Integration of ensemble and evolutionary machine learning algorithms for monitoring diver behavior using physiological signals

Koohestani, Afsaneh, Abdar, Moloud, Khosravi, Abbas, Nahavandi, Saeid and Koohestani, Mahereh 2019, Integration of ensemble and evolutionary machine learning algorithms for monitoring diver behavior using physiological signals, IEEE access, vol. 7, pp. 98971-98992, doi: 10.1109/ACCESS.2019.2926444.

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Title Integration of ensemble and evolutionary machine learning algorithms for monitoring diver behavior using physiological signals
Author(s) Koohestani, Afsaneh
Abdar, Moloud
Khosravi, AbbasORCID iD for Khosravi, Abbas orcid.org/0000-0001-6927-0744
Nahavandi, SaeidORCID iD for Nahavandi, Saeid orcid.org/0000-0002-0360-5270
Koohestani, Mahereh
Journal name IEEE access
Volume number 7
Start page 98971
End page 98992
Total pages 22
Publisher Institute of Electrical and Electronics Engineers
Place of publication Piscataway, N.J.
Publication date 2019
ISSN 2169-3536
Keyword(s) Science & Technology
Technology
Computer Science, Information Systems
Engineering, Electrical & Electronic
Telecommunications
Computer Science
Engineering
Diver behavior
machine learning
ensemble learning
evolutionary optimization algorithms (EOAs)
physiological signals
Summary The level of consciousness and the concentration of drivers while driving play a vital role for reducing the number of accidents. In recent decade, in-vehicle infotainment (IVI) [or in-car entertainment (ICE)] is one of the main reasons that lead to degradation of drivers performance and losing awareness. However, the impacts of some other reasons, such as drowsiness and driving fatigue, are entirely important as well. Hence, early detection of such performance degradation using different methods is a very hot research domain. To this end, the data set is collected using two different simulated driving scenarios: normal and loaded drive (17 elderly and 51 young/35 male and 33 female). This paper, therefore, concentrates on driving performance analysis using various machine learning techniques. The optimization part of the proposed methodology has two main steps. In the first step, the performances of the K-nearest neighbors (KNN), support vector machine (SVM), and naive Bayes (NB) algorithms are improved using bagging, boosting, and voting ensemble learning techniques. Afterward, four well-known evolutionary optimization algorithms [the ant lion optimizer (ALO), whale optimization algorithm (WOA), particle swarm optimization (PSO), and grey wolf optimizer (GWO)] are applied to the system for optimizing the parameters and as a result enhance the performance of whole system. The GWO-voting approach has the best performance compared to other hybrid methods with the accuracy of 97.50%. The obtained outcomes showed that the proposed system can remarkably raise the performance of the classical algorithms used.
Language eng
DOI 10.1109/ACCESS.2019.2926444
Indigenous content off
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2019, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30129400

Document type: Journal Article
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