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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: May 2024
Next issue: Aug 2024
Avg review time: 56 days
Avg accept to publ: 60 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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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

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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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  2/2008 - 12

 HIGHLY CITED PAPER 

Training Neural Networks Using Input Data Characteristics

CERNAZANU, C. See more information about CERNAZANU, C. on SCOPUS See more information about CERNAZANU, C. on IEEExplore See more information about CERNAZANU, C. on Web of Science
 
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Download PDF pdficon (710 KB) | Citation | Downloads: 1,217 | Views: 4,881

Author keywords
neural networks, data mining, correlation-based feature subset selection method, data features extraction, training algorithm

References keywords
neural(8), networks(7), data(7), selection(6), learning(6), mining(5), machine(5), ijcnn(4), feature(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2008-06-02
Volume 8, Issue 2, Year 2008, On page(s): 65 - 70
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2008.02012
Web of Science Accession Number: 000264815000012
SCOPUS ID: 77955635511

Abstract
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Feature selection is often an essential data processing step prior to applying a learning algorithm. The aim of this paper consists in trying to discover whether removal of irrelevant and redundant information improves the performance of neural network training results. The present study will describe a new method of training the neural networks, namely, training neural networks using input data features. For selecting the features, we used a filtering technique (borrowed from data mining) which consists in selecting the best features from a training set. The technique is made up of two components: a feature evaluation technique and a search algorithm for selecting the best features. When applied as a data preprocessing step for one common neural network training algorithms, the best data results obtained from this network are favorably comparable to a classical neural network training algorithms. Nevertheless, the first one requires less computation.


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Cited-By Clarivate Web of Science

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Cited-By CrossRef

[1] Automatic and Parallel Optimized Learning for Neural Networks performing MIMO Applications, FULGINEI, F. R., LAUDANI, A., SALVINI, A., PARODI, M., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 1, Volume 13, 2013.
Digital Object Identifier: 10.4316/AECE.2013.01001
[CrossRef] [Full text]

[2] Enhancement of microgrid dynamic responses under fault conditions using artificial neural network for fast changes of photovoltaic radiation and FLC for wind turbine, Rezvani, Alireza, Izadbakhsh, Maziar, Gandomkar, Majid, Energy Systems, ISSN 1868-3967, Issue 4, Volume 6, 2015.
Digital Object Identifier: 10.1007/s12667-015-0156-6
[CrossRef]

[3] A novel sensorless field oriented controller for Permanent Magnet Synchronous Motors, Aygun, Hilmi, Gokdag, Mustafa, Aktas, Mustafa, Cernat, Mihai, 2014 IEEE 23rd International Symposium on Industrial Electronics (ISIE), ISBN 978-1-4799-2399-1, 2014.
Digital Object Identifier: 10.1109/ISIE.2014.6864700
[CrossRef]

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


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