Fuzzy Sets Research Articles

Product ranking considering differences across online review platforms: a method based on intuitionistic fuzzy soft sets

Numerous online review platforms have become flooded with a large number of textual reviews, allowing consumers to compare and select products based on online reviews from multiple platforms. Existing studies have found that there may be certain differences in the content focus of online reviews from different platforms and that the evaluation indicators of different platforms may be different. Ranking products by considering differences in platforms and integrating online reviews from multiple platforms is of great significance in supporting consumer purchase decisions; however, relevant studies are relatively scarce. Therefore, this study proposes a product ranking method using intuitionistic fuzzy soft sets, considering the differences in platforms. First, online reviews were preprocessed to obtain product indicator parameters on different platforms. Second, a sentiment analysis algorithm was used to determine the sentiment strengths of the indicator parameters. Subsequently, an intuitionistic fuzzy soft set for each platform concerning the indicator parameters was constructed. Furthermore, a comprehensive intuitionistic fuzzy soft set was obtained by the AND operation, and the ranking of the products was determined. Finally, a case study was conducted to illustrate the use of the proposed method. Through a comparative analysis, the characteristics and advantages of the proposed method were further illustrated.

Similarity Measures of Probabilistic Interval Preference Ordering Sets and Their Applications in Decision-Making

The concept of probabilistic interval preference ordering sets (PIPOSs) provides a scientific and intuitive framework for solving real-life multi-criteria group decision-making problems. In some areas such as investment decision-making and supplier selection, PIPOSs have a wider application space, and the development of similarity and distance measures based on PIPOSs holds great significance. Similarity measure is a basic and prominent tool for dealing with imperfect and ambiguous information in fuzzy sets, but it can also be used to deal with uncertain information in preference ordering. These metrics play an important role in the actual decision-making process, as they effectively quantify the degree of similarity between two PIPOSs, and further allow for the prioritization of different scenarios. In this article, we sort out the definitions and arithmetic rules of PIPOSs, and creatively propose several new similarity measures based on PIPOSs. Then, we propose a group decision-making method based on similarity measures and conduct a comparative study with three existing similarity measures to illustrate its advantages over existing metrics. Finally, we confirm its validity through numerical illustrations in the case study, and also conduct a comparative assessment to verify the scientific validity and effectiveness of the newly introduced measure against the existing metrics.

MCDM Method for Managing the Water Resources Based on Possibility Theory under Bipolar Fuzzy Environment

Background: Uncertainty is a common factor in every real-life decision-making problem. Possibility theory is one uncertainty theory in Fuzzy sets (FS). The possibility-based decision-making under a fuzzy environment is a significant multi-criteria decision-making (MCDM) method. Methods: A bipolar FS is an extension of a fuzzy set. With the bipolarity concept, we can handle both positive and negative thoughts. In this study, we have provided a possibility mean of a bipolar fuzzy number. We have developed a ranking method for bipolar fuzzy numbers using this pos-sibility concept. A novel possibility MCDM method is suggested for solving the water resources management (WRM) in the Nagpur area, Maharashtra State, India. Results: The MCDM technique is an effective tool for solving WRM problems in an area. Many uncertainties and bipolarities occur together in Nagpur water resources. WRM technique with fuzzy is one approach that can be used to solve the area's water problem. We have used the pro-posed MCDM to address the water-related issues of this district. With this proposed MCDM method, numerically, we employed water resource problems under a bipolar fuzzy environ-ment. Conclusion: The Nagpur area is covered by Basaltic rock and faces water shortage. The district is experiencing severe water shortages. Groundwater, surface water, and rainfall are three water resources considered as alternatives. According to the proposed MCDM technique in Nagpur district, Groundwater is the best water source from three flanks: quality of water, affordability, and availability.

A novel algorithmic approach to model uncertainties associated with sustainable supply chain management using complex spherical fuzzy soft settings

A novel algorithmic approach to model uncertainties associated with sustainable supply chain management using complex spherical fuzzy soft settings

How does brand authenticity influence brand loyalty? Exploring the roles of brand attachment and brand trust

PurposeThe study aims to delve into the interactive relationships among brand authenticity, brand attachment, brand trust and brand loyalty using the ERC authenticity framework, exploring the potential mechanisms and different configurations affecting brand loyalty through brand authenticity.Design/methodology/approachAbout 446 valid samples were collected through an online survey of Chinese consumers of international sports brands. Structural equation modeling (SEM) and fuzzy set qualitative comparative analysis (fsQCA) were employed to test the research hypotheses.FindingsSEM results indicate significant positive correlations among brand true-to-ideal authenticity, true-to-fact authenticity and true-to-self authenticity. All dimensions of brand authenticity exert notable positive impacts on brand attachment, brand trust and brand loyalty. Brand true-to-ideal authenticity, true-to-fact authenticity and true-to-self authenticity not only directly influence consumer brand loyalty but also indirectly affect it through brand attachment and brand trust; fsQCA results reveal five heterogeneous configurations to predict brand loyalty.Originality/valueThis study not only uses SEM to validate the structural relationships among the three dimensions of brand authenticity and their linear relationships with brand attachment, brand trust and brand loyalty but also uses fsQCA to identify nonlinear relationships between concepts. It extends complexity theory to the research field of brand authenticity–brand loyalty. Furthermore, based on the research results, this study provides management suggestions for brand managers and marketers.

Bipolar complex fuzzy near rings

In this article, we devise the novel concept of bipolar complex fuzzy (BCF) near rings (BCFNR), to fill a momentous research gap in connection of bipolar complex fuzzy sets with the theory of near rings in the existing literature. We expand the theory of near ring into the structure of BCF set (BCFS), offering a more suitable approach for the representation of algebraic systems with inherent ambiguity, bipolarity, and 2nd dimension information. Further, we introduce the concept of the bipolar complex fuzzy sub-near ring (BCFSNR), bipolar complex fuzzy left ideal (BCFLI), bipolar complex fuzzy right ideal (BCFRI), and bipolar complex fuzzy ideal (BCFI) in the near ring. After that, we prove the related theorem and results of these devised concepts. We also introduce theorems based on the homomorphism theory and Noetherian theory of near rings within the frame of bipolar complex fuzzy near rings. At the end of the manuscript, we reveal the application of BCFNRs in decision-making (DM) and illustrate it through an example.

Factors influencing physical activity behavior in older adults with subjective cognitive decline: an empirical study using SEM and fsQCA methods.

Despite the evident potential benefits of engaging in physical activity (PA) for older adults with subjective cognitive decline (SCD), their PA levels remains low. Previous research has predominantly focused on PA behaviors in individuals with dementia/mild cognitive impairment, with limited attention given to those with SCD. Therefore, this study aims to identify key factors influencing PA behavior in older adults with SCD based on the Self-Determination Theory (SDT) and the capability-opportunity-motivation (COM-B) model. Three hundred and three individuals aged 60 and above with SCD participated in this study. A face-to-face structured questionnaire survey was conducted. Data were analyzed using Structural Equation Modeling (SEM) and Fuzzy Set Qualitative Comparative Analysis (fsQCA). SEM results indicate that PA social support primarily influences PA behavior through three indirect pathways: the separate mediating effect of basic psychological needs, the separate mediating effect of motivation, and the chained mediating effect of both. Physical literacy, on the other hand, influences PA behavior through the separate mediating effect of motivation. Necessary conditions analysis by fsQCA reveals that no single factor is necessary for promoting PA behavior in older adults with SCD, while sufficiency analysis identifies four different combinations of factors leading to PA behavior. The model derived from the framework of SDT and the COM-B model effectively explains and predicts the interrelationships among variables. Physical activity behavior in older individuals with SCD is the result of multifactorial synergies.

Subgroups and Homomorphism Structures of Complex Pythagorean Fuzzy Sets

This research introduces the notion of complex Pythagorean fuzzy subgroup (CPFSG). Both complex fuzzy subgroup (CFSG) and complex intuitionistic fuzzy subgroup (CIFSG) have significance in assigning membership grades in the unit disk in the complex plane. CFSG has a limitation solved by CIFSG, while CIFSG deals with a limited range of values. The important novelty of the CPFSG lies in its ability to solve the above limitations simultaneously and gets a wider range of values to be engaged in CPFSG. This work has introduced and investigated CPFSG as a new algebraic structure via the conditions that the sum of the square membership and non-membership lies on the unit interval for both the amplitude term and phase term. The result as any CIFSG is CPFSG but the convers is not true has been proved. Complex Pythagorean fuzzy coset has been defined and complex Pythagorean fuzzy normal subgroup (CPFNSG) and their algebraic characteristic has been demonstrated. Homomorphism on the CPFSG is shown. Some results as the inverse image of CPFSG and CPFNSG under isomorphism function are also a CPFSG and CPFNSG, respectively.

Divisional intuitionistic fuzzy least squares twin SVM for pipeline leakage detection

Improving the accuracy of pipeline leak detection in noisy environments is significant for maintaining pipeline safety. Currently, the weighted support vector machine (SVM) is extensively employed. Among them, Intuitionistic fuzzy twin SVM (IFTSVM) has good robustness due to the integration of intuitionistic fuzzy set theory. However, IFTSVM does not fully consider the position information of samples in the feature space, cannot reduce the impact of heterogeneous clustering noise. Moreover, it requires solving two quadratic programming problems (QPPs), which is inefficient. To address the aforementioned issues, this paper proposes a divisional intuitionistic fuzzy least squares twin SVM (DIFL-TSVM) for pipeline leakage detection. This approach proposes a novel divisional intuitionistic fuzzy membership calculation strategy to reduce the impact of heterogeneous clustering noise and simplify the calculation of sample membership. In addition, DIFL-TSVM transforms the original QPPs into two linear equations using the least squares method to improve the efficiency of model. This paper verifies that the DIFL-TSVM model not only has the ability to eliminate the effects of discrete noise and heterogeneous clustering noise but also more efficiency on water supply pipeline leakage diagnosis experiments and UCI datasets. The experimental results show that DIFL-TSVM has higher leak identification with a detection accuracy of 95.6 %.

A Distance Measure for Intuitionistic Fuzzy Multicriteria Decision Making in Pattern Recognition and Medical Diagnosis

ABSTRACT In artificial intelligence, decision-making is a widely studied field, and distance measure plays a crucial role in decision science. Several distance measures for Intuitionistic Fuzzy sets (IFSs) have been developed to handle uncertain situations. However, some of these measures lack properties of distance measures or produce unreasonable results. This study introduces a new distance measure between IFSs that satisfies the necessary properties and resolves counter-intuitive cases. The proposed distance measure is verified to be more reasonable through numerical examples, and it is applied to pattern recognition and medical diagnosis problems.

Relational antecedents of sharing and not sharing knowledge: a configurational approach

PurposeKnowledge sharing within organizations has significant benefits for group and organizational performance. This study aims to identify the combinations of individual and relational factors that influence high- and low-intensity knowledge sharing among workers using the ability-motivation-opportunities (AMO) framework.Design/methodology/approachThis research adopts a qualitative, set-theoretic approach, using fuzzy set qualitative comparative analysis (fsQCA) to analyse data collected from knowledge workers in an international corporation.FindingsOur findings show that knowledge sharing is fundamentally relational, meaning that the quality and history of interaction with co-workers affect the intensity of knowledge-sharing behaviour. All AMO dimensions prove important for sharing, although some of them can substitute one another. However, if there is a lack of ability and opportunity, employees will not share knowledge even if they are motivated by reciprocity.Practical implicationsThe results offer human resources and project management practice alternative combinations of individual and relational factors that can be used to streamline knowledge sharing, emphasizing the need to invest in building relationships among co-workers.Originality/valueThis study contributes to the literature by identifying configurations of core and peripheral conditions pertaining to abilities, motivation and opportunities that can facilitate or constrain knowledge-giving.

Thriving through innovation: Boosting green tech performance in China's new energy sector

In response to the challenges of sustainable development, green technological innovation (GTI) is considered as a key driver of high-quality growth. However, current research often lacks a comprehensive assessment of GTI's multidimensional performance and rarely explores improvement pathways from a structural perspective. This study evaluates the GTI performance of China's new energy sector using data from 62 listed companies between 2015 and 2021, employing a Back Propagation (BP) neural network model optimized by a genetic algorithm. Additionally, it explores performance enhancement paths through fuzzy set Qualitative Comparative Analysis (fsQCA). The findings indicate that: Industrial structure and human resource levels significantly boost GTI performance in the new energy sector; Optimizing and upgrading industrial structures plays a crucial role in GTI improvement; High Research and development (R&D) investment and human capital strengthen market competitiveness, leading to higher GTI levels; The development of GTI in the new energy sector exhibited a general upward trend, with notable differences across sub-industries and regions from 2015 to 2021. Based on these results, this study recommends differentiated industry support, targeted regional policies, and enhanced talent cultivation to improve GTI performance in China's emerging energy industry.

SmartISM 2.0: A Roadmap and System to Implement Fuzzy ISM and Fuzzy MICMAC

Interpretive structural modeling (ISM) is a widely used technique to establish hierarchical relationships among a set of variables in diverse domains, including sustainability. This technique is generally coupled with MICMAC (Matrice d’Impacts Croisés Multiplication Appliquée á un Classement (cross-impact matrix multiplication applied to classification)) to classify variables in four clusters, although the manual application of the technique is complex and prone to error. In one of the previous works, a novel concept of reduced conical matrix was introduced, and the SmartISM software was developed for the user-friendly implementation of ISM and MICMAC. The web-based SmartISM software has been used more than 48,123 times in 87 countries to generate ISM models and MICMAC diagrams. This work attempts to identify existing approaches to fuzzy ISM and fuzzy MICMAC and upscale the SmartISM to incorporate fuzzy approaches. The fuzzy set theory proposed by Zadeh 1965 and Goguen 1969 helps the decision makers to provide their input with the consideration of vagueness in the real environment. The systematic review of 32 studies identified five significant approaches that have used different linguistic scales, fuzzy numbers, and defuzzification methods. Further, the approaches have differences in either using single or double defuzzification, and the aggregation of inputs of decision makers either before or after defuzzification, as well as the incorporation of transitivity either before or after defuzzification. A roadmap was devised to aggregate and generalize different approaches. Further, two of the identified approaches have been implemented in SmartISM 2.0 and the results have been reported. Finally, the comparative analysis of different approaches using SmartISM 2.0 in the area of digital transformation shows that, with a wide flexibility of fuzzy scales, the results converge and improve the confidence in the final model. The roadmap and SmartISM 2.0 will help in the implementation of fuzzy ISM and fuzzy MICMAC in a more robust and informed way.

Valuable Data "Gain" and "Loss": The Quantitative Impact of Information Choice on Consumers' Decision to Buy Selenium-Rich Agricultural Products.

Biotechnology assumes a paramount role in addressing micronutrient deficiencies. The promotion thereof and the augmentation of public awareness are indispensable for implementation. The advancement of big data presents challenges due to the plethora of information and the constrained processing capacity, thereby inducing difficulties in consumer decision-making. The study is obliged to intensify information dissemination to empower consumers to apprehend the value of selenium-enriched products as an integral constituent of positive nutrition guidance. The study undertook an experiment related to nutrition information acquisition, in which participants provided relevant interferences. The study utilized the structural equation model (SEM) and fuzzy set qualitative comparative analysis (fsQCA) to analyze the data. The study arrived at three research conclusions. Firstly, the furnishing of valuable information constitutes a significant factor in motivating consumers to purchase selenium-rich agricultural products. Secondly, the communication of brand information holds crucial significance in shaping the perception of product advantages and plays a salient role in the promotion and construction of selenium-rich agricultural products. Finally, the dissemination of health information can be incorporated into the process of promoting selenium-rich agricultural products. This conforms to the urgent necessity to address hidden hunger and establish a value identity.

Risk Assessment of Employing Digital Robots in Process Automation

Using digital technologies is essential to gain a competitive advantage in the global market by adapting to new business models. While digital technologies make business processes efficient, they enable companies to make faster and more accurate decisions by automating daily and routine process tasks. Robotic process automation (RPA) automates routine and repetitive business processes, allowing many jobs performed by humans to be performed faster. This way, advantages such as reduced error rates, reduced costs, increased production speed, and labor productivity are provided. For the successful implementation of RPA, potential risks need to be considered. In this study, failure mode and effect analysis (FMEA) based on decomposed fuzzy sets (DFSs), a new extension of intuitionistic fuzzy sets, has been used to evaluate subjectiveness in expert judgments. Differing from the other extensions of fuzzy set theory, the advantage of DFSs is to simultaneously consider decision-makers’ optimistic and pessimistic answers. Thus, the answer given by the decision-maker to the positive and negative questions on the same subject defines the indeterminacy of the decision-maker, and the method takes this indeterminacy into account in the evaluation. This study assesses and evaluates the potential risks of six digital robots in process automation. Thirteen risks were individually assessed for each automated process. This study found “Sustainability challenge” critical in three processes, “Absence of governance management” in two, and “Security“ in one. Variability in risk importance arose from process vulnerabilities.

Polarization and hate speech based on fuzzy logic and transformers: the case of the 2023 Spanish general elections

ABSTRACT Affective polarization in the digital debate of the Spanish presidential election campaign (2023), following the sudden call of the Spanish president on July 23, was measured. Using transformers, topics were detected, and sentiment analysis techniques were applied in the political debate during the elections to measure the emotional valence of the debate. The topics that dominate most of the debate are Candidates (n1 = 17170) and Opposition (n3 = 15327). These topics also show the highest typical polarization deviances. Based on affective polarization, a polarization measure (JDJ) grounded in the fuzzy sets was applied. The topic activism has the highest polarization value, while the topic of voting has the lowest. This analysis highlights a dichotomy that defines the Spanish political reality: the positive image of conventional political participation in the face of the rejection of collective action processes.

Information entropy-assisted intuitionistic fuzzy rough feature subset selection

Fuzzy-rough set (FRS) has been effectively implemented as a powerful pre-processing tool to cope with the issues such as vagueness, imprecision, noise and uncertainty in high-dimensional data analysis. However, FRS fails to handle the uncertainty due to identification, which is very much common in heterogeneous data. Moreover, it is always challenging to handle different issues such as vagueness, uncertainty, and noise during elimination of irrelevant and redundant features. This paper investigates an intuitionistic fuzzy rough set (IFRS)-aided feature selection method by using information entropy notion to tackle these issues simultaneously. We establish an intuitionistic fuzzy (IF) similarity relation. Then IFRS model is discussed based on this relation. Next, we present a novel IF granular structure. Based on the granular structure, we present lambda-conditional entropy for IF framework. Further, relevant mathematical theorems are proved to justify the theoretical aspect of the models. Moreover, a positive region preserved attribute selection method is proposed. A comprehensive experimental study is discussed to demonstrate the effectiveness and practical validation of the proposed method. Finally, a methodology is developed based on proposed models, which increases accuracy for the minority RNA silencing suppressors against majority class non-suppressors as evident from better sensitivity and G-means metrics.

A complexity theory in geotourism: traveler environmentally sustainable behaviors in global geoparks

ABSTRACT The importance of environmentally sustainable behavioral intentions (ESBI) in tourism is widely acknowledged, yet its role in geotourism is uncertain. Based on norm activation theory, behavioral reasoning theory, and complexity theory, this study used structural equation modeling and fuzzy set qualitative comparative analysis (fsQCA) to analyze data from 455 tourists at global geoparks in China. Results indicated that responsibility ascription, attitude, values, and place attachment influence ESBI. fsQCA identified four configuration combinations, confirming complexity theory principles. This paper addressed gaps in the literature and provided practical implications for geoparks.

Deep belief network with fuzzy parameters and its membership function sensitivity analysis

Over the last few years, deep belief networks (DBNs) have been extensively utilized for efficient and reliable performance in several complex systems. One critical factor contributing to the enhanced learning of the DBN layers is the handling of network parameters, such as weights and biases. The efficient training of these parameters significantly influences the overall enhanced performance of the DBN. However, the initialization of these parameters is often random, and the data samples are normally corrupted by unwanted noise. This causes the uncertainty to arise among weights and biases of the DBNs, which ultimately hinders the performance of the network. To address this challenge, we propose a novel DBN model with weights and biases represented using fuzzy sets. The approach systematically handles inherent uncertainties in parameters resulting in a more robust and reliable training process. We show the working of the proposed algorithm considering four widely used benchmark datasets such as: MNSIT, n-MNIST (MNIST with additive white Gaussian noise (AWGN) and MNIST with motion blur) and CIFAR-10. The experimental results show superiority of the proposed approach as compared to classical DBN in terms of robustness and enhanced performance. Moreover, it has the capability to produce equivalent results with a smaller number of nodes in the hidden layer; thus, reducing the computational complexity of the network architecture. Additionally, we also study the sensitivity analysis for stability and consistency by considering different membership functions to model the uncertain weights and biases. Further, we establish the statistical significance of the obtained results by conducting both one-way and Kruskal-Wallis analyses of variance tests.

Estimation of Reservoir Storage Capacity Using the Gould-Dincer Formula with the Aid of Possibility Theory

This paper presents a method for estimating reservoir storage capacity using the Gould–Dincer normal formula (G-DN), enhanced by the possibility theory. The G-DN equation is valuable for regional studies of reservoir reliability, particularly under climate change scenarios, using regional statistics. However, because the G-DN formula deals with measured data, it introduces a degree of uncertainty and fuzziness that traditional probability theory struggles to address. Possibility theory, an extension of fuzzy set theory, offers a suitable framework for managing this uncertainty and fuzziness. In this study, the G-DN formula is adapted to incorporate fuzzy logic, and the possibilistic nature of reservoir capacity is translated into a probabilistic framework using α-cuts from the possibility theory. These α-cuts approximate probability confidence intervals with high confidence. Applying the proposed methodology, in the present crisp case with the storage capacity D = 0.75, the value of the capacity C was found to be 1271×106 m3, and that for D = 0.5 was 634.5×106 m3. On the other hand, in the fuzzy case using the possibility theory, the value of the capacity for D = 0.75 is the internal [315,5679]×106 m3 and for D = 0.5 the value is interval [158,2839]×106 m3, with a probability of ≥95% and a risk level of α = 5% for both cases. The proposed approach could be used as a robust tool in the toolkit of engineers working on irrigation, drainage, and water resource projects, supporting informed and effective engineering decisions.