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Analysis of chemical exposure through inhalation using hybrid neural network

机译:用混合神经网络吸入通过吸入分析

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In this analysis, human health risk through inhalation due to exposure to Benzene from vehicular emissions in New Zealand is assessed as an example of the application of a hybrid neural network. Exposure factors affecting the inhalation are inhaled contaminant, age, body weight, health status and activity patterns of humans. There are four major variables affecting the inhaled contaminant viz., gas emissions from motor vehicles on the road, wind speed, temperature and atmospheric stability. The topic of uncertainty applies equally to all variables involved in exposure analysis. Neural network and fuzzy theory is implemented to solve the uncertainty, which exists to a greater extent. The architecture of hybrid neural network that is used to estimate the exposure of carcinogens through inhalation is explained in detail in this paper.
机译:在这种分析中,通过在新西兰的车辆排放引起的苯由于暴露于苯而通过吸入的人体健康风险被评估为混合神经网络的应用示例。影响吸入的暴露因子是吸入污染物,年龄,体重,健康状况和人类的活动模式。有四个主要变量影响吸入污染物的cont v,来自汽车的机动车辆的气体排放,风速,温度和大气稳定性。不确定性的主题同样适用于曝光分析中涉及的所有变量。实施神经网络和模糊理论以解决不确定性,在更大程度上存在。本文详细说明了用于通过吸入估计致癌致癌致癌的混合神经网络的结构。

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