Search

This article presents a higher-order circular intuitionistic fuzzy time series forecasting method for predicting the stock change index, which is shown to be an improvement over traditional time series forecasting methods. The method is based on the principles of circular intuitionistic fuzzy set theory. It uses both positive and negative membership values and a circular radius to handle uncertainty and imprecision in the data. The circularity of the time series is also taken into consideration, leading to more accurate and robust forecasts. The higher-order forecasting capability of this method provides more comprehensive predictions compared to previous methods. One of the key challenges we face when using the amount featured as a case study in our article to project the future value of ratings is the influence of the stock market index. Through rigorous experiments and comparison with traditional time series forecasting methods, the results of the study demonstrate that the proposed higher-order circular intuitionistic fuzzy time series forecasting method is a superior approach for predicting the stock change index.

A combination of the single-valued neutrosophic set (SV-NS) and the probabilistic hesitant fuzzy set is the single-valued neutrosophic probabilistic hesitant fuzzy (SV-NPHF) environment (PHFS). It is intended for some unsatisfactory, ambiguous, and contradictory circumstances in which each element has a number of various values that are brought about by the situation’s actuality. The decision-maker can quickly gather and analyze the facts by employing a strategic decision-making technique. On the other hand, uncertainty will be a big part of our daily lives when we are learning. We present a decision-making strategy for the SV-NPHF context to address this data ambiguity. The fundamental operational concepts for SV-NPHF information under Dombi aggregation operators were initially developed on the basis of this study. The SV-NPHF Dombi weighted arithmetic average (SV-NPHFDWAA) operator and SV-NPHF Dombi weighted arithmetic geometric (SV-NPHFDWAG) operators are two SV-NPHF Dombi aggregation Operators that are then examined. Following that, we look into further characterizations of the proposed operators, including idempotency, boundedness, and monotonicity. For the derived operators, we additionally developed the score and accuracy functions. When using SV-NPHF data in a multi attribute decision support system (MADSS), it is necessary to compare the effectiveness of various (AOs) in order to make the best decision. In addition, it is demonstrated how to use symmetry analysis to choose the optimal social media platform for earning and learning in a practical application of SV-NPHFDWAA and SV-NPHFDWAG.

Spherical q-linear Diophantine fuzzy sets (Sq-LDFSs) proved more effective for handling uncertainty and vagueness in multi-criteria decision-making (MADM). It does not only cover the data in two variable parameters but is also beneficial for three parametric data. By Pythagorean fuzzy sets, the difference is calculated only between two parameters (membership and non-membership). According to human thoughts, fuzzy data can be found in three parameters (membership uncertainty, and non-membership). So, to make a compromise decision, comparing Sq-LDFSs is essential. Existing measures of different fuzzy sets do, however, can have several flaws that can lead to counterintuitive results. For instance, they treat any increase or decrease in the membership degree as the same as the non-membership degree because the uncertainty does not change, even though each parameter has a different implication. In the Sq-LDFSs comparison, this research develops the differential measure (DFM). The main goal of the DFM is to cover the unfair arguments that come from treating different types of FSs opposing criteria equally. Due to their relative positions in the attribute space and the similarity of their membership and non-membership degrees, two Sq-LDFSs form this preference connection when the uncertainty remains same in both sets. According to the degree of superiority or inferiority, two Sq-LDFSs are shown as identical, equivalent, superior, or inferior over one another. The suggested DFM's fundamental characteristics are provided. Based on the newly developed DFM, a unique approach to multiple criterion group decision-making is offered. Our suggested method verifies the novel way of calculating the expert weights for Sq-LDFSS as in PFSs. Our proposed technique in three parameters is applied to evaluate solid-state drives and choose the optimum photovoltaic cell in two applications by taking uncertainty parameter zero. The method's applicability and validity shown by the findings are contrasted with those obtained using various other existing approaches. To assess its stability and usefulness, a sensitivity analysis is done. [ABSTRACT FROM AUTHOR]

Crude oil, the backbone of modern industry, holds unparalleled significance as a global energy cornerstone. Unlocking its potential hinges on effective pretreatment techniques, ensuring purity, and maximizing efficiency. This study extends the established Spherical Fuzzy Set paradigm to explore the domain of Disc Spherical Fuzzy Sets (D-SFSs) in critical decision-making for crude oil preparation. Investigating D-SFSs within the Aczel Alsina norm, the research employs comparison rules, conversion rules, and distance metrics. Primary operations of the Aczel Alsina norm in D-SFSs are examined, laying the groundwork for introducing unique aggregation operations within this framework. The paper's primary aim is to propose a hybrid method, termed MEREC-SWARA-MARCOS-D-SFSs Multiple Attribute Group Decision Making, which integrates the aforementioned aggregation procedures. A case study on crude oil pretreatment validates the effectiveness of the proposed method. Furthermore, a comprehensive comparison with CoCoSo underscores the reliability of the method. This study represents a significant stride in enhancing decision-making by providing a robust framework to tackle complex situations, particularly in the critical domain of crude oil pretreatment. [ABSTRACT FROM AUTHOR]

The main goal of informal computing is to overcome the limitations of hypersensitivity to defects and uncertainty while maintaining a balance between high accuracy, accessibility, and cost-effectiveness. This paper investigates the potential applications of intuitionistic fuzzy sets (IFS) with rough sets in the context of sparse data. When it comes to capture uncertain information emanating from both upper and lower approximations, these intuitionistic fuzzy rough numbers (IFRNs) are superior to intuitionistic fuzzy sets and pythagorean fuzzy sets, respectively. We use rough sets in conjunction with IFSs to develop several fairly aggregation operators and analyze their underlying properties. We present numerous impartial laws that incorporate the idea of proportionate dispersion in order to ensure that the membership and non-membership activities of IFRNs are treated equally within these principles. These operations lead to the development of the intuitionistic fuzzy rough weighted fairly aggregation operator (IFRWFA) and intuitionistic fuzzy rough ordered weighted fairly aggregation operator (IFRFOWA). These operators successfully adjust to membership and non-membership categories with fairness and subtlety. We highlight the unique qualities of these suggested aggregation operators and investigate their use in the multi-attribute decision-making field. We use the intuitionistic fuzzy rough environment's architecture to create a novel strategy in situation involving several decision-makers and non-weighted data. Additionally, we developed a novel technique by combining the IFSs with quaternion numbers. We establish a unique connection between alternatives and qualities by using intuitionistic fuzzy quaternion numbers (IFQNs). With the help of this framework, we can simulate uncertainty in real-world situations and address a number of decision-making problems. Using the examples we have released, we offer a sophisticated and systematically constructed illustrative scenario that is intricately woven with the complexity of medical evaluation in order to thoroughly assess the relevance and efficacy of the suggested methodology. [ABSTRACT FROM AUTHOR]

Spherical q-linear Diophantine fuzzy sets (Sq-LDFSs) showed a significant improvement to handling uncertainty in multi-criteria decision-making (MADM). It is advantageous for two-parametric data as well as for data with three variable parameters. One of the most crucial functions of supply chain management is to increase competitive pressure. The study's standout innovation, Multi-Attributive Ideal Real Comparative Analysis (MAIRCA), has been implemented to give powerful group decision-making. An ecological perspective is becoming more prevalent due to the competitive climate and customer perception. Green supplier selection (GSS) has become a significant issue. In this study, we address the problem of GSS, which aims for flexibility, robustness, ecological sensitivity, leanness, and feasibility. The feasibility criteria in recycling, environmental, carbon footprints, and water consumption are different from those in standard supplier selection. The aim of our work is to introduced the weighted Average/Geometric aggregation operators based on Sq-LDFSs. For this we defined some operational rules as a foundation of aggregation operators. Secondly we proposed a MAIRCA approach for Sq-LDFSs to address these issues. The MAIRCA strategy, which uses multi-criteria group decision-making (MCGDM) to evaluate and choose traditional and environmental conventionalities, is used to reduce instability and ambiguity. The spherical q-linear Diophantine fuzzy MAIRCA approach provides comparative analysis of decision-makers and criteria. By merging Sq-LDFS and MAIRCA, a hybrid strategy is formed, successfully selecting the best provider among options based on the order of significance. These numerical examples demonstrate the suggested MCGDM approaches that were applied in actual situations, giving a realistic appreciation of their efficacy. The comparative study of the final ranking further supports the idea that these strategies are dependable in decision-making processes in addition to being practical and usable. [ABSTRACT FROM AUTHOR]

The literature frequently uses fuzzy inference methods for time series forecasting. In business and other situations, it is frequently necessary to forecast numerous time series. The q-Rung orthopair fuzzy set is a beneficial and competent tool to address ambiguity. In this research, a computational forecasting method based on q-Rung orthopair fuzzy time series has been created to deliver better prediction results to deal with situations containing higher uncertainty caused by large fluctuations in consecutive years' values in time series data and with no visualization of trend or periodicity. The main objective of this article is to handle time series forecasting with the usage of q-Rung orthopair fuzzy sets for things like floods, admission of students, number of patients, etc. After this, people can then manage issues that will arise in the future. Previously, there was a gap in determining the forecasting of data whose entire value of membership and non-membership exceeded 1. To fill this kind of gap, we used q-Rung orthopair fuzzy sets in time series forecasting. We also used numerous algebraic components for the q-Rung orthopair fuzzy time series, which has a union, max-min composition, cartesian product, and algorithm that are useful to calculate the method of data forecasting. Moreover, we also defined the algorithm and proposed MATLAB code that facilitates the execution of mathematical calculations, design, analysis, and optimization (structural and mathematical), and gives results with speed, correctness, and precision. At the end, we tested the model using historical student enrollment data and the annual peak discharge at Guddu Barrage. Furthermore, we calculated the error to get an idea of to what extent this method is suitable. [ABSTRACT FROM AUTHOR]

The key objective of this article is to introduce the innovative idea of a complex intuitionistic hesitant fuzzy set (CIHFS), which blends the intuitionistic hesitant fuzzy set with the complex fuzzy set to address the uncertain information in real-life complex problems. In CIHFS, the range of the membership functions is extended from the subset of the real number to the unit disc under the hesitant environment. To determine how well the CIHFSs can be distinguished from one another, we first propose generalized distance measures and weighted generalized distance measures based on the Hamming, Euclidean, and Hausdorff metrics. Some interesting properties and their relationships are thoroughly discussed. Furthermore, a decision-making framework for selecting the optimal option from the feasible set has been proposed, which is grounded in these distance metrics. For the purpose of proving the method's efficacy, we included examples from pattern recognition and medical diagnostics. [ABSTRACT FROM AUTHOR]

The primary goal of this research is to determine the optimal agricultural field selection that would most effectively support manufacturing producers in manufacturing production while accounting for unpredictability and reliability in their decision-making. The PFS is known to address the levels of participation and non-participation. To begin, we introduce the novel concept of a PFZN, which is a hybrid structure of Pythagorean fuzzy sets and the ZN. The PFZN is graded in terms of membership and non-membership, as well as reliability, which provides a strong advice in real-world decision support concerns. The PFZN is a useful tool for dealing with uncertainty in decision-aid problems. The PFZN is a practical way for dealing with such uncertainties in decision-aid problems. The list of aggregation operators: PFZN Einstein weighted averaging and PFZN Einstein weighted geometric, is established under the novel Pythagorean fuzzy ZNs. It is a more precise mathematical instrument for dealing with precision and uncertainty. The core of this research is to develop a numerical algorithm to tackle the uncertainty in real-life problems using PFZNs. To show the applicability and effectiveness of the proposed algorithm, we illustrate the numerical case study related to determining the optimal agricultural field. The main purpose of this work is to describe the extended EDAS approach, then compare the proposed methodology with many other methodologies now in use, and then demonstrate how the suggested methodology may be applied to real-world problems. In addition, the final ranking results that were obtained by the devised techniques were more efficient and dependable in comparison to the results provided by other methods presented in the literature. [ABSTRACT FROM AUTHOR]

The single valued neutrosophic probabilistic hesitant fuzzy rough Einstein aggregation operator (SV-NPHFRE-AO) is an extension of the neutrosophic probabilistic hesitant fuzzy rough set theory. It is a powerful decision-making tool that combines the concepts of neutrosophic logic, probability theory, hesitant fuzzy sets, rough sets, and Einstein aggregation operators. SV-NPHFREAO can be applied in many fields, including livestock decision making. Making judgments about a wide range of issues, including feed formulation, breeding program design, disease diagnostics, and market analysis, is part of the process of managing livestock. By combining data from many sources, SV-NPHFRE-AO can assist decision-makers in livestock management in integrating and evaluating diverse criteria, which can result in more informed choices. It also provides a more accurate and comprehensive representation of decision-making problems by considering the multiple criteria involved and the relationships between them. The single valued neutrosophic set (SVNS) aggregation operators (AOs) based on Einstein properties using hesitant fuzzy sets (HFSs) and probabilistic hesitant fuzzy sets (PHFSs) with rough sets (RSs) are proposed in this study and can handle a large volume of data, making them suitable for complex and large-scale livestock decision-making problems. We first defined SV-neutrosophic probabilistic hesitant fuzzy rough weighted averaging (SV-NPHFRWA), SV-neutrosophic probabilistic hesitant fuzzy rough weighted geometric (SV-NPHFRWG), SV-neutrosophic probabilistic hesitant fuzzy rough ordered weighted averaging (SV-NPHFROWA) and SV-neutrosophic probabilistic hesitant fuzzy rough hybrid weighted averaging (SV-NPHFRHWA) AOs. Then, based on Einstein properties, we extended these operators and developed the single-valued neutrosophic probabilistic hesitant fuzzy rough Einstein weighted averaging (SV-NPHFREWA) operator. Additionally, an illustrative scenario to show the applicability of the suggested decision-making approach is provided, along with a sensitivity analysis and comparison analysis, which demonstrate that its outcomes are realistic and reliable. We also provide another relation between criteria and alternatives of decision-making using neutrosophic information with quaternion context. By using such type of operators, livestock managers can make more informed decisions, leading to better animal health, higher productivity, and increased profitability. [ABSTRACT FROM AUTHOR]

Simulation software replicates the behavior of real electrical equipment using mathematical models. This is efficient not only in regard to time savings but also in terms of investment. It, at large scale for instance airplane pilots, chemical or nuclear plant operators, etc., provides valuable experiential learning without the risk of a catastrophic outcome. But the selection of a circuit simulator with effective simulation accuracy poses significant challenges for today's decision-makers because of uncertainty and ambiguity. Thus, better judgments with increased productivity and accuracy are crucial. For this, we developed a complex probabilistic hesitant fuzzy soft set (CPHFSS) to capture ambiguity and uncertain information with higher accuracy in application scenarios. In this manuscript, the novel concept of CPHFSS is explored and its fundamental laws are discussed. Additionally, we investigated several algebraic aspects of CPHFSS, including union, intersections, soft max-AND, and soft min-OR operators, and we provided numerical examples to illustrate these key qualities. The three decision-making strategies are also constructed using the investigated idea of CPHFSS. Furthermore, numerical examples related to bridges and circuit simulation are provided in order to assess the validity and efficacy of the proposed methodologies. The graphical expressions of the acquired results are also explored. Finally, we conclude the whole work. [ABSTRACT FROM AUTHOR]

This research proposes multicriteria decision-making (MCDM)-based real-time Mesenchymal stem cells (MSC) transfusion framework. The testing phase of the methodology denotes the ability to stick to plastic surfaces, the upregulation and downregulation of certain surface protein markers, and lastly, the ability to differentiate into various cell types. First, two scenarios of an enhanced dataset based on a medical perspective were created in the development phase to produce varying levels of emergency. Second, for real-time monitoring ofCOVID-19 patients with different emergency levels (i.e.,mild, moderate, severe, and critical), an automated triage algorithm based on a formal medical guideline is proposed, taking into account the improvement and deterioration procedures fromone level to the next. For this strategy, Einstein aggregation information under the Pythagorean probabilistic hesitant fuzzy environment (PyPHFE) is developed. Einstein operations on PyPHFE such as Einstein sum, product, scalar multiplication, and their properties are investigated. Then, several Pythagorean probabilistic hesitant fuzzy Einstein aggregation operators, namely the Pythagorean probabilistic hesitant fuzzy weighted average (PyPHFWA) operator, Pythagorean probabilistic hesitant fuzzy Einstein weighted geometric (PyPHFEWG) operator, Pythagorean probabilistic hesitant fuzzy Einstein ordered weighted average (PyPHFEOWA) operator, Pythagorean probabilistic hesitant fuzzy Einstein ordered weighted geometric (PyPHFEOWG) operator, Pythagorean probabilistic hesitant fuzzy Einstein hybrid average (PyPHFEHA) operator and Pythagorean probabilistic hesitant fuzzy Einstein hybrid geometric (PyPHFEHG) operator are investigated. All the above-mentioned operators are helpful in design the algorithm to tackle uncertainty in decision making problems. In last, a numerical case study of decision making is presented to demonstrate the applicability and validity of the proposed technique. Besides, the comparison of the existing and the proposed technique is established to show the effectiveness and validity of the established technique. [ABSTRACT FROM AUTHOR]