Risk Assessment and Fuzzy Logic as Related to Environmental Science

2015 ◽  
pp. 81-97 ◽  
Author(s):  
Istvan Bogardi ◽  
Andras Bardossy ◽  
M. Dewayne Mays ◽  
Lucien Duckstein
Keyword(s):  
Risk Assessment ◽  
Fuzzy Logic ◽  
Environmental Science

Lightning risk assessment using fuzzy logic

2004 ◽  
Vol 60 (2-4) ◽  
pp. 233-239 ◽  
Author(s):  
Luis E Gallego ◽  
Oscar Duarte ◽  
Horacio Torres ◽  
Mauricio Vargas ◽  
Johny Montaña ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Fuzzy Logic

Supply Chain Risk Assessment Using Fuzzy Logic

2021 ◽  
Author(s):  
Abdelrahman Ibrahim Mostafa ◽  
Abdelrahman Mostafa Rashed ◽  
Yasmin Ashraf Alsherif ◽  
Yomna Nagah Enien ◽  
Menatalla Kaoud ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Fuzzy Logic ◽  
Supply Chain ◽  
Supply Chain Risk

Application of Fuzzy Logic to Safety Risk Assessment of China's Maritime Passages

2012 ◽  
Vol 2273 (1) ◽  
pp. 112-120 ◽  
Author(s):  
Dao-Zheng Huang ◽  
Hao Hu ◽  
Yi-Zhou Li
Keyword(s):  
Risk Assessment ◽  
Fuzzy Logic ◽  
Safety Risk

Risk assessment of cost overrun using fuzzy logic model

2022 ◽  
Author(s):  
G.B.S. Alekhya ◽  
K. Shashikanth ◽  
M. Anjaneya Prasad
Keyword(s):  
Risk Assessment ◽  
Fuzzy Logic ◽  
Logic Model ◽  
Cost Overrun ◽  
Fuzzy Logic Model

A methodology for e-banking risk assessment using fuzzy logic and Bayesian network

2021 ◽  
Vol 19 (3) ◽  
pp. 101-124
Author(s):  
Ako Rita Erhovwo ◽  
Okpako Abugor Ejaita ◽  
Duke Oghorodi
Keyword(s):  
Risk Assessment ◽  
Fuzzy Logic ◽  
Bayesian Network ◽  
Operational Risk ◽  
Assessment Process ◽  
Exposure Level ◽  
Model Assessment ◽  
Assessment Methodology ◽  
Risk Assessment Process ◽  
Risk Assessment Methodology

Risk assessment methodology in general has been around for quite a while, its prominence in the E-banking field is a fairly recent phenomenon. We are at the point where risk assessments are critical to the overall function of banks. Banks are required to assess the processes underlying their operations against potential threats, vulnerabilities, and their potential impact, which helps in revealing the risk exposure level, and the residual risks. Identifying clearly a risk assessment methodology is often the first step of assessing and evaluating risk associated with an organization operation. This paper presents a risk assessment methodology for Ebanking Operational Risk. The proposed risk assessment methodology consists of four major steps: a risk model, assessment approach, analysis approach and a risk assessment process. The main tool of the proposed risk assessment methodology is the risk assessment process. The assessment process gives detailed explanation with respect to which models or techniques may be applied and how they are expressed. In this paper the risk assessment technique is built upon fuzzy logic (FL) concept and Bayesian network (BN). In fuzzy logic, an element is included with a degree of membership. Bayesian network is an inference classifier that is capable of representing conditional independencies. The Bayesian and fuzzy logic–based risk assessment process gives good predictions for risk learning and inference in the E-banking systems. Keywords: Fuzzy logic, Bayesian network, risk assessment methodology, operational risk, Ebanking

scholarly journals Fuzzy logic based risk assessment system giving individualized advice for metabolic syndrome and fatal cardiovascular diseases

2019 ◽  
Vol 27 ◽  
pp. 59-66 ◽  
Author(s):  
Hayriye Korkmaz ◽  
Emre Canayaz ◽  
Sibel Birtane Akar ◽  
Zehra Aysun Altikardes
Keyword(s):  
Risk Assessment ◽  
Metabolic Syndrome ◽  
Fuzzy Logic ◽  
Cardiovascular Diseases ◽  
Assessment System

scholarly journals Applications of Fuzzy Logic in Risk Assessment - The RA_X Case

10.5772/37212 ◽  
2012 ◽  
Author(s):  
Isabel L. ◽  
Mrio Simes-Marques
Keyword(s):  
Risk Assessment ◽  
Fuzzy Logic

Risk assessment for the industrial network based on interval type-2 fuzzy sets

Kybernetes ◽  
2019 ◽  
Vol 49 (3) ◽  
pp. 916-937
Author(s):  
Chao Ren ◽  
Xiaoxing Liu ◽  
Zongqing Zhang
Keyword(s):  
Risk Assessment ◽  
Fuzzy Logic ◽  
Fuzzy Sets ◽  
Risk Evaluation ◽  
Quantitative Risk Assessment ◽  
Content Type ◽  
Industrial Network ◽  
Risk Magnitude ◽  
Interval Type

Purpose The purpose of this paper is to develop a risk evaluation method for the industrial network under high uncertain environment. Design/methodology/approach This paper introduces an extended safety and critical effect analysis (SCEA) method, which takes the weight of each industry in a network into risk assessment. Furthermore, expert experience and fuzzy logic are introduced for the evaluation of other parameters. Findings The proposed approach not only develops weight as the fifth parameter in quantitative risk assessment but also applies the interval type-2 fuzzy sets to depict the uncertainty in the risk evaluation process. The risk rating of each parameter excluding weight is determined by using the interval type-2 fuzzy numbers. The risk magnitude of each industry in the network is quantified by the extended SCEA method. Research limitations/implications There is less study in quantitative risk assessment in the industrial network. Additionally, fuzzy logic and expert experience are expressed in the presented approach. Moreover, different parameters can be determined by different weights in network risk assessment in the future study. Originality/value The extended SCEA method presents a new way to measure risk magnitude for industrial networks. The industrial network is developed in risk quantification by assessing weights of nodes as a parameter into the extended SCEA. The interval type-2 fuzzy number is introduced to model the uncertainty of risk assessment and to express the risk evaluation information from experts.

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