A Bayesian network model for the Asian Games fire risk assessment

Author(s):  
Song Lu ◽  
Dan Wu ◽  
Shi-chang Lu ◽  
He-ping Zhang
Author(s):  
Jingjing Pei ◽  
Guantao Wang

The Bayesian network method is introduced into the process of fire risk quantitative assessment. The event tree model is established, and the Bayesian network model is transformed from the event tree model based on the typical fire scenarios in high-rise space. A Bayesian fire risk assessment algorithm for high-rise buildings based on mutual information reliability is proposed. Bayesian network is modified considering the influence of uncertainties. Finally, the modified Bayesian network model is used to calculate the probability of fire developing to different stages, and the estimated value of property loss is used to express the severity of the accident and calculate the fire risk value. The results show that the existence of uncertainties has a significant impact on the results of risk assessment; the quantitative assessment method based on Bayesian network is better than the ETA method based on event tree analysis in dealing with uncertainties and is more suitable for high-rise space fire risk assessment.


2018 ◽  
Vol 275 ◽  
pp. 2525-2554 ◽  
Author(s):  
Madjid Tavana ◽  
Amir-Reza Abtahi ◽  
Debora Di Caprio ◽  
Maryam Poortarigh

Author(s):  
Kristian Herland ◽  
Heikki Hämmäinen ◽  
Pekka Kekolahti

This study comprises an information security risk assessment of smartphone use in Finland using Bayesian networks. The primary research method is a knowledge-based approach to build a causal Bayesian network model of information security risks and consequences. The risks, consequences, probabilities and impacts are identified from domain experts in a 2-stage interview process with 8 experts as well as from existing research and statistics. This information is then used to construct a Bayesian network model which lends itself to different use cases such as sensitivity and scenario analysis. The identified risks’probabilities follow a long tail wherein the most probable risks include unintentional data disclosure, failures of device or network, shoulder surfing or eavesdropping and loss or theft of device. Experts believe that almost 50% of users share more information to other parties through their smartphones than they acknowledge or would be willing to share. This study contains several implications for consumers as well as indicates a clear need for increasing security awareness among smartphone users.  


2014 ◽  
pp. 725-735
Author(s):  
Panagiota Papakosta ◽  
Anke Scherb ◽  
Kilian Zwirglmaier ◽  
Daniel Straub

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