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- W3128569762 abstract "Aim at the occurrence of security accidents, emergency evacuation has become an important means that cannot be ignored. At present, most of the risk assessment studies are focused on accident analysis, while only a few studies conduct risk assessments on evacuation issues. Without the basis for risk assessment, it is easy to ignore some important factors when formulating an evacuation emergency plan, resulting in lacking of science in the evacuation plan and even a negative impact in actual implementation. Therefore, the research on risk assessment of evacuation is a very significant research direction. In addition, with the advent of the era of big data and the development of artificial intelligence, the data required for risk assessment is also increasing, and traditional risk assessment methods are difficult to deal with these huge amounts of data. Therefore, the use of deep learning methods into risk assessment and deal with this problem is also one trend in future. The major objective of this study was to apply deep learning method to evacuation risk assessment, and give a framework of risk assessment in evacuation based on convolutional neural networks." @default.
- W3128569762 created "2021-02-15" @default.
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- W3128569762 date "2020-11-06" @default.
- W3128569762 modified "2023-09-27" @default.
- W3128569762 title "Research Framework of Risk Assessment in Evacuation Based on Deep Learning" @default.
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- W3128569762 doi "https://doi.org/10.1109/cac51589.2020.9326940" @default.
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