New exponential operation laws and operators for interval-valued q-rung orthopair fuzzy sets in group decision making process

Algorithm for investigating risk analysis and factors affecting suicidal attempts under uncertainty

Many people die from suicide, and it is a significant challenge in most societies, which calls for improved assessment procedures. This work presents a risk assessment model and outlines the risk factors for suicidal attempts under conditions of risk uncertainty. This algorithm assesses risk factors entirely using an interval-valued q-rung orthopair fuzzy (ivq-ROF) set information based Sugeno-Weber aggregation operators and EDAS method. Second, it applies Positive Distance from the Average (PDA) and Negative Distance from the Average (NDA) to balance an assessment, normalize various criteria, and rank them into higher order. We proposed ivq-ROF Sugeno-Weber weighted averaging (ivq-ROFSWWA), ivq-ROFS weighted geometric (ivq-ROFSWG) operators and EDAS method for improving the process of aggregation of fuzzy information. In the final type of stage, add up the PDA and NDA scores to determine the critical risk factors. This approach also increases the accuracy of predicting suicide risk, which is a vital asset for mental health researchers and practitioners to build effective intervention and prevention initiatives. Also, the nature of the algorithm renders decisions on compound data interfaces beneficial to numerous public health situations. Its application may include understanding factors that should inform policies that touch on mental health services and enhance the utilization of scarce resources in meeting the growing demand for such services. In conclusion, this study aspires to avoid future suicides due to a solid analytical framework for the research problem.

Some novel concepts of interval-valued q-rung orthopair fuzzy graphs and computational framework of fuzzy air conditioning system

The interval-valued q-rung orthopair fuzzy sets being an extension of interval-valued intuitionistic and interval-valued Pythagorean fuzzy sets is more flexible model to address vague information that has only two attributes yes or no. The combination of the concept of graph structures with interval-valued q-rung orthopair fuzzy sets termed interval-valued q-rung orthopair fuzzy graphs has been introduced in the literature. Due to its nature, interval-valued q-rung orthopair fuzzy graph provides a vast space for membership and non-membership values. In this study, we initiate the notions of covering and matching in the paradigm of interval-valued q-rung orthopair fuzzy graphs (IVq-ROFGs) and provide the analysis of fuzzy air conditioning system (FACS) based on these concepts. Some results and theorems related to these concepts, previously established for different fuzzy graphs, are also extended. In the beginning, we introduce the idea of covering in IVq-ROFGs and investigate several characteristics of some special types of IVq-ROFGs like cyclic IVq-ROFGs, complete IVq-ROFGs and complete bipartite IVq-ROFGs. Afterwards, we introduce the concept of matching in IVq-ROFGs and discuss various types of matchings within this frame. To demonstrate the effectiveness of our study, we apply the concepts of SAs and SIS in IVq-ROFGs supported by algorithm and pseudocode, to establish an appropriate framework for the FACS. This framework assesses the relationship between room temperature and target temperature. Finally, comparative study is conducted to verify that the presented model is the extension of the existing models in the literature.

An integrated group decision-making method under q-rung orthopair fuzzy 2-tuple linguistic context with partial weight information

Considering the advantages of q-rung orthopair fuzzy 2-tuple linguistic set (q-RFLS), which includes both linguistic and numeric data to describe evaluations, this article aims to design a new decision-making methodology by integrating Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) and qualitative flexible (QUALIFLEX) methods based on the revised aggregation operators to solve multiple criteria group decision making (MCGDM). To accomplish this, we first revise the extant operational laws of q-RFLSs to make up for their shortcomings. Based on novel operational laws, we develop q-rung orthopair fuzzy 2-tuple linguistic (q-RFL) weighted averaging and geometric operators and provide the corresponding results. Next, we develop a maximization deviation model to determine the criterion weights in the decision-making procedure, which accounts for partial weight unknown information. Then, the VIKOR and QUALIFLEX methodologies are combined, which can assess the concordance index of each ranking combination using group utility and individual maximum regret value of alternative and acquire the ranking result based on each permutation's general concordance index values. Consequently, a case study is conducted to select the best bike-sharing recycling supplier utilizing the suggested VIKOR-QUALIFLEX MCGDM method, demonstrating the method's applicability and availability. Finally, through sensitivity and comparative analysis, the validity and superiority of the proposed method are demonstrated.

A novel group decision making method based on CoCoSo and interval-valued Q-rung orthopair fuzzy sets

Interval-valued q-rung orthopair fuzzy set (IVq-ROFS) is a powerful tool for dealing with uncertainty. In this paper, we first propose a new method for aggregating multiple IVq-ROFSs, which is easier to understand and implement in the multi-attribute group decision making process compared to current aggregation operators. Secondly, this paper introduces a new fuzzy entropy with parameters based on IVq-ROFS, which is highly flexible due to its adjustable parameters. Based on this, the IVq-ROFS-based attribute weight calculation method is proposed to obtain the objective weights of the attributes, which is more reasonable and objective than the existing methods. Then, for the dimensional differences between the three compromise scores in the original Combined Compromise Solution (CoCoSo) method, the enhanced compromise scores are proposed. These scores are obtained by normalizing the three dependent compromise scores, ensuring that they fall within the same range. Finally, a novel CoCoSo mothed on IVq-ROFS using the proposed fuzzy entropy and enhanced compromise scores is presented. The proposed method is highly adaptable and scalable, not limited to IVq-ROFS. The excellent performance and robustness of the proposed method are verified in sepsis diagnosis applications.

Multi-attribute decision-making method based on q-rung orthopair probabilistic hesitant fuzzy schweizer-sklar power weighted hamy mean operator

In order to further improve the computing power of the information aggregation operator in the q-rung orthopair probabilistic hesitant fuzzy environment, this paper proposes a multi-attribute decision-making method based on the q-rung orthopair probabilistic hesitant fuzzy Schweizer-Sklar power weighted Hamy mean operator. Firstly, the algorithm of q-rung orthopair probabilistic hesitant fuzzy set is improved based on the Schweizer-Sklar T-norm. In order to better reflect the degree of hesitation of decision-making experts, a new q-rung orthopair probabilistic hesitant fuzzy distance measure is proposed, which provides a basis for subsequent power weighted calculations. Furthermore, considering the correlation between attributes and the influence of data extremes, some information aggregation operators and their power weighted forms are proposed. Finally, a multi-attribute decision-making model based on the q-rung orthopair probabilistic hesitant fuzzy Schweizer-Sklar power weighted Hamy mean operator is established, and the reliability and validity of the research content in this paper are verified through decision-making examples and comparative analysis.

An integrated group decision-making method for the evaluation of hypertension follow-up systems using interval-valued q-rung orthopair fuzzy sets

It is imperative to comprehensively evaluate the function, cost, performance and other indices when purchasing a hypertension follow-up (HFU) system for community hospitals. To select the best software product from multiple alternatives, in this paper, we develop a novel integrated group decision-making (GDM) method for the quality evaluation of the system under the interval-valued q-rung orthopair fuzzy sets (IVq-ROFSs). The design of our evaluation indices is based on the characteristics of the HFU system, which in turn represents the evaluation requirements of typical software applications and reflects the particularity of the system. A similarity is extended to measure the IVq-ROFNs, and a new score function is devised for distinguishing IVq-ROFNs to figure out the best IVq-ROFN. The weighted fairly aggregation (WFA) operator is then extended to the interval-valued q-rung orthopair WFA weighted average operator (IVq-ROFWFAWA) for aggregating information. The attribute weights are derived using the LINMAP model based on the similarity of IVq-ROFNs. We design a new expert weight deriving strategy, which makes each alternative have its own expert weight, and use the ARAS method to select the best alternative based on these weights. With these actions, a GDM algorithm that integrates the similarity, score function, IVq-ROFWFAWA operator, attribute weights, expert weights and ARAS is proposed. The applicability of the proposed method is demonstrated through a case study. Its effectiveness and feasibility are verified by comparing it to other state-of-the-art methods and operators.

Complex q-rung orthopair fuzzy Frank aggregation operators and their application to multi-attribute decision making

The complex q-rung orthopair fuzzy sets (Cq-ROFSs) can serve as a generalization of q-rung orthopair fuzzy sets (q-ROFSs) and complex fuzzy sets FS (CFSs). Cq-ROFSs provide more freedom for people handling uncertainty and vagueness by the truth and falsity grades on the condition that the sum of the q-powers of the real part and imaginary part is within the unit interval. Further, Frank operational laws are an extended form of Archimedes' T mode and Archimedes' S mode and Frank aggregation operators have a certain parameter which makes them more flexible and more generalized than many other aggregation operators in the process of information fusion. The objectives of this paper are to extend the Frank operations to the complex q-rung orthopair fuzzy environment and to introduce their score function and accuracy function. Meanwhile, some complex q-rung fuzzy Frank aggregation operators are developed, such as the complex q-rung orthopair fuzzy Frank weighted averaging (Cq-ROFFWA) operator, the complex q-rung orthopair fuzzy Frank weighted geometric (Cq-ROFFWG) operator, and the complex q-rung orthopair fuzzy Frank ordered weighted averaging (Cq-ROFFOWA) operator, and their special cases are discussed. In addition, an innovative MADM method is introduced according to the propounded operators to deal with multi-attribute decision-making problems under the complex q-rung orthopair fuzzy environment. Consequently, the practicability and effectiveness of the created methods are proposed by parameter exploration and comparative analysis.

TODIM method based on the CRITIC method for multi-attribute group decision making with dual probabilistic linguistic information

The main research of this paper is decision making under the dual probabilistic linguistic term sets (DPLTSs). This paper introduces a method, which combined TODIM method and CRITIC method. In this research, the CRITIC method is used to determine the weight, and the distance formula of TODIM method has been modified in order to adapt to DPLTS situation. Then, the TODIM method is used for multi-attribute group decision making (MAGDM) problem. Finally, a case study concerning investment project selection is given to demonstrate the merits of the developed methods. This combined method can be used for the automatic areal feature matching, medical quality assessment, and ranking of matching processes. There are very few papers about using TODIM method under DPLTS situation at present, so this is a new perspective on MAGDM. The DPLTS-TODIM-CRITIC method was compared with correlation coefficient method and closeness coefficient method, and it is easy to find the advantage of this new method over the other two existing methods.

Modelling uncertainties with TOPSIS and GRA based on q-rung orthopair m-polar fuzzy soft information in COVID-19

Fuzzy hybrid models are strong mathematical tools to address vague and uncertain information in real-life circumstances. The aim of this article is to introduce a new fuzzy hybrid model named as of q-rung orthopair m-polar fuzzy soft set (q-RO-m-PFSS) as a robust fusion of soft set (SS), m-polar fuzzy set (m-PFS) and q-rung orthopair fuzzy set (q-ROFS). A q-RO-m-PFSS is a new approach towards modelling uncertainties in the multi-criteria decision making (MCDM) problems. Some fundamental operations on q-RO-m-PFSSs, their key properties, and related significant results are introduced. Additionally, the complexity of logistics and supply chain management during COVID-19 is analysed using TOPSIS (technique for ordering preference through the ideal solution) and GRA (grey relational analysis) with the help of q-RO-m-PFS information. The linguistic terms are used to express q-RO-m-PFS information in terms of numeric values. The proposed approaches are worthy efficient in the selection of ventilator's manufacturers for the patients suffering from epidemic disease named as COVID-19. A practical application of proposed MCDM techniques is demonstrated by respective numerical examples. The comparison analysis of the final ranking computed by proposed techniques is also given to justify the feasibility, applicability and reliability of these techniques.

An algorithm to compute the strength of competing interactions in the Bering Sea based on pythagorean fuzzy hypergraphs

The networks of various problems have competing constituents, and there is a concern to compute the strength of competition among these entities. Competition hypergraphs capture all groups of predators that are competing in a community through their hyperedges. This paper reintroduces competition hypergraphs in the context of Pythagorean fuzzy set theory, thereby producing Pythagorean fuzzy competition hypergraphs. The data of real-world ecological systems posses uncertainty, and the proposed hypergraphs can efficiently deal with such information to model wide range of competing interactions. We suggest several extensions of Pythagorean fuzzy competition hypergraphs, including Pythagorean fuzzy economic competition hypergraphs, Pythagorean fuzzy row as well as column hypergraphs, Pythagorean fuzzy k-competition hypergraphs, m-step Pythagorean fuzzy competition hypergraphs and Pythagorean fuzzy neighborhood hypergraphs. The proposed graphical structures are good tools to measure the strength of direct and indirect competing and non-competing interactions. Their aptness is illustrated through examples, and results support their intrinsic interest. We propose algorithms that help to compose some of the presented graphical structures. We consider predator-prey interactions among organisms of the Bering Sea as an application: Pythagorean fuzzy competition hypergraphs encapsulate the competing relationships among its inhabitants. Specifically, the algorithm which constructs the Pythagorean fuzzy competition hypergraphs can also compute the strength of competing and non-competing relations of this scenario.

Inter-specific competition among trees in pythagorean fuzzy soft environment

A Pythagorean fuzzy set is very effective mathematical framework to represent parameter-wise imprecision which is the property of linguistic communication. A Pythagorean fuzzy soft graph is more potent than the intuitionistic fuzzy soft as well as the fuzzy soft graph as it depicts the interactions among the objects of a system using Pythagorean membership grades with respect to different parameters. This article addresses the content of competition graphs as well as economic competition graphs like k-competition graphs, m-step competition graphs and p-competition graphs in Pythagorean fuzzy soft environment. All these concepts are illustrated with examples and fascinating results. Furthermore, an application which describes the competition among distinct forest trees, that grow together in the mixed conifer forests of California, for plant resources is elaborated graphically. An algorithm is also designed for the construction of Pythagorean fuzzy soft competition graphs. It is worthwhile to express the competing and non-competing interactions in various networks with the help of Pythagorean fuzzy soft competition graphs wherein a variation in competition relative to different attributes is visible.