Bangladesh's off-grid regions face significant energy access challenges, requiring sustainable solutions like autonomous hybrid energy systems (HES). A key obstacle to their implementation is the selection of optimal energy storage systems (ESSs), which involves balancing diverse technical, economic, environmental, and social criteria. To overcome this challenge, a novel approach is proposed that uses generalized (p,q)-rung orthopair trapezoidal fuzzy numbers (G(p,q)-ROTrFNs) to better represent uncertainties by modeling both membership and non-membership degrees. A new multi-criteria group decision-making (MCGDM) method is introduced by integrating G(p,q)-ROTrFNs with the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS). This method provides a robust evaluation of ESS alternatives. Additionally, new operational rules and scoring functions based on expected values are introduced, ensuring both mathematical rigor and adaptability to various decision contexts. When applied to Bangladesh's decentralized HES, the analysis identifies battery-based ESSs as the optimal choice, while hydrogen-based ESSs rank the lowest. However, scenario analysis shows that financial incentives and public awareness campaigns can drive the adoption of hydrogen-based ESSs, overcoming current economic barriers. Sensitivity analysis confirms the robustness of the proposed method under varying parameters, and comparative studies highlight its superiority over traditional multi-criteria decision-making methods based on trapezoidal fuzzy numbers and q-rung orthopair trapezoidal fuzzy numbers approaches. The proposed method addresses critical gaps in existing MCGDM approaches by harmonizing computational efficiency, flexibility in uncertainty modeling, and adaptability to socio-technical constraints, thereby enabling informed, sustainable energy planning in resource-limited regions.

The rapid growth of real-world data highlights the need for advanced similarity measures to support pattern recognition and multi-criteria decision-making (MCDM). This paper introduces new cosine and cotangent similarity measures within the framework of fractional q-rung picture fuzzy hypersoft sets (Fq-RPFHSS), an extension of picture fuzzy hypersoft sets that allows a multi-argument domain for parameter proximity. Weighted versions of the proposed measures are developed, and their fundamental properties, including boundedness, non-degeneracy, symmetry, and transitivity, are established through formal theorems. To illustrate their effectiveness, a hypothetical decision-making example and a pattern recognition application are presented, demonstrating the flexibility and reliability of the proposed framework in handling uncertainty. The findings confirm that the introduced similarity measures and the Fq-RPFHSS environment provide a powerful and adaptable tool for decision analysis and pattern recognition, offering broader applicability than existing fuzzy soft and hypersoft set models.

In comparison with hesitant fuzzy sets and q-rung orthopair fuzzy sets, the dual hesitant q-rung orthopair fuzzy sets are more effective and flexible tool to deal with uncertainty and hesitation in decision-making problems. This article aims to propose a new variant of fuzzy set, viz., the dual hesitant q-rung orthopair trapezoidal fuzzy (DHq-ROTrF) set, and to develop a multicriteria group decision-making approach that employs DHq-ROTrF numbers (DHq-ROTrFNs) as evaluation values for capturing uncertainty and hesitation of expert judgments in a better way. The trapezoidal representation allows each evaluation to be expressed as a range rather than a single value, thereby improving the realism, flexibility, and robustness of uncertainty modelling compared to conventional dual hesitant q-rung orthopair fuzzy set. To begin with, this study proposes a set of fundamental operational laws for DHq-ROTrFNs based on Dombi t-norms and t-conorms. Using these operations, two novel aggregation operators, viz., the DHq-ROTrF Dombi weighted averaging operator and the DHq-ROTrF Dombi weighted geometric operator are introduced. The incorporation of Dombi operations enhances the flexibility and adaptability of the aggregation process in handling uncertainty and hesitation in group decision-making. In addition, various key aspects of the proposed operators are investigated. Furthermore, a new score function is formulated based on the concept of expected value to facilitate the ranking of alternatives. To demonstrate the application potentiality of the proposed method, a numerical example inspired by a real-life decision-making scenario for the recruitment of sales consultants is considered. The Criteria Importance Through Intercriteria Correlation (CRITIC) method is employed to determine the criteria weights, while the Weighted Aggregated Sum Product Assessment (WASPAS) method is used to rank the alternatives. Furthermore, the effects of the Dombi and rung parameters on the decision outcomes are thoroughly analysed to validate the robustness and practical usefulness of the developed approach. The findings confirm that the proposed framework can serve as an effective decision-support tool for complex group decision-making problems under uncertainty.

The problem in hybrid energy system (HES) planning lies in the over-reliance on quantitative optimization data, which may obscure critical qualitative factors like long-term sustainability. Additionally, slight variations in results from different optimization tools, despite identical inputs, further complicated decision-making, highlighting the need for advanced tools like p, q-quasirung orthopair trapezoidal fuzzy numbers (p, q-ROTrFNs) to enhance accuracy and clarity in evaluating alternatives. Therefore, in this study a novel concept ofp,q-ROTrFNs is introduced as an extension of traditional trapezoidal fuzzy numbers, designed to address limitations in handling uncertainty and vagueness in HES planning. A more refined representation of membership and nonmembership functions is offered by incorporating the p and q parameters, providing enhanced flexibility in managing imprecise data in multi-criteria decision-making processes. Additionally, score and accuracy functions based on expected values are developed to allow the comparison and ranking of p, q-ROTrFNs. Also, New operational rules for p, q-ROTrFNs are formulated to support their application in diverse decision-making scenarios, ensuring mathematical consistency. Moreover, p, q-ROTrFNs Preference Ranking on the Basis of Ideal-average Distance is method is proposed as a comprehensive framework for ranking alternatives in decision-making scenarios characterized by complex uncertainty. To develop the method two types of aggregation operators based on Schweizer-Sklar t-norms and t-conorms and a distance measure are introduced for aggregating p, q-ROTrFNs and quantify the similarity or dissimilarity between p, q-ROTrFNs respectively. After implementing the the PV-WT-HKT-DG-BT alternative achieved the score the seven A group decision making of seven experts demonstrated consistency in ranking. Additionally, sensitivity analysis confirmed the method's reliability, while a comparative study validated its effectiveness.

In the relevant literature, there is no study dealing with the financial credibility of third-party logistic providers with the help of decision-making frames. Further, there are no criteria to evaluate the third-party logistics providers' creditworthiness in practice, and decision-makers in the banks consider their judgments and experiences to assess the demand of the logistics firms. This study proposes a multi-criteria group decision-making framework through a dual hesitant linguistic q-rung orthopair fuzzy (DHLq-ROF) set to manage uncertainties more effectively and make a theoretical contribution to the academic literature. For ranking, the score function and accuracy function are defined. Additionally, some novel operational laws based on Frank t-norms and t-conorms are defined for DHLq-ROF numbers. A wide range of generalized aggregation operators, such as DHLq-ROF Frank weighted averaging, DHLq-ROF Frank weighted geometric, DHLq-ROF Frank generalized weighted averaging, and DHLq-ROF Frank generalized weighted geometric operators, are also investigated. Beyond that, several prominent characteristics of the proposed operators are studied. It is applied to a financial credibility problem for a multinational organization to demonstrate the introduced model's applicability. Considering the results obtained regarding the importance of the criteria, the most crucial criterion is market indebtedness, followed by fleet vehicle structure and current rate criteria, respectively. The results indicate that UPS, Kuhne & Nagel and DHL Deutsche Post are the best third-party logistic providers. The sensitivity analysis shows that the framework possesses favourable flexibility and effectiveness. Thanks to the framework's ability to produce practical solutions to challenging decision-making problems, it can be reliably preferred in engineering and other fields.

This paper proposes eight novel similarity measures for T-spherical fuzzy sets, addressing the limitations of existing measures in distinguishing closely related arguments. These new measures are specifically designed to enhance granularity and discriminative power, making them highly effective in complex decision-making scenarios such as pattern recognition and medical diagnosis. Additionally, Hamacher t-conorms and t-norms are introduced to aggregate T-spherical fuzzy numbers, offering a generalization of algebraic and Einstein classes of t-conorms and t-norms in aggregation theory. A numerical example demonstrates the effectiveness of the proposed similarity measures over other existing similarity measures, and a case study in medical diagnosis highlights their practical application. Comparative analysis with existing methods shows that the proposed approach not only overcomes the limitations of traditional similarity measures but also enhances the efficiency of pattern recognition in solving medical diagnosis problems. The study highlights its potential to improve performance in medical diagnosis and pattern recognition applications by offering a novel theoretical framework and validated practical tools.

Food waste depletes land, harms biodiversity, and damages ecosystems. As a consequence, it has been identified as one of the major factors that develop severe environmental problems globally. For successful management of food waste, selection of proper and appropriate treatment technology is one of the most significant decision-making issues due to involvement of numerous sustainability criteria and data uncertainty. This paper aims to introduce a hybrid four-stage decision-making methodology for selecting food waste treatment technologies (FWTT) using q-rung orthopair fuzzy soft rough (q-ROFSftR) sets. The proposed methodology begins with a weighting algorithm to assess the importance of decision-makers involved in the FWTT decision-making procedure. The second stage aggregates group of decision-makers’ opinions using an improved q-ROFSftR aggregation operator, which includes the q-ROFSftR Hamacher weighted averaging operator and its geometric variants. In the third stage, the stepwise weight assessment ratio analysis (SWARA) method is extended to the q-ROFSftR context to determine the criteria weights. Using the proposed distance measure, the final stage presents a hybrid q-ROFSftR-based technique for order of preference by similarity to ideal solution (TOPSIS) method to evaluate FWTTs with respect to multiple criteria and uncertain information. Further, the practicality of introduced methodology is demonstrated via a case study of FWTT selection, and the result indicates that “incineration” is the most suitable treatment technology followed by anaerobic digestion, composting, and landfill. Furthermore, the sensitivity and comparative analyses confirm the validity and stability of the proposed methodology. The present work offers valuable insights for selection of FWTT under uncertain environment, which also ensures the food security, economic growth, and environmental sustainability of a country.

The term q-rung orthopair fuzzy set is an essential variant of fuzzy set with the capacity of tackling fuzziness and imprecision in the decision-making process. A fundamental concept in the decision-making process is the idea of correlation coefficient because of its wide applications. The process of decision-making is complex due to imprecisions, and as such the idea of correlation coefficient has been investigated under q-rung orthopair fuzzy setting. Some authors have constructed some techniques of correlation coefficient under q-rung orthopair fuzzy sets with practical applications. However, these existing techniques are defectives with several drawbacks in terms of precision and alignment with the conditions of correlation coefficient. In this work, two new techniques for estimating correlation coefficient under q-rung orthopair fuzzy sets are presented and theoretically discussed. Moreover, we apply the new techniques of correlation coefficient under q-rung orthopair fuzzy sets in disease diagnosis and employment process by using simulated q-rung orthopair fuzzy data based on multi-criteria decision-making approach and recognition principle. Some comparative analyses are provided to ascertain the benefits of the new techniques of correlation coefficient under q-rung orthopair fuzzy sets over the obtainable techniques with regard to reliability and performance rating.

The South Asian Association for Regional Cooperation (SAARC) plays a crucial role in fostering regional integration and cooperation among the countries of South Asia. However, the organization faces various challenges in achieving its objectives due to the complex and dynamic nature of the region. This study aims to assess the triage and efficacy of strategies employed by SAARC in pursuit of regional integrity in South Asia, using a Multi-Criteria Group Decision Making (MCGDM) technique. The assessment process involves several uncertainties which are resolved using q-rung orthopair hesitant fuzzy (q-ROHF) set. A distance measure is developed for q-ROHF sets and is applied to find the best strategy among six well known strategies, viz., SAARC Agreement on Trade in Services (SATIS), SAARC Development Fund (SDF), Establishment of South Asian University (SAU), SAARC Arbitration Council (SARCO), South Asian Preferential Trade Agreement (SAPTA) and South Asian Free Trade Area (SAFTA) through SWARA-TOPSIS based MCGDM technique. The MCGDM method is updated by developing several aggregation operators, viz., Archimedean q-ROHF weighted average, q-ROHF Einstein weighted average, q-ROHF Hamacher weighted average, q-ROHF Frank weighted average along with their geometric forms to combine decision makers’ individual decision. The result shows that the most effective strategy for the economic integration of SAARC is SDF; SATIS comes next as best strategy for economic integration of SAARC. The study reveals that SAU has least impact on the regional economic integration of SAARC. The achieved results reveals the age old proverb that “play on the stomach and sit on the back” – the members of SAARC who are by nature economically poor can afford to take initiative for a successful effective regionalization if it is planned to bring socio economic development of themselves.

The healthcare industry faces numerous challenges in managing its supply chain efficiently, where critical decisions must be made promptly to ensure the availability of essential medical resources. This research introduces a novel artificial intelligence (AI) approach, utilizing the “Sugeno–Weber (SW) t-conorms and t-norms” (SWt-CNs&t-Ns) for decision-making in a Dual Hesitant q-Rung Orthopair Fuzzy (DHq-ROF) context. The SWt-CNs&t-Ns are chosen for their adaptability in data unification, serving as prominent operations for union and intersection processes. Developing a set of fundamental operations is imperative to effectively utilize SWt-CNs&t-Ns and hybrid aggregation operators in DHq-ROF settings. Following the introduction of these processes, several aggregating operators have been provided. These operators include DHq-ROF SW weighted averaging, ordered weighted averaging, hybrid averaging, and their geometric counterparts utilizing DHq-ROF data. The SW triangular norm-based approach aggregates group preferences, facilitating a systematic decision-making process. Triangular norms ensure a realistic representation of interrelationships among decision criteria, leading to optimal healthcare supply chain management solutions. Furthermore, the SW triangular norm-based approach aggregates group preferences, enabling a systematic and comprehensive decision-making process. Choosing the best healthcare supply chain management solutions is easier when you use triangular norms because they give a more accurate picture of how the decision criteria affect each other. The effectiveness of the proposed AI framework is demonstrated through a series of experiments and case studies, showcasing its ability to enhance decision accuracy, reduce uncertainty, and improve overall supply chain performance. The research findings underscore the potential of AI-driven solutions to revolutionize healthcare supply chain management, ultimately leading to better resource allocation, cost efficiency, and improved patient care.

ABSTRACTBreast masses are often one of the primary signs of breast cancer, and precise segmentation of these masses is essential for accurate diagnosis and treatment planning. Diagnosis may be complex depending on the size and visibility of the mass. When the mass is not visible clearly, precise segmentation becomes very difficult and in that case enhancement is essential. Inadequate compression, patient movement, or paddle/breast movement during the exposure process might cause hazy mammogram images. Without enhancement, accurate segmentation and detection cannot be done. As there exists uncertainties in different regions, reducing uncertainty is still a main problem and so fuzzy methods may deal these uncertainties in a better way. Though there are many fuzzy and advanced fuzzy methods, we consider Pythagorean fuzzy set as one of the fuzzy sets that may be powerful to deal with uncertainty. This research proposes a new Pythagorean fuzzy methodology for mammography image enhancement. The image is first transformed into a fuzzy image, and the nonmembership function is then calculated using a newly created Pythagorean fuzzy generator. Membership function of Pythagorean fuzzy image is computed from nonmembership function. The plot between the membership value and the hesitation degree is used to calculate a constant term in the membership function. Next, an enhanced image is obtained by applying fuzzy intensification operator to the Pythagorean fuzzy image. The proposed method is compared qualitatively and quantitatively with those of non‐fuzzy, intuitionistic fuzzy, Type 2 fuzzy, and Pythagorean fuzzy methods, it is found that the suggested method outperforms the other methods. To show the usefulness of the proposed enhanced method, segmentation is carried out on the enhanced images.

Strategic planning is a crucial activity for the owners and managers of business organisations, and it is an effective process that incorporates many distinct decision-making circumstances. Although there are numerous researches done to aid decision-makers, there are only a few studies that may provide the desirable generality, flexibility, and compatibility in adapting risk preferences. This study intends to propose a generalized multicriteria group decision-making (MCGDM) methodology by means of generalized parameter and Archimedean t-norms and t-conorms under linguistic q -rung orthopair fuzzy (Lq-ROF) theory for managing uncertainties in strategy formulations. For this purpose, a wide range of generalized aggregation operators, viz., Lq-ROF Archimedean weighted averaging, Lq-ROF Archimedean weighted geometric, Lq-ROF Archimedean generalized weighted averaging, and Lq-ROF Archimedean generalized weighted geometric operators are investigated. The contribution of this research lies in providing a robust and flexible methodology that integrates generalized parameters and Archimedean operators within the Lq-ROF framework, thereby offering a valuable tool for decision-makers navigating the complexities of strategy formulation in the presence of uncertainties. Additionally, some novel operational laws based on Archimedean t-norms and t-conorms are defined for Lq-ROF numbers. Further, several prominent characteristics of the developed operators are investigated. A strategic MCGDM model with Lq-ROF context is put forward and is applied to a financial strategy-making problem for a multinational organisation. The sensitivity analysis undertaken shows that the developed method possesses favourable flexibility and effectiveness. The potentiality and superiority are explored by comparing the obtained results with several existing studies.

Fermatean fuzzy set is an improved variant of intuitionistic fuzzy set that allows the relaxation of the restraints on the degrees of belongingness and nonbelongingness to enhance its ability to tackle complex decision-making problems. The conception of Fermatean fuzzy correlation coefficient operator is of a significant importance in computational intelligence. Certain correlation coefficient operators for measuring the association/correlation of Fermatean fuzzy sets (FFSs) have been studied, however with some setbacks in terms of accuracy and model formulation. In this chapter, two new correlation coefficient operators that reliably measure the correlation between any two arbitrary FFSs are developed. Some properties of the new Fermatean fuzzy correlation coefficient operators are presented to authenticate their appropriateness as viable correlation coefficients. Furthermore, the application of the new Fermatean fuzzy correlation coefficient operators is discussed in the cases of medical diagnosis based on Fermatean fuzzy simulated medical data. Lastly, the new Fermatean fuzzy correlation coefficient operators are compared with existing Fermatean fuzzy correlation coefficient approaches and adjudged to outclass existing approaches with better numerical results as presented in Tables 2.4–2.7 and graphically shown Figs. 2.2–2.5.

Transportation systems are a key part of sustainable development, and they need to be carefully evaluated to show that they have a strong impact on the target area's social, environmental, and economic sustainability. For this reason, involving the developed decision support systems helps to shed light on the users' demand and provide unblemished policy decisions considering the existing situation. The "q-rung orthopair fuzzy set (q-ROFS)"is a generalization of the "intuitionistic fuzzy sets (IFSs)"and "Pythagorean fuzzy sets (PFSs)"that expresses vague and uncertain data more efficiently. In the interim, the notion of "dual hesitant q-rung orthopair fuzzy set (DHq-ROFS)"is presented to account for human hesitancy, which may be more applicable to genuine "multicriteria group decision-making (MCGDM)"situations. The main goal of this study is to address MCGDM problems using Heronian mean (HM) and DHq-ROF data. The first step is to introduce the Frank t-norm and t-conorm-based DHq-ROF HM (DHq-ROFFHM) operator. DHq-ROFFHM's features are next described in depth. In addition, the DHq-ROF Frank weighted HM (DHq-ROFFWHM) operator is presented, which takes into account different degrees of liking for input arguments. The DHq-ROF Frank weighted power partitioned HM model is then used to come up with a way to solve models in MCGDM problems where individual arguments are grouped together and have relationships with each other. A final example shows how the established model can be implemented and how well it works.

Dual hesitant q -rung orthopair fuzzy set has already been appeared as a useful tool to express fuzzy and ambiguous information more precisely than other variants of fuzzy sets. Usually, equal weights of the possible membership as well as non-membership values in a dual hesitant q -rung orthopair fuzzy set, are considered in modelling decision making problems, which is quite unreasonable. Because, in ascertaining possible membership or non-membership values for an alternative under some criteria, the frequency level of appearing those values frequently differs. Thus, employing same weights/ degrees of importance to each of the assigned membership and non-membership values would affect overall process of decision making. To overcome such situation, this paper introduces the notion of weighted dual hesitant q -rung orthopair fuzzy set which allows decision makers to assign different weights of possible arguments in details. Taking advantage of Hamacher t -norms and t -conorms as a generalization of algebraic and Einstein operations, some operational laws for weighted dual hesitant q -rung orthopair fuzzy sets are investigated in this paper. Further, based on those defined operational rules, a series of weighted aggregation operators are proposed to aggregate the weighted dual hesitant q -rung orthopair fuzzy information effectively. Next, applying the proposed operators, a methodology for solving real-life group decision making problems under weighted dual hesitant q -rung orthopair fuzzy context is developed. Lastly, the aptness of the introduced method is illustrated by solving few numerical examples.

This research work contributes significantly to the current information field by offering an innovative model named the T-spherical fuzzy hypersoft (T-SFHS) set (T-SFHSS). This framework addresses both aspects of the three-dimensional knowledge implicated in the satisfaction, abstinence, and dissatisfaction inherent in human decision-making. It is an innovative approach to the problem of introducing computer cognition and decision-making in uncertain settings into the real world. The T-SFHSS is superior at determining what to do with unclear or imprecise data. The T-SFHSS enhances fuzzy sets such as the “intuitionistic fuzzy hypersoft set” and the “Pythagorean fuzzy hypersoft set”. It aims to increase the precision of fuzzy set calculations. To aggregate the decision data most effectively, we propose some novel Sugeno-Weber t-norm and t-conorm-based operational rules for T-SFHS numbers (T-SFHSNs). We then propose some T-SFHS aggregation operators with desirable properties in light of these operational laws. We conduct an illustrative study on natural agribusiness to demonstrate the viability and utility of the present methodology. The correctness of the obtained results can be verified by contrasting the proposed SW aggregation operators (AOs) on T-SFSS with the approaches already in use. The findings show that the proposed methodology is more consistent and successful than the current procedures.

Dual hesitant q ‐rung orthopair fuzzy (DH q ‐ROF) set appears as a powerful tool in compare to other variants of fuzzy sets to deal with uncertainties associated with available information in various real‐life decision‐making cases. In order to make DH q ‐ROF aggregation information process flexible, at first some operations viz., addition, multiplication, scalar multiplication, exponential laws based on Schweizer‐Sklar class of t ‐conorms and t ‐norms are defined. Subsequently, using these operations, weighted average and geometric operators and ordered weighted average and geometric operators are introduced. But weighted average or geometric operators and ordered weighted average or geometric operators consider only the weight of the opinions and the weight of the ordered position of each given opinion respectively. To resolve weights of the arguments, hybrid aggregation operators viz., DH q ‐ROF Schweizer‐Sklar hybrid averaging, DH q ‐ROF Schweizer‐Sklar hybrid geometric operators are developed and their properties are discussed. Afterwards, a new method to deal with multicriteria group decision making problems under DH q ‐ROF environment is framed. To illustrate the proposed method a decision making problem related to investment company selection is considered and solved. To show the advantages of the proposed study, a comparative analysis among the developed and existing studies is discussed.

Intuitionistic fuzzy set (IFS) is a reliable device for resolving uncertainty and haziness encountered in decision-making process. In most cases, the significance of IFSs are explored based on correlation measures in myriad of areas like in engineering, image segmentation, pattern recognition, diagnostic analysis, etc. Some methods for computing intuitionistic fuzzy correlation coefficient (IFCC) have been investigated, however with some inadequacies. In this present work, a new method of IFCC is developed to correct the drawbacks in some existing techniques in terms of mathematical presentation and the exclusion of the hesitation parameter to enhance reasonable output. A comparative analysis is presented to ascertain the edge of the new technique over some similar approaches. In addition, the new correlation coefficient technique is applied to discuss some pattern recognition problems. This new IFCC method could be investigated based on spherical fuzzy data, q-rung orthopair fuzzy data, and picture fuzzy data.

For complex last mile problems, the parcel lockers play an important role in urban areas. So, selecting a suitable location is very much crucial to provide optimal service and better logistical performance. From that viewpoint, a multicriteria group decision making method is developed in this article using dual hesitant q-rung orthopair fuzzy (DHq-ROF) set which is more effective than existing variations of fuzzy sets. The Aczel-Alsina (AA) class of t-conorms and t-norms (AAt-CNs & t-Ns) emerged as an important class of union and intersection operations due to its greater flexibility in the information fusion process. To take advantage of the benefits of AAt-CNs & t-Ns and hybrid aggregation operators in DHq-ROF environments, several fundamental operations based on AAt-CNs & t-Ns are first defined. Following the introduction of these stated operations, a series of aggregation operators, viz., DHq-ROF AA weighted averaging, ordered weighted averaging, hybrid averaging and their geometric versions with DHq-ROF information, has been proposed. Based on these operators, a creative approach to handle multi-attribute group decision-making problems has been framed. The parcel lockers' location selection problem is estimated to validate the created strategy and show its applicability and efficacy. The achieved results establish that the post office is the best location for locating parcel lockers.

A generalized orthopair fuzzy set (GOFS), also known as a q -rung orthopair fuzzy set ( q -ROFS), is a higher variant of ordinary fuzzy sets by relaxing restrictions on the degrees of membership and non-membership. In fact, GOFSs generalize intuitionistic fuzzy sets (IFSs), Pythagorean fuzzy sets (PFSs), and Fermatean fuzzy sets (FFSs) with an improved ability to tackle vagueness. On the other hand, correlation analysis measures the statistical relationships between two samples or variables. Certain approaches for measuring the correlation coefficient of GOFSs have been studied, however, with some setbacks. In this paper, we propose a new correlation coefficient that measures the interrelation between any two arbitrary GOFSs with a better rating. Some properties of the novel generalized orthopair correlation coefficient are presented to validate its appropriateness. In addition, the novel correlation coefficient is validated with some numerical examples and adjudged to outperform some existing approaches via comparative analysis. Finally, we discuss the applications of the novel approach in problems involving pattern recognition and medical diagnosis based on simulated data presented as generalized orthopair fuzzy values.