A new two-step ensemble learning model for improving stress prediction of automobile drivers
Joint Authors
Isa, Ghassan
al-Nashashiibi, Mayy
al-Banna, Abd al-Karim
al-Khalili, Nuha
Hadi, Wail
Source
The International Arab Journal of Information Technology
Issue
Vol. 18, Issue 6 (30 Nov. 2021), pp.819-829, 11 p.
Publisher
Zarqa University Deanship of Scientific Research
Publication Date
2021-11-30
Country of Publication
Jordan
No. of Pages
11
Main Subjects
Information Technology and Computer Science
Abstract EN
Commuting when there is a significant volume of traffic congestion has been acknowledged as one of the key factors causing stress.
Significant levels of stress whilst driving are seen to have a profoundly negative effect on the actions and ability of a driver; this has the capacity to result in risks, hazards and accidents.
As such, there is a recognized need to determine drivers’ levels of stress and accordingly predict the key causes responsible for high levels of stress.
In this work, the objective is centred on providing an ensemble machine learning framework in order to determine the stress levels of drivers.
Moreover, the study also provides a fresh set of data, as gathered from 14 different drivers, with data collection having taken place during driving in Amman, Jordan.
Data was gathered via the implementation of a wearable biomedical instrument that was attached to the driver on a continuous basis in order to gather physiological data.
The data gathered was accordingly categorised into two different groups: ‘Yes’, which represents the presence of stress, whilst ‘No’ represents the absence of stress.
Importantly, in an effort to circumvent the negative impact of driver instances with a minority class on stress predictions, oversampling technique was applied.
A two-step ensemble classifier was developed through bringing together the findings from random forest, decision tree, and Repeated Incremental Pruning to Produce Error Reduction (RIPPER) classifiers, which was then inputted into a Multi-Layer Perceptron neural network.
The experimental findings highlight that the suggested framework is far more precise and has a more scalable capacity when compared with all classifiers in relation to accuracy, g-mean measures and sensitivity.
American Psychological Association (APA)
al-Nashashiibi, Mayy& Hadi, Wail& al-Khalili, Nuha& Isa, Ghassan& al-Banna, Abd al-Karim. 2021. A new two-step ensemble learning model for improving stress prediction of automobile drivers. The International Arab Journal of Information Technology،Vol. 18, no. 6, pp.819-829.
https://search.emarefa.net/detail/BIM-1430947
Modern Language Association (MLA)
al-Nashashiibi, Mayy…[et al.]. A new two-step ensemble learning model for improving stress prediction of automobile drivers. The International Arab Journal of Information Technology Vol. 18, no. 6 (Nov. 2021), pp.819-829.
https://search.emarefa.net/detail/BIM-1430947
American Medical Association (AMA)
al-Nashashiibi, Mayy& Hadi, Wail& al-Khalili, Nuha& Isa, Ghassan& al-Banna, Abd al-Karim. A new two-step ensemble learning model for improving stress prediction of automobile drivers. The International Arab Journal of Information Technology. 2021. Vol. 18, no. 6, pp.819-829.
https://search.emarefa.net/detail/BIM-1430947
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 827-829
Record ID
BIM-1430947