Employee Mental Workload Classification in Industrial Workplaces: A Machine Learning Approach
Hussain, Ayesha; Keikhosrokiani, Pantea; Asl, Moussa Pourya (2024-05-11)
Avaa tiedosto
Sisältö avataan julkiseksi: 11.05.2026
Springer
11.05.2024
Hussain, A., Keikhosrokiani, P., Asl, M.P. (2024). Employee Mental Workload Classification in Industrial Workplaces: A Machine Learning Approach. In: Saeed, F., Mohammed, F., Fazea, Y. (eds) Advances in Intelligent Computing Techniques and Applications. IRICT 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 211. Springer, Cham. https://doi.org/10.1007/978-3-031-59707-7_4
https://rightsstatements.org/vocab/InC/1.0/
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG. This is a post-peer-review, pre-copyedit version of an article published in Advances in intelligent computing techniques and applications: Intelligent systems, intelligent health informatics, intelligent big data analytics and smart computing, volume 2. The final authenticated version is available online at: https://doi.org/10.1007/978-3-031-59707-7_4
https://rightsstatements.org/vocab/InC/1.0/
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG. This is a post-peer-review, pre-copyedit version of an article published in Advances in intelligent computing techniques and applications: Intelligent systems, intelligent health informatics, intelligent big data analytics and smart computing, volume 2. The final authenticated version is available online at: https://doi.org/10.1007/978-3-031-59707-7_4
https://rightsstatements.org/vocab/InC/1.0/
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202405294042
https://urn.fi/URN:NBN:fi:oulu-202405294042
Tiivistelmä
Abstract
Employees at industrial workplaces are expected to produce labour of a certain standard. They are instructed to improve their quality of work, and this may take a toll on their mental health. Mental workload directly affects employees’ performance, productivity, and well-being. Therefore, this paper conducts a comparative study for the classification of mental workload where a mental workload dataset is subjected to four machine learning classification models-Naïve Bayes, Extreme Gradient Boosting, Support Vector Machine and K-Nearest Neighbour. Their performance is measured against the performance metrics-accuracy, precision, recall and f1-score. Before synthetic minority oversampling method Support Vector Machine performed the best with 90.41% accuracy and K-Nearest Neighbour performed the best with 98.61% accuracy after Synthetic Method of Oversampling Technique.
Employees at industrial workplaces are expected to produce labour of a certain standard. They are instructed to improve their quality of work, and this may take a toll on their mental health. Mental workload directly affects employees’ performance, productivity, and well-being. Therefore, this paper conducts a comparative study for the classification of mental workload where a mental workload dataset is subjected to four machine learning classification models-Naïve Bayes, Extreme Gradient Boosting, Support Vector Machine and K-Nearest Neighbour. Their performance is measured against the performance metrics-accuracy, precision, recall and f1-score. Before synthetic minority oversampling method Support Vector Machine performed the best with 90.41% accuracy and K-Nearest Neighbour performed the best with 98.61% accuracy after Synthetic Method of Oversampling Technique.
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