Applications of Fuzzy Modeling in Risk Management

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Fuzzy Sets, Systems and Decision Making".

Deadline for manuscript submissions: 20 June 2024 | Viewed by 9329

Special Issue Editors


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Guest Editor
Institute of Mechatronics and Vehicle Engineering, Donát Bánki Faculty of Mechanical and Safety Engineering, Óbuda University, 1081 Budapest, Hungary
Interests: fuzzy systems; fuzzy decision making; risk assessment; complexity reduction

E-Mail Website
Guest Editor
Institute of Mechatronics and Vehicle Engineering, Donát Bánki Faculty of Mechanical and Safety Engineering, Óbuda University, 1081 Budapest, Hungary
Interests: mathematical modeling of maintenance processes; application of risk management in aviation; application of fuzzy models in maintenance management

Special Issue Information

Dear Colleagues,

Risk management has long been an important area of engineering, environmental and health research, but has become even more of a focus today due to the emergence of the COVID-19 pandemic.   

Among the risk factors, quantitative and qualitative parameters can be observed as well, and these kinds of systems are full of uncertainty and subjectivity in the data and in evaluation process. The above characteristics justify the use of soft computation methods, especially fuzzy logic-based models.

In many cases, real-time risk management is required, where the short reaction time has vital importance. However, due to the large number of risk parameters and the complexity of their context result in the complexity of the model, which should be handled adequately. Consequently, for the application of different reduction techniques, anytime algorithms are essential. 

This Special Issue invites original contributions, new developments of classical results, and advanced topics of high potential for future research and applications in different field of risk management using fuzzy models.

Potential topics include, but are not limited to:

  • Risk management and risk assessment in any field;
  • Multicriteria decision making;
  • Predictive models;
  • Hybrid risk assessment or risk management models;
  • Real-time risk assessment and management;
  • Complexity reduction techniques in fuzzy models.

Dr. Edit Toth-Laufer
Prof. Dr. László Pokorádi
Guest Editors

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Keywords

  • risk management
  • risk assessment
  • fuzzy reasoning
  • multiple criteria decision
  • expert systems
  • fuzzy applications
  • hybrid systems
  • neuro-fuzzy systems
  • uncertain information

Published Papers (5 papers)

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Research

17 pages, 3676 KiB  
Article
Multilevel Fuzzy Inference System for Estimating Risk of Type 2 Diabetes
by Jelena Tašić, Zsófia Nagy-Perjési and Márta Takács
Mathematics 2024, 12(8), 1167; https://doi.org/10.3390/math12081167 - 12 Apr 2024
Viewed by 570
Abstract
In this paper, we present a multilevel fuzzy inference model for predicting the risk of type 2 diabetes. We have designed a system for predicting this risk by taking into account various factors such as physical, behavioral, and environmental parameters related to the [...] Read more.
In this paper, we present a multilevel fuzzy inference model for predicting the risk of type 2 diabetes. We have designed a system for predicting this risk by taking into account various factors such as physical, behavioral, and environmental parameters related to the investigated patient and thus facilitate experts to diagnose the risk of diabetes. The important risk parameters of type 2 diabetes are identified based on the literature and the recommendations of experts. The parameters are scaled and fuzzified on their own universe and, based on the experts’ recommendation, fuzzy inference subsystems are created with 3–4 related risk parameters to calculate the risk level. These sub-systems are then arranged into Mamdani-type inference systems so that the system calculates an aggregated risk level. The overview of the large number of diverse types of risk factors, which may be difficult for specialists and doctors, is facilitated by the proposed system. Full article
(This article belongs to the Special Issue Applications of Fuzzy Modeling in Risk Management)
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18 pages, 358 KiB  
Article
A Fuzzy Entropy-Based Group Consensus Measure for Financial Investments
by József Dombi, Jenő Fáró and Tamás Jónás
Mathematics 2024, 12(1), 4; https://doi.org/10.3390/math12010004 - 19 Dec 2023
Viewed by 578
Abstract
This study presents a novel, fuzzy entropy-based approach to the measurement of consensus in group decision making. Here, the basic assumption is that the decision inputs are the ‘yes’ or ‘no’ votes of group members on a financial investment that has a particular [...] Read more.
This study presents a novel, fuzzy entropy-based approach to the measurement of consensus in group decision making. Here, the basic assumption is that the decision inputs are the ‘yes’ or ‘no’ votes of group members on a financial investment that has a particular expected rate of return. In this paper, using a class of fuzzy entropies, a novel consensus measure satisfying reasonable requirements is introduced for a case where the decision inputs are dichotomous variables. It is also shown here that some existing consensus measures are just special cases of the proposed fuzzy entropy-based consensus measure when the input variables are dichotomous. Next, the so-called group consensus map for financial investments is presented. It is demonstrated that this construction can be used to characterize the level of consensus among the members of a group concerning financial investments as a function of the expected rate of return. Moreover, it is described how a consensus map can be constructed from empirical data and how this map is connected with behavioral economics. Full article
(This article belongs to the Special Issue Applications of Fuzzy Modeling in Risk Management)
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20 pages, 2121 KiB  
Article
An Extended ORESTE Approach for Evaluating Rockburst Risk under Uncertain Environments
by Keyou Shi, Yong Liu and Weizhang Liang
Mathematics 2022, 10(10), 1699; https://doi.org/10.3390/math10101699 - 16 May 2022
Cited by 3 | Viewed by 1493
Abstract
Rockburst is a severe geological disaster accompanied with the violent ejection of rock debris, which greatly threatens the safety of underground workers and equipment. This study aims to propose a novel multi-criteria decision-making (MCDM) approach for evaluating rockburst risk under uncertain environments. First, [...] Read more.
Rockburst is a severe geological disaster accompanied with the violent ejection of rock debris, which greatly threatens the safety of underground workers and equipment. This study aims to propose a novel multi-criteria decision-making (MCDM) approach for evaluating rockburst risk under uncertain environments. First, considering the heterogeneity of rock mass and complexity of geological environments, trapezoidal fuzzy numbers (TrFNs) are adopted to express initial indicator information. Thereafter, the superiority linguistic ratings of experts and a modified entropy weights model with TrFNs are used to calculate the subjective and objective weights, respectively. Then, comprehensive weights can be determined by integrating subjective and objective weights based on game theory. After that, the organísation, rangement et synthèse de données relarionnelles (ORESTE) approach is extended to obtain evaluation results in a trapezoidal fuzzy circumstance. Finally, the proposed approach is applied to assess rockburst risk in the Kaiyang phosphate mine. In addition, the evaluation results are compared with empirical methods and other trapezoidal fuzzy MCDM approaches. Results show that the proposed extended ORESTE approach is reliable for evaluating rockburst risk, and provides an effective reference for the design of prevention techniques. Full article
(This article belongs to the Special Issue Applications of Fuzzy Modeling in Risk Management)
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16 pages, 4544 KiB  
Article
Bipolar Dissimilarity and Similarity Correlations of Numbers
by Ildar Z. Batyrshin and Edit Tóth-Laufer
Mathematics 2022, 10(5), 797; https://doi.org/10.3390/math10050797 - 2 Mar 2022
Cited by 3 | Viewed by 1790
Abstract
Many papers on fuzzy risk analysis calculate the similarity between fuzzy numbers. Usually, they use symmetric and reflexive similarity measures between parameters of fuzzy sets or “centers of gravity” of generalized fuzzy numbers represented by real numbers. This paper studies bipolar similarity functions [...] Read more.
Many papers on fuzzy risk analysis calculate the similarity between fuzzy numbers. Usually, they use symmetric and reflexive similarity measures between parameters of fuzzy sets or “centers of gravity” of generalized fuzzy numbers represented by real numbers. This paper studies bipolar similarity functions (fuzzy relations) defined on a domain with involutive (negation) operation. The bipolarity property reflects a structure of the domain with involutive operation, and bipolar similarity functions are more suitable for calculating a similarity between elements of such domain. On the set of real numbers, similarity measures should take into account symmetry between positive and negative numbers given by involutive negation of numbers. Another reason to consider bipolar similarity functions is that these functions define measures of correlation (association) between elements of the domain. The paper gives a short introduction to the theory of correlation functions defined on sets with an involutive operation. It shows that the dissimilarity function generating Pearson’s correlation coefficient is bipolar. Further, it proposes new normalized similarity and dissimilarity functions on the set of real numbers. It shows that non-bipolar similarity functions have drawbacks in comparison with bipolar similarity functions. For this reason, bipolar similarity measures can be recommended for use in fuzzy risk analysis. Finally, the correlation functions between numbers corresponding to bipolar similarity functions are proposed. Full article
(This article belongs to the Special Issue Applications of Fuzzy Modeling in Risk Management)
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17 pages, 3969 KiB  
Article
FMEA in Smartphones: A Fuzzy Approach
by Esmeralda Kadena, Sinan Koçak, Katalin Takács-György and András Keszthelyi
Mathematics 2022, 10(3), 513; https://doi.org/10.3390/math10030513 - 5 Feb 2022
Cited by 5 | Viewed by 2587
Abstract
Smartphones are attracting increasing interest due to how they are revolutionizing our lives. On the other hand, hardware and software failures that occur in them are continually present. This work aims to investigate these failures in a typical smartphone by collecting data from [...] Read more.
Smartphones are attracting increasing interest due to how they are revolutionizing our lives. On the other hand, hardware and software failures that occur in them are continually present. This work aims to investigate these failures in a typical smartphone by collecting data from a class of people. Concerns have been raised that call into question the efficiency of applied methods for identifying and prioritizing the potential defects. The widely used hybridized engineering method, Fuzzy Failure Mode and Effect Analysis (F-FMEA), is an excellent approach to solving these problems. The F-FMEA method was applied to prioritize the potential failures based on their Severity (S), expected Occurrence (O), and the likelihood of Detectability (D). After collecting failure data from different users on a selected smartphone, two well-known defuzzification methods facing the Risk Priority Number (RPN) in F-FMEA were applied. Despite this interest, to the best of our knowledge, no one has studied smartphone failures with a technique that combines the results of different fuzzy applications. Thus, to combine the results of the derived fuzzy subsystems for the average value, we suggest a summative defuzzification method. Our findings indicate that F-FMEA with a summative defuzzification procedure is a clear improvement on the F-FMEA method. Even though the summation method modifies close results of the defuzzification one, it was shown that it provides more accurate results. Full article
(This article belongs to the Special Issue Applications of Fuzzy Modeling in Risk Management)
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