied by diagnosis and treatment as important information. For retrieving the interest information for student, this access path can be combined with another modern solution: the content-based visual query on the multimedia medical database using primitive features like color and texture.


References | Cited By

Cited-By Clarivate Web of Science

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Cited-By SCOPUS

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Cited-By CrossRef

[1] Post-processing of Deep Web Information Extraction Based on Domain Ontology, LIU, L., PENG, T., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 4, Volume 13, 2013.
Digital Object Identifier: 10.4316/AECE.2013.04005
[CrossRef] [Full text]

[2] A Semantic-Based Topic Knowledge Map System (STKMS) for Lesson-Learned Documents Reuse in Product Design, HUANG, Ywen, JIANG, Zhua, IEICE Transactions on Information and Systems, ISSN 0916-8532, Issue 5, Volume E97.D, 2014.
Digital Object Identifier: 10.1587/transinf.E97.D.1049
[CrossRef]

Updated 2 days, 18 hours ago

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


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