Fuzzy Measure Research Articles

Location Selection of Electric Vehicle Charging Stations Through Employing the Spherical Fuzzy CoCoSo and CRITIC Technique

Energy conservation and emission reduction are important policies vigorously promoted in China. With the continuous popularization of the concept of green transportation, electric vehicles have become a green transportation tool with good development prospects, greatly reducing the pressure on the environment and resources caused by rapid economic growth. The development status of electric vehicles has a significant impact on urban energy security, environmental protection, and sustainable development in China. With the widespread application of new energy vehicles, charging piles have become an important auxiliary infrastructure necessary for the development of electric vehicles. They have significant social and economic benefits, so it is imperative to build electric vehicle charging piles. There are many factors to consider in the scientific layout of electric vehicle charging stations, and the location selection problem of electric vehicle charging stations is a multiple-attribute group decision-making (MAGDM) problem. Recently, the Combined Compromise Solution (CoCoSo) technique and CRITIC technique have been utilized to deal with MAGDM issues. Spherical fuzzy sets (SFSs) can uncover the uncertainty and fuzziness in MAGDM more effectively and deeply. In this paper, on basis of CoCoSo technique, a novel spherical fuzzy number CoCoSo (SFN-CoCoSo) technique based on spherical fuzzy number cosine similarity measure (SFNCSM) and spherical fuzzy number Euclidean distance (SFNED) is conducted for dealing with MAGDM. Moreover, when the attribute weights are completely unknown, the CRITIC technique is extended to SFSs to acquire the attribute weights based on the SFNCSM and SFNED. Finally, the SFN-CoCoSo technique is utilized for location selection problem of electric vehicle charging stations to prove practicability of the developed technique and compare the SFN-CoCoSo technique with existing techniques to further demonstrate its superiority.

Using fuzzy similarity measure in content-based video retrieval based on image query

Using fuzzy similarity measure in content-based video retrieval based on image query

ОЦЕНКА НЕОПРЕДЕЛЕННОСТИ И ИНСТРУМЕНТЫ ДЛЯ ОПТИМИЗАЦИИ РЕГИОНАЛЬНОЙ НАЛОГОВОЙ ПОЛИТИКИ И ПРИМЕНЕНИЯ РЕГУЛЯРИЗИРУЮЩЕГО БАЙЕСОВСКОГО ПОДХОДА

Measurement uncertainty is a quantitative measure of the quality of measurement results, without which they cannot be compared with each other, with specified reference values or with a reference point. Uncertainty assessment is essential to ensure metrological traceability of measurement results and to ensure accuracy and reliability. In addition, measurement uncertainty should be taken into account whenever a decision is made based on measurement results, for example in acceptance/rejection or transmission, deviation processes. The purpose of this article is to consider the main aspects of uncertainty in measurements, as well as to study the basic principles of fuzzy and soft measurements in relation to information assessment issues in terms of optimizing regional tax policy and applying the regularizing Bayesian approach.

Exploring the development of athletic interests of students in physical education colleges and universities based on cluster analysis: physical education athletic training

Abstract Sports interest is the internal motivation for students in physical education colleges and universities to participate in sports training enthusiastically. Based on the definition of fuzzy set and fuzzy clustering uncertainty measure, this paper utilizes the fuzzy C-mean clustering algorithm to evaluate and analyze students’ athletic training performance data by clustering, and based on the results of the analysis, it formulates the physical education teaching program based on the development of students’ athletic interest. At the same time, taking the physical education course of grade 2022 in College Q as an example, the IPA quadrant diagram was used to analyze the ‘importance-satisfaction’ of physical education teaching. The results showed that in the IPA quadrant, the five factors of teachers’ professional quality [4.89,4.13], teachers’ ability to explain [4.56,4.16], mastery of movement [4.64,4.03], novel content of teaching materials [4.46,3.99] and good image of the teacher [4.42,4.19] were in the region of high importance-satisfaction, which indicated that the continuation of these factors could improve students’ satisfaction of physical education teaching. The role can increase students’ interest in sports and overall satisfaction with physical education sports training. From the side, it verifies the feasibility of the teaching strategy proposed in this paper. It provides data support for the in-depth implementation of quality education and the cultivation of moral, intellectual, physical, and aesthetic talents of all-round development.

Interval-valued Pythagorean fuzzy entropy and its application to multi-criterion group decision-making

<abstract><p>Entropy is an important tool of information measurement in the fuzzy set and its inference. The research on information measurement based on interval-valued Pythagorean fuzzy sets mostly involves the distance formula for interval-valued Pythagorean fuzzy numbers, but seldom involves the measurement of fuzziness. In view of this situation, we have aimed to propose new interval-valued Pythagorean fuzzy entropy and weighted exponential entropy schemes. Based on the interval-valued Pythagorean fuzzy weighted averaging operator, a strategy based on weighted exponential entropy is proposed to solve the multi-criteria group decision-making (MCGDM) problem in the interval-valued Pythagorean environment. Two examples illustrate that this paper provides a feasible new method to solve the MCGDM problem in an interval-valued Pythagorean fuzzy (IVPF) environment. Finally, by comparing with the existing methods, it is concluded that the entropy measure of IVPF schemes and the corresponding MCGDM method can select the optimal solution of the practical problem more precisely and accurately. Therefore, the comparative analysis shows that the proposed measurement method has the characteristics of flexibility and universality.</p></abstract>

Algoritmi za procenu kvalitativnih i kvantitativnih rizika kreditiranja malih i srednjih preduzeća zasnovani na fazi računu

Today, crisis conditions in the economy and finance require high-quality risk assessment. In the article, the authors propose two algorithms for assessing the projects lending risks (PLRs) to small and medium-sized businesses. To assess qualitative PLRs, we proposed to use a hierarchical system of criteria, in which the importance of the criteria is described using the Sugeno fuzzy measure, and the generalized estimate of the qualitative risk is calculated using the Sugeno fuzzy integral. To evaluate quantitative PLRs, we proposed to use the characteristics of fuzzy numbers that describe the project effectiveness criteria and have an arbitrary-form membership function. In addition, to describe quantitative risks, we proposed to use the risk-function of a fuzzy number, which reflects not only the size of possible losses, but also the possibility of their occurrence. This allows you to comprehensively and objectively assess the level of risks. We have demonstrated and discussed this algorithms on the example of preparing data for making a decision on lending to a project for the production of corn syrup in Ukraine.

Balanced capacities

We consider capacity (fuzzy measure, non-additive probability) on a compactum as a monotone cooperative normed game. Then it is natural to consider probability measures as elements of core of such game. We prove a topological version of the Bondareva-Shapley theorem that non-emptiness of the core is equivalent to balancedness of the capacity. We investigate categorical properties of balanced capacities and give characterizations of some fuzzy integrals of balanced capacities.

Linear Diophantine Fuzzy Sets: Image Edge Detection Techniques based on Similarity Measures

In the digital imaging process, fuzzy logic provides many advantages, including uncertainty management, adaptability to variations, noise tolerance, and adaptive classification. One of the techniques of digital image processing is the edge detection. The edge detection process is an essential tool to segment the foreground objects from the image background. So, it facilitates subsequent analysis and comprehension of the image’s underlying structural properties. This process can be moved on with the notion of fuzzy sets and their generalizations. The concept of Linear Diophantine fuzzy sets is a generalization of fuzzy sets where reference parameters correspond to membership and non-membership grades. This study aims to apply linear Diophantine fuzzy sets (LDFSs) to edge detection of images. The novelty of this paper is twofold. The first one is that we conduct a comprehensive evaluation to ascertain the similarity values using the linear Diophantine fuzzy similarity measure by leveraging the gray normalized membership values associated with fundamental edge detection techniques. The other is to modify the image pixels into the LDFSs and then filter the images by using the presented similarity measure operators given in the LDFS environment.

A Fuzzy Entropy-Based Group Consensus Measure for Financial Investments

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.

Convergence of random elements via fuzzy integrals

Can convergence of random variables, random vectors or, more generally, random elements of a metric space be studied using fuzzy integrals, in particular the seminormed integral? Among the positive results we present, it is shown that for large families of t-seminorms convergence in distribution and convergence in probability can be characterized via seminormed integrals. This suggests that, while fuzzy integrals were devised for working with non-additive fuzzy measures, some of their theoretical properties within the realm of probability measures can be comparable to those of the Lebesgue integral.

A FUZZY MEASURE THEORY BASED PROBE INTO A SPATIAL VARIATON ON SURFACE TEMPERATURE OVER INDIA

Abstract. This study used time series and fuzzy measures to comprehend the surface temperature over Northeast India (NEI), Northwest India (NWI), and Central India (CI). We presented a technique for calculating the uncertainty surrounding surface temperature data related to the Indian summer monsoon from 1901–2007. We utilized the Dempster-Shafer Theory to produce belief measures once the random variables had been standardized. We used two judging criteria and derived the joint belief measures for all the cases. This approach gave us some insight into the uncertainties associated with the three-time series taken from three spatially separated zones. It was confirmed that the strength of the evidence across a longer time horizon is a more predictable parameter.

Extraction of evolutionary factors in smart manufacturing systems with heterogeneous product preferences and trust levels

Smart products, as an increasingly significant part of smart manufacturing systems, have brought users extreme experiences. To provide a significantly better user experience and succeed amidst intense market competition, smart products undergo continuous and iterative evolution guided by diverse user requirements. However, users' requirements are heterogeneous, ever-changing, and misleading owing to the heterogeneous familiarity and deceptive interactions, leading to an erroneous identification of product evolutionary factors and inferior subsequent evolution. In existing works, the heterogeneous preferences and trust levels of users are considered superficially, and the extracted product evolutionary factors fail to adequately fulfill users' requirements. To address this issue, a reliable extraction method for evolutionary factors of smart products with heterogeneous product preferences and trust levels is proposed. First, the fuzzy similarity measure is introduced and a group classification method based on this measure is designed to filter out users without reference value. Second, the trust score induced order weighted averaging (TS-IOWA) operator is employed to obtain the comprehensive trust level of an individual user. Third, the dual trust aggregating operator is improved by two corrective factors being incorporated with the Einstein product and the Einstein sum to overcome the deficiency: an over-reduction of trust level and an over-expansion of distrust level, and then employed to aggregate multiple trust/distrust values and sort the evolutionary factors. Finally, two case studies on the App rating system and the evolution of smart wristbands are provided to verify the effectiveness of this proposed method. Throughout the analysis, the ranking accuracy of users' trust levels has increased by 25%, and the matching ratio between the extraction results and user requirements has increased from 57.14% to 85.7% with an improvement of 50%. The results demonstrate that the proposed method is capable of obtaining more credible evolutionary factors of smart products.

A New Fuzzy Joint Choquet Integral Method Under Interval-Valued Function

A new fuzzy group decision-making method considering multi-attributes correlation under interval-valued function is presented, which mainly includes (1) acquiring the group fuzzy preference matrix and (2) handling the interactions between multiple evaluation attributes. To do that, firstly, the fuzzy joint Choquet integral based on an interval-valued function is proposed, which not only reflects the interaction between multiple attributes in a complex and uncertain environment, but also retains the initial preference of the decision maker. Secondly, a Shapley value with fuzzy measure is applied to assign each decision maker's weight, and the fuzzy group preference matrix is acquired by fusing the fuzzy preference matrices of all decision makers. Finally, a nursing home selection case is depicted to explain the effectiveness of the proposed technique. The corresponding sensitivity analysis is operated, which clarifies the reliability and flexibility of the proposed technique.

Technology adoption theories towards environmentally sustainable pharma 4.0: a rational selection approach

PurposeThis study is centered on the identification of the most appropriate Technology Adoption (TA) model for investigating the adoption of Industry 4.0 technologies within pharmaceutical and related enterprises. The aim is to facilitate a smooth transition to advanced technologies while concurrently achieving environmental sustainability.Design/methodology/approachSelection of a suitable TA theory is carried out using a hybrid multi-criteria decision-making (MCDM) approach incorporating PIvot Pairwise RElative Criteria Importance Assessment (PIPRECIA) and Fuzzy Measurement of alternatives and ranking according to Compromise solution (F-MARCOS) methods. A group of three experts is formulated for the ranking of criteria and alternatives based on those criteria.FindingsThe results indicate that out of all six TA models considered unified theory of acceptance and use of technology (UTAUT) model gets the highest utility function value, followed by the technical adoption model (TAM). Further, sensitivity analysis is conducted to confirm the validity of the MCDM model employed.Research limitations/implicationsChallenging times like COVID-19 pointed out the importance of technology in the pharmaceutical and healthcare sectors. TA studies in this area can help in the identification of critical factors that can assist pharmaceutical firms in their efforts to embrace emerging technologies, enhance their outputs and increase their efficiency.Originality/valueThe novelty of this research lies in the fact that the utilization of a TA theory prior to its implementation has not been witnessed in existing scholarly literature. The utilization of a TA theory, specifically within the pharmaceutical industry, can assist enterprises in directing their attention toward pertinent factors when contemplating the implementation of emerging technologies and achieving sustainable development.

Distributivity of a Uni-nullnorm with Continuous and Archimedean Underlying T-norms and T-conorms Over an Arbitrary Uninorm

ABSTRACT The issue of distributivity of aggregation operators is crucial for many different areas such as fuzzy sets and fuzzy logic, pseudo-analysis and measure theory, and particulary in the decision making theory. The problem of distributivity of an operator form a special class of uni-nullnorms over a general uninorm is being addressed through this paper. The class in question consists of uni-nullnorms with continuous and Archimedean underlying t-norms and t-conorms, and the results presented here are a natural continuation and extension of some previous works with an emphasis on a much wider class of uninorms as inner operators.

Research on decision method based on probability hesitation fuzzy comprehensive distance measure

Aiming at the defects of the existing probabilistic hesitation fuzzy distance measures, which require the number of membership degree to be consistent and the order to be rearranged, a probabilistic hesitation fuzzy multi-attribute decision making method based on the comprehensive characteristic distance measure is proposed. First, a new law of probability hesitant fuzzy number comparison is defined. Then four characteristics of aggregation, discreteness, fuzziness and consistency are defined, and a new comprehensive distance measure is defined based on definitions above. Finally, on the basis of traditional TODIM method and prospect theory, the validity and rationality of the distance measure and new probability hesitant fuzzy recognition method in this paper are verified by examples and comparative analysis.

A Robust Multilabel Feature Selection Approach Based on Graph Structure Considering Fuzzy Dependency and Feature Interaction

The performance of multilabel learning depends heavily on the quality of the input features. A mass of irrelevant and redundant features may seriously affect the performance of multilabel learning, and feature selection is an effective technique to solve this problem. However, most multilabel feature selection methods mainly emphasize removing these useless features, and the exploration of feature interaction is ignored. Moreover, the widespread existence of real-world data with uncertainty, ambiguity, and noise limits the performance of feature selection. To this end, our work is dedicated to designing an efficient and robust multilabel feature selection scheme. First, the distribution character of multilabel data is analyzed to generate robust fuzzy multi-neighborhood granules. By exploring the classification information implied in the data under the granularity structure, a robust multilabel <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -nearest neighbor fuzzy rough set model is constructed, and the concept of fuzzy dependency is studied. Second, a series of fuzzy multi-neighborhood uncertainty measures in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -nearest neighbor fuzzy rough approximation spaces are studied to analyze the correlations of feature pairs, including interactivity. Third, by investigating the uncertainty measure between feature and label, between features, multilabel data is modeled as a complete weighted graph. Then, these vertices are assessed iteratively to guide the assignment of feature weights. Finally, a graph structure-based robust multilabel feature selection algorithm (GRMFS) is designed. The experiments are conducted on fifteen multilabel datasets. The results verify the superior performance of GRMFS as compared with nine representative feature selection methods.

A risk-based decision making framework to analyze the properties of cobalt–chromium alloys

In this study, the properties of cobalt–chromium alloys are systematically prioritized to aid in the minimization of orthopedic implant failures and risks in biomedical applications. Within the model, six main groups (including a total of 31 properties) are defined: economic aspects, design and production properties, mechanical properties, physical properties, chemical properties and biological properties. A risk-based fuzzy decision making framework is proposed for prioritization. First, a risk-management decision matrix is created by employing interval type-2 fuzzy failure modes and effects analysis. Afterward, the properties of cobalt–chromium alloys are analyzed by utilizing interval type-2 fuzzy measurement of alternatives and ranking according to compromise solution. In the last phase, a prediction model is devised with an adaptive network fuzzy inference system to save computational time and effort and to enable the incorporation of new scientific results into the biomaterial evaluation process. The results of the current study demonstrate that compatibility, osseointegration, corrosion resistance, fatigue resistance and time-dependent deformation are the top five properties contributing to potential orthopedic implant failures and risks. Furthermore, the developed model produces very satisfactory results with acceptable deviations. Consequently, this study presents a new and reliable guide for the unbiased evaluation of cobalt–chromium alloys.

Z-mixture three-sided stable matching in seaborne coal exchange with cooperative partners and heterogeneous relationships among attributes

Three-sided matching decision-making (TSMDM) problems are widespread in the real world. In practical TSMDM scenarios, two of the three sides collaborate, and matching objects on each side give their assessments about matching objects on another side according to multi-attributes with heterogeneous relationships. Given its advantages in describing uncertain information and its credibility, Z-mixture number is utilized to represent assessment from matching object, which offers continuous variable or discrete variable leaves matching objects free to choose. Based on this, this paper constructs a multiple attribute TSMDM model with Z-mixture numbers to deal with such situations. First, the prioritized interactive aggregation operator is proposed to aggregate Z-mixture numbers, which takes the prioritization and various interrelationships among the input arguments into account. Second, a weight determination model based on maximal deviation and fuzzy measure is proposed to determine the importance of attribute coalitions. Furthermore, a three-sided stable matching model with cooperation which pursues the maximization of overall satisfaction of matching objects by considering the stable matching conditions is constructed to obtain the stable matching among matching objects. Lastly, a case for the matching of supply, transportation and demand in seaborne coal exchange is presented to validate the rationality and practicability of the proposed method. Compared with the existing studies, the advantages and effectiveness of the proposed method are demonstrated.

Retracted: Based on Fuzzy Measure Algorithm Message Adaptive Rate Algorithm of Internet of Things

Retracted: Based on Fuzzy Measure Algorithm Message Adaptive Rate Algorithm of Internet of Things