A Bayesian Network under Strict Chain Model for Computing Flow Risks in Smart City

A Bayesian Network under Strict Chain Model for Computing Flow Risks in Smart City

Risk management is a key factor for smart city running. There are many risk events in a strict process like transportation management of a smart city or a medical surgery in a smart hospital, and every step may lead to one kind of risk or more. In view of the fact that the occurrence of the flow ris...

Full description

Bibliographic Details
Main Authors: Zengfanxiang Wei, Lei Zhang, Qi Yue, Muchen Zhong
Format: Article
Language:English
Published: Hindawi-Wiley 2020-01-01
Series:Complexity
Online Access:http://dx.doi.org/10.1155/2020/5920827
id doaj-1ec9d2d4d82e4f2ea4064090680cc720
record_format Article
spelling doaj-1ec9d2d4d82e4f2ea4064090680cc7202020-11-25T03:14:56ZengHindawi-WileyComplexity1076-27871099-05262020-01-01202010.1155/2020/59208275920827A Bayesian Network under Strict Chain Model for Computing Flow Risks in Smart CityZengfanxiang Wei0Lei Zhang1Qi Yue2Muchen Zhong3School of Economics and Management, Beijing Jiaotong University, Beijing 100044, ChinaSchool of Economics and Management, Beijing Jiaotong University, Beijing 100044, ChinaSchool of Information Management, Jiangxi University of Finance and Economics, Nanchang 330013, ChinaDepartment of Cyber Security, Lancaster University, Lancaster LA2 0PF, Lancaster, UKRisk management is a key factor for smart city running. There are many risk events in a strict process like transportation management of a smart city or a medical surgery in a smart hospital, and every step may lead to one kind of risk or more. In view of the fact that the occurrence of the flow risks follows the sequence formed by each process step, this paper presents a Bayesian network under strict chain (BN_SC) to model this situation. In this model, the probabilistic reasoning formula is given according to the sequence of process steps, and the probabilities given by the model can do risk factor analysis to support the system to find an effective way to improve the process like machine manufacturing or a medical surgery. Finally, an example is analyzed based on the information given by doctors according to the situation of LC in their hospital located in Sichuan Province of China, which shows the effectiveness and rationality of the proposed BN_SC model.http://dx.doi.org/10.1155/2020/5920827
collection DOAJ
language English
format Article
sources DOAJ
author Zengfanxiang Wei
Lei Zhang
Qi Yue
Muchen Zhong
spellingShingle Zengfanxiang Wei
Lei Zhang
Qi Yue
Muchen Zhong
A Bayesian Network under Strict Chain Model for Computing Flow Risks in Smart City
Complexity
author_facet Zengfanxiang Wei
Lei Zhang
Qi Yue
Muchen Zhong
author_sort Zengfanxiang Wei
title A Bayesian Network under Strict Chain Model for Computing Flow Risks in Smart City
title_short A Bayesian Network under Strict Chain Model for Computing Flow Risks in Smart City
title_full A Bayesian Network under Strict Chain Model for Computing Flow Risks in Smart City
title_fullStr A Bayesian Network under Strict Chain Model for Computing Flow Risks in Smart City
title_full_unstemmed A Bayesian Network under Strict Chain Model for Computing Flow Risks in Smart City
title_sort bayesian network under strict chain model for computing flow risks in smart city
publisher Hindawi-Wiley
series Complexity
issn 1076-2787
1099-0526
publishDate 2020-01-01
description Risk management is a key factor for smart city running. There are many risk events in a strict process like transportation management of a smart city or a medical surgery in a smart hospital, and every step may lead to one kind of risk or more. In view of the fact that the occurrence of the flow risks follows the sequence formed by each process step, this paper presents a Bayesian network under strict chain (BN_SC) to model this situation. In this model, the probabilistic reasoning formula is given according to the sequence of process steps, and the probabilities given by the model can do risk factor analysis to support the system to find an effective way to improve the process like machine manufacturing or a medical surgery. Finally, an example is analyzed based on the information given by doctors according to the situation of LC in their hospital located in Sichuan Province of China, which shows the effectiveness and rationality of the proposed BN_SC model.
url http://dx.doi.org/10.1155/2020/5920827
work_keys_str_mv AT zengfanxiangwei abayesiannetworkunderstrictchainmodelforcomputingflowrisksinsmartcity
AT leizhang abayesiannetworkunderstrictchainmodelforcomputingflowrisksinsmartcity
AT qiyue abayesiannetworkunderstrictchainmodelforcomputingflowrisksinsmartcity
AT muchenzhong abayesiannetworkunderstrictchainmodelforcomputingflowrisksinsmartcity
AT zengfanxiangwei bayesiannetworkunderstrictchainmodelforcomputingflowrisksinsmartcity
AT leizhang bayesiannetworkunderstrictchainmodelforcomputingflowrisksinsmartcity
AT qiyue bayesiannetworkunderstrictchainmodelforcomputingflowrisksinsmartcity
AT muchenzhong bayesiannetworkunderstrictchainmodelforcomputingflowrisksinsmartcity
_version_ 1715268457279258624

Similar Items