International archives of occupational and environmental health
25432298
PURPOSE: Prediction of hearing loss in noisy workplaces is considered to be an important aspect of hearing conservation program. Artificial intelligence, as a new approach, can be used to predict the complex phenomenon such as hearing loss. Using art...
The Journal of the Acoustical Society of America
31046337
The ISO-1999 [(2013). International Organization for Standardization, Geneva, Switzerland] standard is the most commonly used approach for estimating noise-induced hearing trauma. However, its insensitivity to noise characteristics limits its practic...
OBJECTIVES: To demonstrate the feasibility of developing machine learning models for the prediction of hearing impairment in humans exposed to complex non-Gaussian industrial noise.
Archives of orthopaedic and trauma surgery
35507089
The aim of the study was to examine the noise exposure for operating theater staff during total knee arthroplasty (TKA) with three different robot systems. There is already evidence that noise exposure during TKA performed manually exceeds recommende...
BACKGROUND: Quantitatively analyzing the impact of UV radiation and noise during welding operations is essential to assess the exposure, identify potential hazards, and develop targeted safety protocols to ensure worker safety and adherence to safety...
OBJECTIVES: This study aims to predict the risk of noise-induced hearing loss (NIHL) through a back-propagation neural network (BPNN) model. It provides an early, simple and accurate prediction method for NIHL.
OBJECTIVES: This study aimed to determine which machine learning model is most suitable for predicting noise-induced hearing loss and the effect of tinnitus on the models' accuracy.
BACKGROUND:  Given the high prevalence of hearing loss among truck drivers, using artificial neural networks (ANNs) to predict and detect contributing factors early can aid managers significantly.
This study aimed to explore the combined impacts of occupational noise and dust on hearing and extra-auditory functions and identify associated risk factors via machine learning techniques. Data from 14,145 workers (627 with occupational noise-induce...
International journal of occupational safety and ergonomics : JOSE
39387183
This study investigates the evaluation of risks faced by employees in a selected large-scale apparel mill using a risk assessment method with a fuzzy logic approach. The study found that risk assessment in the apparel industry is more accurate and re...