Job rotations based on physical and psychological workloads: A proposal for the footwear industry

Leite, Wilza Karla dos Santos; Araújo, Anísio José da Silva; Silva, Luiz Bueno da; Costa, Luciano Carlos Azevedo da; Silva, Jonhatan Magno Norte da; Vieira, Elamara Marama de Araujo; Souza, Erivaldo Lopes de; Kramer, Hugo Harry Frederico Ribeiro; Oliveira, Romero Cardoso

doi:10.1016/j.cie.2024.109887

Computers & Industrial Engineering

2024

DOI: 10.1016/j.cie.2024.109887

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Job rotations based on physical and psychological workloads: A proposal for the footwear industry

Wilza Karla dos Santos Leite,

Anísio José da Silva Araújo,

Luiz Bueno da Silva

et al.

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Evaluation of the Performance of Neural and Non-Neural Methods to Classify the Severity of Work Accidents Occurring in the Footwear Industry Complex

da Silva,

da Silva Braz,

da Silva

et al. 2024

ASI

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In the footwear industry, occupational risks are significant, and work accidents are frequent. Professionals in the field prepare documents and reports about these accidents, but the need for more time and resources limits learning based on past incidents. Machine learning (ML) and deep learning (DL) methods have been applied to analyze data from these documents, identifying accident patterns and classifying the damage’s severity. However, evaluating the performance of these methods in different economic sectors is crucial. This study examined neural and non-neural methods for classifying the severity of workplace accidents in the footwear industry complex. The random forest (RF) and extreme gradient boosting (XGBoost) methods were the most effective non-neural methods. The neural methods 1D convolutional neural networks (1D-CNN) and bidirectional long short-term memory (Bi-LSTM) showed superior performance, with parameters above 98% and 99%, respectively, although with a longer training time. It is concluded that using these methods is viable for classifying accidents in the footwear industry. The methods can classify new accidents and simulate scenarios, demonstrating their adaptability and reliability in different economic sectors for accident prevention.

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Evaluation of the Performance of Neural and Non-Neural Methods to Classify the Severity of Work Accidents Occurring in the Footwear Industry Complex

da Silva,

da Silva Braz,

da Silva

et al. 2024

ASI

View full text Add to dashboard Cite

show abstract

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Job rotations based on physical and psychological workloads: A proposal for the footwear industry

Cited by 1 publication

References 56 publications

Evaluation of the Performance of Neural and Non-Neural Methods to Classify the Severity of Work Accidents Occurring in the Footwear Industry Complex

Evaluation of the Performance of Neural and Non-Neural Methods to Classify the Severity of Work Accidents Occurring in the Footwear Industry Complex

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