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ISSN:
1798-2340 (Online)
Frequency:
Monthly
DOI:
10.12720/jait
Indexing:
ESCI (Web of Science)
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CNKI
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etc
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Acceptance Rate:
19%
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500 USD
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Impact Factor 2022: 1.0
3.1
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Editor-in-Chief
Prof. Kin C. Yow
University of Regina, Saskatchewan, Canada
I'm delighted to serve as the Editor-in-Chief of
Journal of Advances in Information Technology
.
JAIT
is intended to reflect new directions of research and report latest advances in information technology. I will do my best to increase the prestige of the journal.
What's New
2024-03-28
Vol. 15, No. 3 has been published online!
2024-02-26
The papers published in Vol. 15, Nos. 1&2 have been registered with Crossref.
2024-02-26
Vol. 15, No. 2 has been published online!
Home
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2020
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Volume 11, No. 1, February 2020
>
Exploring Cognitive Distraction of Galvanic Skin Response while Driving: An Artificial Intelligence Modeling
Chiang-Yu Cheng
1
, Wesley Shu
2
, and Han-Ping Tsen
3
1. School of Big Data Management, Soochow University, Taiwan
2. International Business School, Xi’an Jiaotong-Liverpool University, Suzhou, China
3. Department of Information and Management, National Central University, Taiwan
Abstract
—It is quite often that we hear fatal traffic accidents due to driver’s distraction. Car manufactures and researchers are therefore putting their efforts into car safety protection mechanism. However, the application of car safety protection mechanism is frequently hindered by its limitations, such as drivers’ privacy or the high cost of its deployment and each of which leads to rare applications of car safety protection specifically in the field of non-autonomous cognitive distraction. This research proposal intends to apply the sensor of Galvanic Skin Response (GSR) to measure drivers’ non-autonomous cognitive distraction due to the blood glucose variation of diabetes. SVM-RFE will be adopted as the major algorithm to create an alert mechanism with the artificial intelligence concept of supervised machine learning. The researched human-machine sense interaction mechanism can be able to embed into the car computer so that it can detect drivers’ physiological changes during diabetes outbreak and then raise advisable alert and intervention accordingly.
Index Terms
—cognitive distraction, galvanic skin response, artificial intelligence, supervised machine learning
Cite: Chiang-Yu Cheng, Wesley Shu, and Han-Ping Tsen, "Exploring Cognitive Distraction of Galvanic Skin Response while Driving: An Artificial Intelligence Modeling," Journal of Advances in Information Technology, Vol. 11, No. 1, pp. 35-39, February 2020. doi: 10.12720/jait.11.1.35-39
Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License (
CC BY-NC-ND 4.0
), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.
6-EC004-台湾
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