Search Results (25)

With the increasing prevalence of AI-supported services, the assessment of service quality in the e-tourism sector has become a more complex and multidimensional process. This study aims to analyse the dimensions of AI-supported e-tourism service quality using an advanced fuzzy multi-criteria decision-making approach that takes into account uncertainty and hesitation in expert judgements. Within this scope, the relative importance levels of the criteria associated with the six fundamental dimensions defined within the AISAQUAL model were modelled using interval-valued q-rung orthopair fuzzy sets (IVq-ROFS), and a flexible and parametric integration process was applied via Hamacher operators. The analytical framework was structured using the CIMAS method, which directly reflects expert experience in the criterion weighting process; a multi-stage evaluation process was conducted by integrating decision-makers’ levels of experience into the weighting mechanism. In this process, linguistic evaluations were converted into fuzzy numbers, combined using the Hamacher product operator, and reduced to precise values via scoring functions to calculate criterion weights. The findings indicate that incorporating uncertainty and interactions between criteria into the model leads to variations in the relative importance ranking of service quality dimensions. Furthermore, it was determined that the proposed approach produces more consistent and discriminatory results compared to classical weighting methods. In conclusion, the study demonstrates that the use of advanced fuzzy decision-making methods in the evaluation of AI-supported service quality can yield more realistic and reliable results. Full article

Multi-criteria decision-making (MCDM) involves evaluating alternatives under uncertain, vague, and conflicting criteria. While fuzzy set theories, such as intuitionistic, pythagorean, fermatean, and q-rung orthopair fuzzy sets have advanced uncertainty modeling, they remain limited to capturing extreme judgments where membership reaches a value of one alongside significant non-membership. The recently introduced q-fractional fuzzy set (q-FrFS) addresses these shortcomings via a flexible constraint, making it suitable for extreme contexts. However, existing q-FrFS methodologies lack robust aggregation mechanisms capable of balancing trade-offs and modulating compensation during information fusion. To overcome this, this study proposes a novel class of Frank-based aggregation operators tailored specifically to q-FrFS environments. Leveraging the parameterized structure of Frank t-norms and t-conorms, we develop two operators: q-FrFFWA (Frank weighted averaging) and q-FrFFWG (Frank weighted geometric) alongside their essential algebraic properties. These operators enhance the representation and fusion of complex and uncertain data. Furthermore, we present a comprehensive MCDM framework utilizing the proposed operators and demonstrate its applicability by selecting optimal vehicle routing software for last-mile delivery. Sensitivity and comparative analyses affirm the stability and credibility of the proposed methodology. This research contributes to the evolving landscape of fuzzy decision-making by integrating the expressive power of q-FrFS with the adaptive flexibility of Frank aggregation, offering a potent tool for modeling and analyzing multidimensional uncertainties in complex decision environments. Full article

In complex decision-making scenarios, such as smart city renewable energy project selection, decision-makers must contend with multi-dimensional uncertainty, conflicting expert opinions, and evolving temporal dynamics. This study introduces a novel Fractional q-Rung Orthopair Fuzzy Tensor (Fq-ROFT)-based group decision-making methodology that integrates the flexibility of q-rung orthopair fuzzy sets with tensorial representation and fractional-order dynamics. The proposed framework allows for the modeling of positive and negative membership degrees in a multi-dimensional, time-dependent structure while capturing memory effects inherent in expert evaluations. A detailed case study involving six renewable energy alternatives and six criteria demonstrates the method’s ability to aggregate expert opinions, compute fractional dynamic scores, and provide robust, reliable rankings. Comparative analysis with existing approaches, including classical q-ROFSs, intuitionistic fuzzy sets, and weighted sum methods, highlights the superior discriminative power, consistency, and dynamic sensitivity of the Fq-ROFT approach. Sensitivity analysis confirms the robustness of the top-ranked alternatives under variations in expert weights and fractional orders and membership perturbations. The study concludes by discussing the advantages, limitations, and future research directions of the proposed methodology, establishing Fq-ROFT as a powerful tool for dynamic, high-dimensional, and uncertain group decision-making applications. Full article

In recent years, interest in the application of ontologies in various domains of knowledge has grown significantly. Ontologies are widely used in a myriad of areas, such as artificial intelligence, data integration, knowledge management, and the semantic web, to name but a few. However, despite the widespread adoption, there exist a range of problems associated with ontologies, such as the complexity and cognitive challenges associated with ontology engineering, design, and development. One of the solutions to these challenges is to reuse existing ontologies rather than developing new ontologies afresh for new applications. The reuse of ontologies that describe a knowledge domain is a complex task consisting of many aspects. One of the key aspects involves ranking ontologies to aid in their selection. Various techniques have been proposed for this task, but many of them fall short in their expressiveness and ability to capture the cognitive aspects of human-like decision-making processes. Furthermore, much of the existing research focuses on an objective approach to ontology ranking, but it is unquestionable that a wide range of aspects pertaining to the quality of an ontology simply cannot be captured in a quantitative manner. Existing ranking models fail to provide a robust and flexible canvas for facilitating qualitative ontology ranking and selection for reuse. To address the aforementioned shortcomings of existing ontology ranking approaches, this study proposes a novel algorithm for ranking ontologies that extends the Elimination and Choice Translating Reality (ELECTRE) multi-criteria decision-making method with the Linguistic q-Rung Orthopair Fuzzy Set (Lq-ROFS-ELECTRE II), allowing the expression of uncertainty in a more robust and precise manner. The new Lq-ROFS-ELECTRE II algorithm was applied to rank a set of 19 ontologies of the machine learning (ML) domain. The ML ontologies were evaluated using a set of seven qualitative criteria extracted from the Ontometric framework. The proposed Lq-ROFS-ELECTRE II algorithm was then applied to rank the 19 ontologies in light of the seven criteria. The ranking results obtained were compared against the quantitative rankings of the same 19 ontologies using the traditional ELECTRE II algorithm, and confirmed the validity of the ranking performed by the proposed Lq-ROFS-ELECTRE II algorithm and its effectiveness in the task of ontology ranking. Furthermore, a comparative analysis of the proposed Lq-ROFS-ELECTRE II against existing MCDM methods and other existing fuzzy ELECTRE II methods displayed its superior modeling capabilities that allow for more natural decision evaluation from subject experts in real-world applications and allow the decision-maker to have much flexibility in expressing their preferences. These capabilities of the Lq-ROFS-ELECTRE II algorithm make it applicable not only in ontology ranking, but in any domain where there exist decision-making scenarios that comprise multiple conflicting criteria under uncertainty. Full article

Linguistic q-Rung orthopair fuzzy set is a new extension of the linguistic Pythagorean fuzzy set, which effectively represents the fuzzy and uncertain decision-making information based on qualitative modeling. However, its operational rules are unable to process pure linguistic exponential calculations, in which the exponents are represented using linguistic q-Rung orthopair fuzzy values and the bases are represented as linguistic terms or interval linguistic numbers. This greatly restricts its application in decision making under complex environments. As the complement of the existing linguistic q-Rung orthopair fuzzy operational rules, this paper defines linguistic q-Rung orthopair fuzzy calculation rules, including division, subtraction, and exponent operations. Based on theorem-based proofs, the relevant properties of the calculation rules have been analyzed, such as commutative law, distributive law, symmetry, and so on. Moreover, in order to facilitate the application of linguistic q-Rung orthopair fuzzy theory, this paper introduces the concept of dual linguistic q-Rung orthopair fuzzy value. Building on this foundation, a series of weighted aggregation operators for the calculations involving linguistic q-Rung orthopair fuzzy values and dual linguistic q-Rung orthopair fuzzy values have been designed. In conclusion, a novel pure linguistic multi criteria decision-making methodology is introduced in this work. The validity and utility of the proposed method are demonstrated via a real-world application in the decision process of energy resource exploitation. Full article

It is widely known that symmetry does exist in management systems, such as economics, management, and even daily life. In addition, effective and qualified decision-making methods can enhance the performance and symmetry of management systems. Hence, this paper focuses on a decision-making method. Linguistic interval-valued q-rung orthopair fuzzy sets (LIVq-ROFSs) have recently been proposed as being effective in describing decision-makers’ evaluation values in complex situations. This paper proposes a novel multi-attribute group decision-making (MAGDM) method with LIVq-ROFSs to handle realistic decision-making problems. The main contributions of this study are three-fold. First, a new method for determining the weight information of attributes based on decision makers’ evaluation values is proposed. Second, the classical TODIM is extended into LIVq-ROFSs and a new decision-making method is proposed. Third, our proposed MAGDM method is applied to a real decision-making problem to reveal its effectiveness. Full article

In this paper, based on the advantages of q-rung orthopair fuzzy sets (q-ROFSs), complex fuzzy sets (CFSs) and cubic sets (CSs), the concept of complex cubic q-rung orthopair fuzzy sets (CCuq-ROFSs) is introduced and their operation rules and properties are discussed. The objective of this paper was to develop some novel Maclaurin symmetric mean (MSM) operators for any complex cubic q-rung orthopair fuzzy numbers (CCuq-ROFNs) using Hamacher t-norm and t-conorm inspired arithmetic operations. The advantage of employing Hamacher t-norm and t-conorm based arithmetic operations with the MSM operator lies in their ability to take into account not only the interrelationships among multiple attributes but also to provide flexibility in the aggregation process due to the involvement of additional parameters. Also, the prominent characteristic of the MSM is that it can capture the interrelationship among the multi-input arguments and can provide more flexible and robust information fusion. Thus, based on the CCuq-ROF environment, we develop some new Hamacher operations for CCuq-ROFSs, such as the complex cubic q-rung orthopair fuzzy Hamacher average (CCuq-ROFHA) operator, the weighted complex cubic q-rung orthopair fuzzy Hamacher average (WCCuq-ROFHA) operator, the complex cubic q-rung orthopair fuzzy Hamacher Maclaurin symmetric mean (CCuq-ROFHMSM) operator and the weighted complex cubic q-rung orthopair fuzzy Hamacher Maclaurin symmetric mean (WCCuq-ROFHMSM) operator. Further, we develop a novel multi-attribute group decision-making (MAGDM) approach based on the proposed operators in a complex cubic q-rung orthopair fuzzy environment. Finally, a numerical example is provided to demonstrate the effectiveness and superiority of the proposed method through a detailed comparison with existing methods. Full article

Intuitionistic fuzzy sets (IFS), Pythagorean fuzzy sets (PFS), and q-rung orthopair fuzzy sets (q-ROFS) are among those concepts which are widely used in real-world applications. However, these theories have their own limitations in terms of membership and non-membership functions, as they cannot be obtained from the whole unit plane. To overcome these restrictions, we developed the concept of a complex linear Diophantine fuzzy set (CLDFS) by generalizing the notion of a linear Diophantine fuzzy set (LDFS). This concept can be applied to real-world decision-making problems involving complex uncertain information. The main motivation behind this paper is to study the applications of CLDFS in a non-associative algebraic structure (AG-groupoid), which has received less attention as compared to associative structures. We characterize a strongly regular AG-groupoid in terms of newly developed CLDF-score left (right) ideals and CLDF-score $(0,2)$-ideals. Finally, we construct a novel approach to decision-making problems based on the proposed CLDF-score ideals, and some practical examples from civil engineering are considered to demonstrate the flexibility and clarity of the initiated CLDF-score ideals. Full article

The theory investigated in this analysis is substantially more suitable for evaluating the dilemmas in real life to manage complicated, risk-illustrating, and asymmetric information. The complex Pythagorean fuzzy set is expanded upon by the complex q-rung orthopair fuzzy set (Cq-ROFS). They stand out by having a qth power of the real part of the complex-valued membership degree and a qth power of the real part and imaginary part of the complex-valued non-membership degree that is equal to or less than 1. We define the comparison method for two complex q-rung orthopair fuzzy numbers as well as the score and accuracy functions (Cq-ROFNs). Some averaging and geometric aggregation operators are examined using the Cq-ROFSs operational rules. Additionally, their main characteristics have been fully illustrated. Based on the suggested operators, we give a novel approach to solve the multi-attribute group decision-making issues that arise in environmental contexts. Making the best choice when there are asymmetric types of information offered by different specialists is the major goal of this work. Finally, we used real data to choose an ideal extinguisher from a variety of options in order to show the effectiveness of our decision-making technique. The effectiveness of the experimental outcomes compared to earlier research efforts is then shown by comparing them to other methods. Full article

The purpose of our research is to extend the formal representation of the human mind to the concept of the complex q-rung orthopair fuzzy hypersoft set (Cq-ROFHSS), a more general hybrid theory. A great deal of imprecision and ambiguity can be captured by it, which is common in human interpretations. It provides a multiparameterized mathematical tool for the order-based fuzzy modeling of contradictory two-dimensional data, which provides a more effective way of expressing time-period problems as well as two-dimensional information within a dataset. Thus, the proposed theory combines the parametric structure of complex q-rung orthopair fuzzy sets and hypersoft sets. Through the use of the parameter q, the framework captures information beyond the limited space of complex intuitionistic fuzzy hypersoft sets and complex Pythagorean fuzzy hypersoft sets. By establishing basic set-theoretic operations, we demonstrate some of the fundamental properties of the model. To expand the mathematical toolbox in this field, Einstein and other basic operations will be introduced to complex q-rung orthopair fuzzy hypersoft values. The relationship between it and existing methods demonstrates its exceptional flexibility. The Einstein aggregation operator, score function, and accuracy function are used to develop two multi-attribute decision-making algorithms, which prioritize based on the score function and accuracy function to ideal schemes under Cq-ROFHSS, which captures subtle differences in periodically inconsistent data sets. The feasibility of the approach will be demonstrated through a case study of selected distributed control systems. The rationality of these strategies has been confirmed by comparison with mainstream technologies. Additionally, we demonstrate that these results are compatible with explicit histograms and Spearman correlation analyses. The strengths of each approach are analyzed in a comparative manner. The proposed model is then examined and compared with other theories, demonstrating its strength, validity, and flexibility. Full article

Cyber-attacks are getting increasingly complex, and as a result, the functional concerns of intrusion-detection systems (IDSs) are becoming increasingly difficult to resolve. The credibility of security services, such as privacy preservation, authenticity, and accessibility, may be jeopardized if breaches are not detected. Different organizations currently utilize a variety of tactics, strategies, and technology to protect the systems’ credibility in order to combat these dangers. Safeguarding approaches include establishing rules and procedures, developing user awareness, deploying firewall and verification systems, regulating system access, and forming computer-issue management groups. The effectiveness of intrusion-detection systems is not sufficiently recognized. IDS is used in businesses to examine possibly harmful tendencies occurring in technological environments. Determining an effective IDS is a complex task for organizations that require consideration of many key criteria and their sub-aspects. To deal with these multiple and interrelated criteria and their sub-aspects, a multi-criteria decision-making (MCMD) approach was applied. These criteria and their sub-aspects can also include some ambiguity and uncertainty, and thus they were treated using q-rung orthopair fuzzy sets (q-ROFS) and q-rung orthopair fuzzy numbers (q-ROFNs). Additionally, the problem of combining expert and specialist opinions was dealt with using the q-rung orthopair fuzzy weighted geometric (q-ROFWG). Initially, the entropy method was applied to assess the priorities of the key criteria and their sub-aspects. Then, the combined compromised solution (CoCoSo) method was applied to evaluate six IDSs according to their effectiveness and reliability. Afterward, comparative and sensitivity analyses were performed to confirm the stability, reliability, and performance of the proposed approach. The findings indicate that most of the IDSs appear to be systems with high potential. According to the results, Suricata is the best IDS that relies on multi-threading performance. Full article

The cubic q-rung orthopair hesitant fuzzy set (Cq-ROHFS) provides greater information and is capable of representing both the interval-valued q-rung orthopair hesitant fuzzy set (IVq-ROHFS) and the q-rung orthopair hesitant fuzzy set (q-ROHFS). The concept of Cq-ROHFS is more flexible when considering the symmetry between two or more objects. In social life, complex decision information is often too uncertain and hesitant to allow precision. The cubic q-rung orthopair hesitant fuzzy sets are a useful tool for representing uncertain and hesitant fuzzy information in uncertain decision situations. Using the least common multiple (LCM) extension method, we propose a decision-making method based on an exponential similarity measure and hesitancy in the cubic q-rung orthopair hesitant fuzzy environment. To represent assessment information more accurately, our proposed method adjusts parameters according to the decision maker’s preferences in the decision-making process. The Cq-ROHFS setting was used to develop a depression rating method based on the similarity measure for depressed patients. Finally, the validity and applicability of the decision method is demonstrated using an example of depression rating assessment. As a result of this study, the scientific community can gain insight into real-world clinical diagnostic problems and treatment options. Full article

This paper deals with uncertainty, asymmetric information, and risk modelling in a complex power system. The uncertainty is managed by using probability and decision theory methods. Multi-attribute decision-making (MADM) technique is a very effective and well-known tool to investigate fuzzy information more effectively. However, the selection of houses cannot be carried out by utilizing symmetry information, because enterprises does not have complete information, so asymmetric information should be used when selecting enterprises. Hamy mean (HM) operator is a feasible tool to handle strategic decision-making problems because it can capture the order between the finite input terms. Additionally, the complex interval-valued q-rung orthopair fuzzy (CIVq-ROF) setting is a broadly flexible and massively dominant technique to operate problematic and awkward data in actual life problems. The major contribution of this analysis is how to aggregate the collection of alternatives into a singleton set, for this we analyzed the technique of CIVq-ROF Hamy mean (CIVq-ROFHM) operator and CIVq-ROF weighted Hamy mean (Cq-ROFWHM) operator and some well-known results are deliberated. Keeping the advantages of the parameters in HM operators, we discussed the specific cases of the invented operators. To investigate the decision-making problems based on CIVq-ROF information, we suggested the following multi-attribute decision-making (MADM) technique to determine the beneficial term from the finite group of alternatives with the help of evaluating several examples. This manuscript showed how to make decisions when there is asymmetric information about enterprises. Finally, based on the evaluating examples, we try to discover the sensitive analysis and supremacy of the invented operators to find the flexibility and dominancy of the diagnosed approaches. Full article

In today’s environment, as the complexity of actual events develops, products become increasingly complicated. As a result, companies should collaborate to integrate disparate technologies while developing a product or service. Additionally, collaborating with the right supplier helps a company increase the flexibility, competitiveness, and profitability of its goods or services. The goal of this study is to look into the factors that influence supplier selection for speech recognition. Twelve sub-criteria for quality, affordability, maintenance, and adaptability are used to evaluate prospective providers. Two separate hybrid methodologies for finding the best supplier of an information technology product are presented. intuitionistic Fuzzy Due to the uncertainty of the data, VIKOR operates as the decision-making matrix and solves the issue by determining the ideal alternative for group utility using VIKOR. The second technique, Q-ROF TOPSIS, selects suppliers by utilizing q-rung orthopair fuzzy sets, which provides decision makers with greater expression flexibility than the majority of uncertainty-related strategies. To demonstrate the effectiveness of the recommended measures, a case study is conducted. The outcomes of various strategies are compared, as well as the associated advantages. Full article

To determine the connection among any amounts of attributes, the Hamy mean (HM) operator is one of the more broad, flexible, and dominant principles used to operate problematic and inconsistent information in actual life dilemmas. Furthermore, for the option to viably portray more complicated fuzzy vulnerability data, the idea of complex q-rung orthopair fuzzy sets can powerfully change the scope of sign of choice data by changing a boundary q, dependent on the distinctive wavering degree from the leaders, where $\zeta \ge 1$, so they outperform the conventional complex intuitionistic and complex Pythagorean fuzzy sets. In genuine dynamic issues, there is frequently a communication problem between credits. The goal of this study is to initiate the HM operators based on the flexible complex q-rung orthopair fuzzy (Cq-ROF) setting, called the Cq-ROF Hamy mean (Cq-ROFHM) operator and the Cq-ROF weighted Hamy mean (Cq-ROFWHM) operator, and some of their desirable properties are investigated in detail. A multi-attribute decision-making (MADM) dilemma for investigating decision-making problems under the Cq-ROF setting is explored with certain examples. Finally, a down-to-earth model for big business asset-arranging framework determination is provided to check the created approach and to exhibit its reasonableness and adequacy. The exploratory outcomes show that the clever MADM strategy is better than the current MADM techniques for managing MADM issues. Full article