A MCMEIF-LT model for risk assessment based on linguistic terms and risk attitudes-Reference-Cited by-同舟云学术

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A MCMEIF-LT model for risk assessment based on linguistic terms and risk attitudes

Published:2020-06-02 Issue:10 Volume:50 Page:3318-3335
ISSN:0924-669X
Container-title:Applied Intelligence
language:en
Short-container-title:Appl Intell

Author:

Tian Donghong^ORCID,Min Chao,Li Lingna,Gao Jie

Funder

Southwest Petroleum University

Science and Technology Innovation Team of Education Department of Sichuan

Program of Science and Technology of Sichuan Province

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence

Link

https://link.springer.com/content/pdf/10.1007/s10489-020-01737-w.pdf

Reference49 articles.

1. Markowski AS, Mannan MS (2008) Fuzzy risk matrix. J Hazard Mater 159(1):152–157

2. Ni HH, Chen A, Chen N (2010) Some extensions on risk matrix approach. Saf Sci 48(10):1269–1278

3. Abrilaba D (2012) Improving record linkage with supervised learning for disclosure risk assessment. Information Fusion 13(4):274–284

4. Ruan X, Yin ZY, Dan MF (2015) Risk matrix integrating risk attitudes based on utility theory. Risk Analysis 35(8):1437– 1447

5. Tian DH, Yang BW, et al. (2018) A multi-experts and multi-criteria risk assessment model for safety risks in oil and gas industry integrating risk attitudes. Knowl.-Based Syst 156:62–73

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2. A two stage risk assessment model based on interval-valued fuzzy numbers and risk attitudes;Engineering Applications of Artificial Intelligence;2022-09

3. An Improved Ordered Visibility Graph Aggregation Operator for MADM;IEEE Access;2022

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