Computation
By Paula Fenger. 2020
: Industrial hygiene is a preventive technique that tries to avoid professional illnesses and damage to health caused by several possible toxic agents. The purpose of this study is to simultaneously analyze different risk factors (body vibration, lighting, heat stress and noise), to obtain an overall risk assessment of these factors and to classify them on a scale of levels of Unacceptable, Not recommended or Acceptable. In this work, an artificial intelligence model based on the grey clustering method was applied to evaluate the quality of industrial hygiene. The grey clustering method was selected, as it enables the integration of objective factors related to hazards present in the workplace with subjective employee evaluations. A case study, in the three warehouses of a beer industry in Peru, was developed. The results obtained showed that the warehouses have an acceptable level of quality. These results could help industries to make decisions about conducting evaluations of the different occupational agents and determine whether the quality of hygiene represents a risk, as well as give certain recommendations with respect to the factors presented.
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Peter Adam Thwaites
- L. Varshney
- Charalampos E. Tsourakakis
- Jim Q. Smith
- Eva Riccomagno
- Rui Zhang
- Zhiguo Ding
- Xun Zhou
Citations in the corpus
- Causal analysis with Chain Event Graphs (2010, methodology): “Interpreting the directionality of the edges of the BN as causal leads to the Causal Bayesian Network (CBN), which uses a non-parametric representation based on structural equation models [12,19,21,30].”
- Transporting information and energy simultaneously (2008, background): “As extensions, one might consider a “wideband regime” formulation [25], a multiterminal problem where different users have different energy and information requirements, or a deeper look into reversible decoding [11].”
- Causal identifiability via Chain Event Graphs (2013, methodology): “Suppose we have a CBN which is a valid expression of our causal model, and that Pearl’s Front Door conditions hold.”
- MACH: Fast Randomized Tensor Decompositions (2009, methodology): “Here, we present the results of Achlioptas-McSherry [3] which are used in our work 1 .”