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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: 78 days
Avg accept to publ: 48 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


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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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  3/2014 - 9

 HIGH-IMPACT PAPER 

Kohonen Neural Network Stress Detection Using Only Electrodermal Activity Features

BORNOIU, I.-V. See more information about BORNOIU, I.-V. on SCOPUS See more information about BORNOIU, I.-V. on IEEExplore See more information about BORNOIU, I.-V. on Web of Science, GRIGORE, O. See more information about GRIGORE, O. on SCOPUS See more information about GRIGORE, O. on SCOPUS See more information about GRIGORE, O. on Web of Science
 
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Download PDF pdficon (958 KB) | Citation | Downloads: 829 | Views: 3,414

Author keywords
biomedical signal processing, data analysis, electrophysiology, pattern recognition, self organizing feature maps

References keywords
stress(7), electrodermal(6), activity(5), emotion(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2014-08-31
Volume 14, Issue 3, Year 2014, On page(s): 71 - 78
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2014.03009
Web of Science Accession Number: 000340869800009
SCOPUS ID: 84907310113

Abstract
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This paper presents a method for identifying human stress levels by using a Kohonen neural network. The study focuses on differentiating between a relaxed and a stressed state and it presents a series of parameters (skin conductance response signal power, skin conductance response signal frequency, skin conductance level gradient, response rise time and response amplitude) extracted only from the electrodermal activity signal. A very strict recording protocol was used to minimize the artifacts caused by the bad connection between electrodes and skin. A stress inducing method is presented that can be used to replicate results in laboratory conditions.


References | Cited By

Cited-By Clarivate Web of Science

Web of Science® Times Cited: 8 [View]
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Cited-By SCOPUS

SCOPUS® Times Cited: 9
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Cited-By CrossRef

[1] Adaptive Normalization and Feature Extraction for Electrodermal Activity Analysis, Viana-Matesanz, Miguel, Sánchez-Ávila, Carmen, Mathematics, ISSN 2227-7390, Issue 2, Volume 12, 2024.
Digital Object Identifier: 10.3390/math12020202
[CrossRef]

[2] Acute Stress State Classification Based on Electrodermal Activity Modeling, Greco, Alberto, Valenza, Gaetano, Lázaro, Jesús, Garzón-Rey, Jorge Mario, Aguiló, Jordi, de la Cámara, Concepcion, Bailón, Raquel, Scilingo, Enzo Pasquale, IEEE Transactions on Affective Computing, ISSN 1949-3045, Issue 1, Volume 14, 2023.
Digital Object Identifier: 10.1109/TAFFC.2021.3055294
[CrossRef]

[3] Importance of Testing with Independent Subjects and Contexts for Machine-Learning Models to Monitor Construction Workers’ Psychophysiological Responses, Lee, Gaang, Lee, SangHyun, Journal of Construction Engineering and Management, ISSN 0733-9364, Issue 9, Volume 148, 2022.
Digital Object Identifier: 10.1061/(ASCE)CO.1943-7862.0002341
[CrossRef]

[4] ELECTRODERMAL ACTIVITY-BASED ANALYSIS OF EMOTION RECOGNITION USING TEMPORAL-MORPHOLOGICAL FEATURES AND MACHINE LEARNING ALGORITHMS, SRIRAM KUMAR, P., GOVARTHAN, PRAVEEN KUMAR, GANAPATHY, NAGARAJAN, RONICKOM, JAC FREDO AGASTINOSE, Journal of Mechanics in Medicine and Biology, ISSN 0219-5194, Issue 06, Volume 23, 2023.
Digital Object Identifier: 10.1142/S0219519423400444
[CrossRef]

[5] Personalized mental stress detection with self-organizing map: From laboratory to the field, Tervonen, Jaakko, Puttonen, Sampsa, Sillanpää, Mikko J., Hopsu, Leila, Homorodi, Zsolt, Keränen, Janne, Pajukanta, Janne, Tolonen, Antti, Lämsä, Arttu, Mäntyjärvi, Jani, Computers in Biology and Medicine, ISSN 0010-4825, Issue , 2020.
Digital Object Identifier: 10.1016/j.compbiomed.2020.103935
[CrossRef]

[6] Computational Balancing between Wearable Sensor and Smartphone towards Energy-Efficient Remote Healthcare Monitoring, SECERBEGOVIC, A., GOGIC, A., SULJANOVIC, N., ZAJC, M., MUJCIC, A., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 4, Volume 18, 2018.
Digital Object Identifier: 10.4316/AECE.2018.04001
[CrossRef] [Full text]

[7] Social and competition stress detection with wristband physiological signals, Sevil, Mert, Hajizadeh, Iman, Samadi, Sediqeh, Feng, Jianyuan, Lazaro, Caterina, Frantz, Nicole, Yu, Xia, Brandt, Rachel, Maloney, Zacharie, Cinar, Ali, 2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN), ISBN 978-1-5090-6244-7, 2017.
Digital Object Identifier: 10.1109/BSN.2017.7936002
[CrossRef]

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


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