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JCR Impact Factor: 0.459
JCR 5-Year IF: 0.442
Issues per year: 4
Current issue: Feb 2017
Next issue: May 2017
Avg review time: 79 days


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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Broken Bar Fault Detection in IM Operating Under No-Load Condition, RELJIC, D., JERKAN, D., MARCETIC, D., OROS, D.
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2017-Feb-16
With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "Big Data - " before the paper title in OpenConf.

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2016-Jun-14
Thomson Reuters published the Journal Citations Report for 2015. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.459, and the JCR 5-Year Impact Factor is 0.442.

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  3/2014 - 9

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: 253 | Views: 1,277

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  «-- Click to see who has cited this paper

[1] G. Rigas, C. Katsis, P. Bougia and D. Fotiadis, "A Reasoning-Based Framework for Car Driver’s Stress Prediction", in Proc. of 16th Mediterranean Conference on Control and Automation, Ajaccio, Corsica, France, pp. 627 - 632, Jun. 2008.

[2] J. A. Healey, R. W. Picard, "Detecting Stress during Real-World Driving Tasks Using Physiological Sensors", in IEEE Transactions on Intelligent Transportation Systems, vol. 6, issue 2, pp. 156-166, Jun. 2005.
[CrossRef] [Web of Science Times Cited 329] [SCOPUS Times Cited 491]


[3] J. Zhai, A. Barreto, "Stress Detection in Computer Users through Non-Invasive Monitoring of Physiological Signals", in Biomedical Sciences Instrumentation, vol. 42, pp. 495-500, 2006.

[4] A. Drachen, L. E. Nacke, G. Yannakakis, A. L. Pedersen, "Correlation between Heart-Rate, Electrodermal Activity and Player Experience in First-Person Shooter Games", in Proc. of the 5th ACM SIGGRAPH, ACM SIGGRAPH Publishers, pp. 49-54, 2009.
[CrossRef] [SCOPUS Times Cited 68]


[5] D. Kulic, E. A. Croft, "Affective State Estimation for Human-Robot Interaction", in IEEE Transactions on Robotics, vol. 23, issue 5, pp. 991-1000, Oct. 2007.
[CrossRef] [Web of Science Times Cited 61] [SCOPUS Times Cited 82]


[6] G. Rigas, C. D. Katsis, G Ganiatsas, D.I. Fotiadis, " A User Independent, Biosignal Based, Emotion Recognition Method", in Proc. of the 11th International Conference, Corfu, Greece, pp. 314-318, 2007.
[CrossRef]


[7] B. Wolfram, Electrodermal Activity, Springer US, 2nd ed., 2012.
[CrossRef] [Web of Science Times Cited 214] [SCOPUS Times Cited 49]


[8] M. E. Dawson, A. M. Schell, D. Filion, "The Electrodermal System", in Handbook of Psychophysiology 3rd ed., New York: Cambridge University Press, 2007.
[CrossRef]


[9] S. Schmidt, R. Schnider, M. Binder, D. Burkele, H. Walach, "Investigating Methodological Issues in EDA-DMILS: Results from a Pilot Study", Journal of Parapsychology, vol. 65, pp. 59-82, 2001.

[10] R. Henriques, A. Paiva, C. Antunes, "On the Need of New Methods to Mine Electrodermal Activity in Emotion-Centered Studies", in 8th International Workshop, ADMI 2012, Valencia, Spain, pp. 203-215, Jun. 2012.
[CrossRef] [SCOPUS Times Cited 6]


[11] R. Henriques, A. Paiva, C. Antunes, "Accessing Emotion Patterns from Affective Interactions Using Electrodermal Activity", in 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction (ACII), Geneva, pp. 43-48, Sept. 2013.
[CrossRef] [Web of Science Times Cited 3] [SCOPUS Times Cited 6]


[12] C. Kirschbaum, K. M. Pirke, D. Hellhammer, "The 'Trier Social Stress Test' - A Tool for Investigating Psychobiological Stress Responses in a Laboratory Setting", in Neuropsychobiology, no. 28, pp. 76-81, 1993.

[13] M. V. Thoma, R. La Marca, R. Bronnimann, L. Finkel, U. Ehlert, U.M. Nater, "The Effect of Music on the Human Stress Response", in PLoS ONE, vol. 8, 2013.
[CrossRef] [Web of Science Times Cited 19] [SCOPUS Times Cited 30]


[14] S. Schmidt, H. Walach, "Electrodermal Activity (EDA) - State-of-the-Art Measurement and Techniques for Parapsychological Purposes", in Journal of Parapsychology, vol. 64, pp. 139-163, 2000.

[15] I. V. Bornoiu, O. Grigore, "A Study about Feature Extraction for Stress Detection", 2013 8th International Symposium in Advanced Topics in Electrical Engineering (ATEE), Bucharest, Romania, May 2013.
[CrossRef] [SCOPUS Times Cited 4]


[16] K. Kim, S.W. Bang, S.R. Kim, "Emotion Recognition System Using Short-Term Monitoring of Physiological Signals", in Medical and Biological Engineering and Computing 2004, vol. 42, pp. 419-427, 2004.
[CrossRef] [Web of Science Times Cited 233] [SCOPUS Times Cited 357]


[17] T. Cover, P. Hart, "Nearest neighbor pattern classification", in IEEE Transactions on Information Theory, vol. 13, issue 1, pp. 21-27, January 1967.
[CrossRef] [SCOPUS Times Cited 4354]


[18] T. Kohonen, "The Self-Organizing Map", Proceedings IEEE, vol. 78, no. 9, pp. 1464-1479, Sept. 1990.
[CrossRef] [Web of Science Times Cited 3120] [SCOPUS Times Cited 3914]


[19] T. Kohonen, Self-Organizing Maps, Springer-Verlag, Berlin, 1995.
[CrossRef]


[20] S. Haykin, Neural Networks. A comprehensive foundation, Second Edition, Prentice Hall, 1999.

[21] R. Rojas, Neural Networks. A systematic introduction, Berlin, 1996.

[22] M. Su, T. Liu, H. Chang, "Improving the Self-Organizing Feature Map Algorithm Using an Efficient Initialization Scheme", in Tamkang Journal of Science and Engineering, vol.5, no.1, pp. 35-48, 2002.

[23] I. T. Jolliffe, Principal Component Analysis, Springer Series in Statistics, 2nd ed., Springer, NY, 2002.



References Weight

Web of Science® Citations for all references: 3,979 TCR
SCOPUS® Citations for all references: 9,361 TCR

Web of Science® Average Citations per reference: 166 ACR
SCOPUS® Average Citations per reference: 390 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 2017-04-21 02:03 in 101 seconds.




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


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