The task of an information retrieval system (IRS) is to retrieve relevant information from large datasets efficiently. However, selecting the best algorithm for an IRS is a complex decision support system (DSS) problem. The cubic m-polar fuzzy set (CmPFS) model is proposed to address the multi-polarity issue during algorithm selection and to express multi-polar information under m-fuzzy intervals. In this study, we introduce the Schweizer-Sklar (SS) aggregation operator based on CmPFS and develop various new aggregation operators using CmPFS and SS t-norm and t-conorm. New operations such as addition, usual multiplication, and power of two or more Cubic m-polar fuzzy Numbers (CmPFNs) based on SS t-norm and t-conorm are also defined. We also define score and accuracy functions to determine the priorities of alternatives. Four operators are proposed as follows: Cubic m-polar fuzzy Schweizer-Sklar weighted average (CmPFSSWA), Cubic m-polar fuzzy Schweizer-Sklar ordered weighted average (CmPOWA), Cubic m-polar fuzzy Schweizer-Sklar weighted geometric (CmPFSSWG), and Cubic m-polar fuzzy Schweizer-Sklar ordered weighted geometric (CmPFSSOWG). The desired qualities of these operators are explored and used to solve the DSS problem based on CmPFS. Finally, a practical application of an IRS is presented to demonstrate the effectiveness and reliability of the proposed operators.

Modeling uncertainties with multipolar information is an important tool in computational intelligence to address complexities in real-world circumstances. An m-polar fuzzy set (mPFS) is the strong model to express multipolarity with m $m$ membership grades (MGs) in the unit closed interval [ 0 , 1 ] $[0,1]$ . A q-rung orthopair fuzzy set (qROFS) is the strong model to express vague and uncertain information with MGs and nonmembership grades (NMGs). The notion of q-rung orthopair m-polar fuzzy set is a new hybrid extension of both mPFS and qROFS. An ROmPFS is a generalized concept that has the ability to deal with multipolarity with m $m$ ordered pairs of MGs and NMGs. Motivated by these robust concepts, in this article, various aggregation operators (AOs) for the aggregation of q-rung orthopair m-polar fuzzy numbers are proposed, including q-rung orthopair m-polar fuzzy weighted averaging operator, symmetric q-rung orthopair m-polar fuzzy weighted averaging operator, q-rung orthopair m-polar fuzzy weighted geometric operator, symmetric q-rung orthopair m-polar fuzzy weighted geometric operator, and q $q$ -rung orthopair m-polar fuzzy Maclaurin symmetric mean operator. On the basis of proposed AOs, a robust multicriteria decision-making approach is proposed. An application of proposed AOs is presented to address economic crises during COVID-19. Furthermore, the comparison analysis is designed to discuss the validity and rationality of proposed AOs.

Spherical fuzzy sets (SFSs) are strong models for modeling uncertain information in the computational intelligence and decision-making analysis. The key features of SFSs include the sum of the squares of membership grades (positive, neutral, and negative) lies in the unit closed interval [0, 1]. These models outperform other conventional fuzzy structures. The goal of this article is to develop some novel operational laws and “aggregation operators” (AOs) in a spherical fuzzy environment. For this goal, we define new neutral or fairly operational laws that incorporate the concept of proportional distribution to achieve a neutral or fair remedy of three indexes of spherical fuzzy numbers. Subsequently, we propose new “spherical fuzzy fairly weighted average operator” and “spherical fuzzy fairly ordered weighted averaging operator” based on suggested operational laws. The suggested AOs provide more generalized, reliable, and accurate information than other fuzzy techniques. Furthermore, a fairly multicriteria decision-making algorithm is developed using proposed fairly AOs with multiple decision-makers evaluations and partial weight information under SFSs. Moreover, a robust application of suggested algorithm is given to demonstrate the hierarchical medical treatment systems.

The q-rung orthopair sets (q-ROFSs) is an extended version of conventional orthopair fuzzy sets, such as intuitionistic fuzzy sets (IFSs) and Pythagorean fuzzy sets (PFSs). The most appealing feature of q-ROFSs is that they provide a wider range of reasonable membership grades and offer decision makers (DMs) more leeway in expressing their legitimate perceptions. The q-rung orthopair fuzzy numbers (q-ROFNs) play a vital role in computational intelligence, machine learning, neural network, and artificial intelligence. We develop numerous generalized aggregation operators (AOs) for information fusion of q-ROFNs to address some drawbacks of existing AOs. For this objective, we enhance the existing AOs by adding pairs of hesitation within the membership functions, and as a result, we introduce new operational rules for q-ROFNs utilizing Einstein norm operations. Based on suggested operational laws, we introduce new AOs namely “q-rung orthopair fuzzy Einstein interactive weighted geometric operator,” “q-rung orthopair fuzzy Einstein interactive ordered weighted geometric operator,” “generalized q-rung orthopair fuzzy Einstein interactive weighted geometric operator,” “generalized q-rung orthopair fuzzy Einstein interactive ordered weighted geometric operator,” and “generalized q-rung orthopair fuzzy Einstein interactive hybrid geometric operator.” Then, certain special cases of proposed AOs are explored and their some essential characteristics are described. A new multicriteria decision-making (MCDM) approach is devised with the help of suggested AOs for modeling uncertainties in the real-life problems. Additionally, a practical application of proposed MCDM approach is presented. Moreover, the comparison analysis, sensitivity analysis and authenticity analysis of proposed MCDM approach with existing approaches is also presented to discuss the feasibility, authenticity, and superiority of the proposed method.