Modeling noise annoyance using Bayesian networks: A case study of an industrial factory.

Background: Noise annoyance is a detrimental cognitive response elicited by noise exposure. Precisely identifying primary risk factors plays a vital role in effectively preventing and managing this issue.

Objective: This study aimed to develop a comprehensive Bayesian network model to predict noise annoyance, by identifying main contributing factors.

A risk model for occupational noise-induced hearing loss in workers.

Background: Occupational hearing loss is one of the most common work-related diseases with various risk factors and considerable negative impacts on both physical and mental well-being of affected workers. Occupational noise-induced hearing loss (ONIHL) has a complex interaction with personal, environmental and occupational factors.

Objective: This study aimed to develop a risk model for ONIHL in workers by identifying risk factors and their interactions.