Search

The circular economy (CE) seeks to maintain products and materials at their highest utility and value. The organisational and governmental policy have seised onto the CE philosophy to advance socio-economic and environmental development. CE remains an essentially contested concept – making its utilisation as a foundation for managerial and policy decisions challenging. Circularity assessment has not been systematically adopted, especially within supply chain management. Using critical scholarly and practical evidential foundation, we proposed a comprehensive set of metrics that can be utilised in supplier selection, monitoring, and development for circularity. These metrics include the macro, meso, and micro levels. A group decision-making method integrating best-worst method (BWM), regret theory (RT), and dual hesitant fuzzy sets (DHFS) for circular economy and circularity (CEC) supplier evaluation and selection is introduced – providing instrumental value for the identified metrics typology. The proposed BWM-DHFE-RT integrative analytical method can accommodate decisionmaker psychological behaviour under uncertainty while simultaneously capturing divergent or conflicting opinions of different decision-makers. An illustrative business scenario is utilised to demonstrate the application of the proposed method. Though the proposed CE performance metrics and methodology are used for CEC supplier management reasons they have broader applicability. Future research and application directions are discussed. [ABSTRACT FROM AUTHOR]

Organisations are having difficulty in determining and integrating Industry 4.0 technologies (I4.0T) to improve social dimensions of sustainable performance. We address this issue by showing how to identify the impact degree and path of I4.0T using the United Nations sustainable development goals (SDGs). First, we identify the SDGs associated with I4.0 and establish an evaluation framework. Second, we introduce an evaluation method that integrates hesitant fuzzy sets, rough sets, decision-making, trial evaluation laboratory (DEMATEL) and interpretative structural modelling methods to identify the impact degree and path of I4.0T on SDGs. Third, we confirm the effectiveness of our framework and method using secondary case data from World Economic Forum reports. The case results show that I4.0T have varying direct and indirect influences on SDGs. Policy makers should consider that 'Big Data and Analytics' and 'Cloud Computing' are central I4.0T that link other technologies and SDGs. The prioritisation of I4.0T can lead to contributions to addressing 'Responsible consumption and production' and indirectly improve SDGs. The findings set an initial stage to help sequence investment in various technologies depending on SDGs. We identify sequential paths that can aid in planning for and investing in adopting I4.0T to better achieve SDGs. [ABSTRACT FROM AUTHOR]

Data containing noises, outliers, and imbalanced class distributions pose challenges to the traditional classifiers. By incorporating both the membership and non-membership functions, the intuitionistic fuzzy (IF) set has shown potential in designing robust learning algorithms for classifiers. However, the non-membership function used in these IF-based classifiers usually only utilizes the local distribution information of the training samples, and the classifiers are built upon single-hidden layer networks, which degrade the performance of the corresponding classifiers. Broad learning system (BLS) is an emerging neural network model with fast learning speed and flexible network architecture; however, it still fails to distinguish n samples. To this end, in this paper, we propose a new definition of the non-membership function within intuitionistic fuzzy sets and subsequently propose an intuitionistic fuzzy broad learning system (IFBLS) model. The proposed non-membership function incorporates two ratio numbers based on four distances, allowing for the utilization of global information on the distribution of samples and mitigating misclassification of valid samples as noise which is often observed in traditional methods. By using a score function that considers both the membership and non-membership functions to redistribute the importance of the training samples, the proposed IFBLS benefits from both the powerful representation capability of the original BLS and the robust learning of IF-based models. Extensive experiments conducted on 21 imbalanced binary classification problems sourced from the UCI and KEEL repositories illustrate that the proposed IFBLS achieves state-of-the-art performance by attaining the highest testing accuracy in 17 out of the 21 problems. [ABSTRACT FROM AUTHOR]

Metaverse is a new technology expected to generate economic growth in Industry 5.0. Numerous studies have shown that current bitcoin networks offer remarkable prospects for future developments involving metaverse with anonymity and privacy. Hence, modelling effective Industry 5.0 platforms for the bitcoin network is crucial for the future metaverse environment. This modelling process can be classified as multiple-attribute decision-making given three issues: the existence of multiple anonymity and privacy attributes, the uncertainty related to the relative importance of these attributes and the variability of data. The present study endeavours to combine the fuzzy weighted with zero inconsistency method and Diophantine linear fuzzy sets with multiobjective optimisation based on ratio analysis plus the multiplicative form (MULTIMOORA) to determine the ideal approach for metaverse implementation in Industry 5.0. The decision matrix for the study is built by intersecting 22 bitcoin networks to support Industry 5.0's metaverse environment with 24 anonymity and privacy evaluation attributes. The proposed method is further developed to ascertain the importance level of the anonymity and privacy evaluation attributes. These data are used in MULTIMOORA. A sensitivity analysis, correlation coefficient test and comparative analysis are performed to assess the robustness of the proposed method. [ABSTRACT FROM AUTHOR]

This study explores the combinations of conditional variables contributing to depressive symptoms in rural children. We analyzed data from 715 children from a rural mental health database, conducting detailed follow-up investigations on 129 children in Zhejiang and Henan provinces. We used fuzzy set Qualitative Comparative Analysis (fsQCA) and regression analysis to identify causal pathways leading to depression. The results indicate that depression in rural children does not stem from a single, necessary condition but arises from multiple factors. Our findings highlight significant contributions from both maternal and paternal involvement. Specifically, maternal involvement, combined synergistically with peer support and problematic behaviors, as well as paternal involvement, together with peer support and anxiety, significantly affects depressive outcomes. Additionally, anxiety and strong peer relationships independently have a substantial impact on these outcomes. Effective mitigation strategies involve active parental engagement and robust peer support, reducing the influence of risk factors such as problematic behaviors and anxiety. The generalizability of the results is limited by cultural and geographical variations. The study also does not account for all potential factors influencing depression in rural children. Depression in rural children results from multiple interacting factors. Tailored interventions addressing these specific combinations are recommended. • The etiology of depression in children residing in rural areas cannot be ascribed to a singular conditional factor. • Maternal and paternal involvement, peer support, problematic behaviors, and anxiety influence rural children's depression. • Effective mitigation of depression in rural children involves concerted parental engagement and strong peer support. [ABSTRACT FROM AUTHOR]

As neural network-based localization algorithms are becoming popular, there is a need to shorten the training time and the localization time for sustainability and efficiency purposes. To address such issues, the fuzzy transform (or F-transform for short) is employed here for the first time in a neural network-based localization algorithm. The F-transform is a dimensionality reduction method, which has found several applications over the last decade, but it has not been well explored in the form of a prepending layer to a neural network. In this respect, some properties (including the computational cost) of the F-transformed neural scheme are formally discussed here. The performance of the neural network-based approach with and without F-transform, and with a state-of-the-art reduction technique, i.e. the principal component analysis, is evaluated first on simulated data and then on publicly available real-world data. Different neural network architectures have been tried jointly with the above-mentioned reduction techniques. The numerical experiments show the excellent performance of the proposed fuzzy transform-based approach, which can ensure considerable savings in training time and query response time, without significant losses in accuracy. [ABSTRACT FROM AUTHOR]

This paper presents a compact method for selecting the suboptimal number of clusters in unsupervised clustering, which is comparable in complexity to the well-known elbow method, silhouette score and gap statistic, but has been upgraded for clustering with the dominant overlaps. It is primarily based on heuristics and combines human reasoning with the deterministic qualitative analysis of clustering as a mapping from Euclidean space to the unstructured space of cognitive decision making and labeling. Epistemic stability as a notation involves a methodological approach that takes into account the available knowledge in the observed data set and avoids biases and effects on the objectivity of clustering. The stability discussion is also integrated into the assigned and naturally quantitative analysis of the dataset, not to regulate the clustering, but to learn from the pattern of instability. The method is applied to the specific task of classifying medical images from pre-selected image sets based on the associated metadata to determine the homogeneity of the analyzed clusters. However, it is also applicable to any other example of datasets that exhibit statistically multivariate problems with dominant overlaps. In the mathematical discussion of the method presented, the constrained conditions for datasets are established by definitions and assumptions, followed by the central mathematical propositions of the method of epistemic stability. [ABSTRACT FROM AUTHOR]

Structural dynamic problems are influenced by many parameters which are uncertain in nature. As such, for better estimation, the problems can be considered with epistemic-type uncertainties. Due to the epistemic type of uncertainties, the modeled system appears in the form of an imprecise nonlinear eigenvalue problem. Therefore, this paper proposes a novel nonprobabilistic approach to analyze nonlinear eigenvalue problems in structural dynamics, particularly when dealing with imprecisely defined parameters. In order to solve the fuzzy nonlinear eigenvalue models, the same is to be transformed into a fuzzy eigenvalue problem. Traditional methods often rely on probabilistic frameworks, which may not adequately capture uncertainties inherent in real-world structural systems. The proposed approach leverages concepts from fuzzy set theory to handle imprecise data, providing a more robust and realistic analysis. Then, the developed method is used to solve a case study of structural dynamic problem. Here, a spring-mass system is demonstrated through the developed technique to quantify the uncertain field variables. Based on the imprecise system parameters, different fuzzy models are discussed in various cases and results are reported here. This framework offers valuable insights for engineers and researchers working on dynamic analysis of complex structures, paving the way for more reliable and informed decision-making in structural engineering applications. [ABSTRACT FROM AUTHOR]

Domination and its associated concepts are significant ideas in fuzzy graph theory that have applications in diverse fields such as neural networks, game theory, and telecommunication. This study presents the notions of dominance and its associated research in a broader form of fuzzy graph called the multidimensional fuzzy graph. Basic results, such as the relationship between these parameters and vertices, edges, minimal sets, and so forth, are identified and illustrated with appropriate examples. The dominance number of multidimensional fuzzy graphs is determined after undergoing operations such as direct product, tensor product, composition, join, and others. Ultimately, this study displays a decision-making problem that can be efficiently addressed by utilizing the concept of the dominance number. The effectiveness of this approach is then compared to various models, like m-polar fuzzy sets. [ABSTRACT FROM AUTHOR]

Introduction: An innovative methodology is proposed to delve into the pivotal role of regional distribution networks (RDNs) in fostering low-carbon development. Methods: The methodology first constructs an evaluation framework encompassing various dimensions and then integrates spherical fuzzy sets (SFSs) with the best-worst method (BWM), enabling the precise calculation of indicator weight parameters. Subsequently, we employ the measurement of alternatives and ranking according to compromise solution (MARCOS) with SFSs to process and synthesize decision making information. Results: Take the Shanghai region as an example, results show that C4 has the highest performance and C10 has the lowest. Discussion: In conclusion, this research presents a significant step forward in understanding the importance of RDNs in promoting low-carbon development and offers a practical approach for decision-makers to assess and enhance the performance of RDNs. [ABSTRACT FROM AUTHOR]

Usually, it is not direct to translate theoretical results to practice. One of the challenges is the real computation of solutions to proposed problems, when infinite numbers (such as real numbers or the unit interval) are considered. The same is true when consequences (information) from a data set modeled by logical rules need to be obtained. In this framework, this information is obtained from the immediate consequences operator and ensuring its continuity is fundamental for making sure the computation of this information in a countable number of iterations. Recently, a versatile operator using generalized quantifiers has been introduced. This note will advance in the knowledge introducing necessary and sufficient conditions to ensure the continuity of this new flexible operator. [ABSTRACT FROM AUTHOR]

In the realm of volunteered geographic information (VGI), the existence of comparable tags, attributes, and values across diverse categories of geographic objects gives rise to major categorization challenges such as conceptual overlap and indiscernibility. Enhancing the semantic data retrieval of VGI relies on the semantic quality of descriptive content annotated for tagging geographic objects. The main focus of this study is analyzing the descriptive content of OpenStreetMap to assess the significance of semantic levels. The proposed methodology relies on fuzzy rough set calculations to determine the degrees of dependency and significance of semantic levels. Three indicators, namely, the significance of semantic levels, decreasing the heterogeneity of attributes, and replicability were defined and assessed for a subset of building‐related tags. Analyzing building‐related tags in OpenStreetMap unveiled the higher significance for simple object, similarity, purpose, and function levels. The value of decreasing the heterogeneity of attributes was calculated at 63%, and the average replicability indicator of important attributes was doubled. Based on the results, the significance of semantic levels was deemed fit to enhance semantic homogeneity and replicability. [ABSTRACT FROM AUTHOR]

In recent years, the application of fuzzy sets has gained significant attraction in various fields, including medical diagnosis, due to their ability to manage uncertainties and imprecise information. This paper focuses on the comparative analysis of similarity measures within the realm of Generalized Interval-Valued Intuitionistic Fuzzy Soft Expert Sets (GIVIFSESs) and explores their application in the domain of medical diagnosis. Most of the important topics in fuzzy set theory are the similarity measures between the generalizations of fuzzy set theory. Similarity measures are a crucial tool which was used in data science. In this process, we measure how much the data sets are related and comparable. Measures of similarity give a numerical value that reveals the strength of associations between sets or sets of variables. In this paper, we initiate a new concept of generalized interval-valued intuitionistic fuzzy soft expert sets and their fundamental operations. This new concept is more flexible than existing concepts based on their algebraic definition. Unlike fuzzy sets, the concept of generalized interval-valued intuitionistic fuzzy soft expert sets is characterized by a degree of membership and degree of non-membership along with fuzzy set theory. The proposed methodology is validated through an empirical application in medical diagnosis, where (GIVIFSESs) are employed to model the uncertainty and imprecision inherent in expert assessments. The selected similarity measures are then applied to quantify the degree of resemblance between different medical cases, facilitating a more informed decision-making process. We introduce several types of similarity measures on generalized interval-valued intuitionistic fuzzy soft expert sets. We also discuss a similarity measure of Type-I, Type-II, and Type-III for two (GIVIFSESs) and its application in medical diagnosis problems. [ABSTRACT FROM AUTHOR]

In present-day decision science, multiple-attribute decision making is an important area of research for choosing the right alternative among several possible alternatives. To address this issue, this paper proposes a novel multiple-attribute decision making problem based on linguistic r, s, t-spherical fuzzy information. The main purpose of the current study is to explore a novel linguistic r, s, t-spherical fuzzy score function and extend the measurement of alternatives and ranking according to the multiple-attribute decision making method with weight information to linguistic r, s, t-spherical fuzzy variables. Firstly, we propose the concept of linguistic r, s, t-spherical fuzzy variable, where the linguistic positive-membership, the linguistic neutral-membership and the linguistic negative-membership are represented by linguistic variables, and its operation rules are also discussed. The linguistic r, s, t-spherical fuzzy weighted averaging and linguistic r, s, t-spherical fuzzy weighted geometric operator are developed based on the proposed operation rules. Following that, the linguistic r, s, t-spherical fuzzy multiple-attribute decision making approach is established to cope with multiple-attribute decision making problems. Later, a case study of a selecting the suitable green supplier is addressed to show the practicality of the presented method. The comparison analysis with other existing operators shows the reliability of our work. [ABSTRACT FROM AUTHOR]

The concept of all-inclusive (AI) hotel service is complex and multidimensional; grounded on the SERVQUAL model, this research aims to identify the main components of AI service that moderate online ratings. Aside from the dimensions comprised in the SERVQUAL model, this research discusses price, place, and experiences as essential components in AI hotel ratings. This research was applied in the Algarve, a mature sun and sea destination with more than thirty all-inclusive hotels in operation since 2008. A fuzzy set qualitative analysis (fsQCA) approach is used from 6742 validated reviews of four- and five-star all-inclusive hotels retrieved from TripAdvisor between August 2003 and April 2019. Single constructs in the AI ecosystem have no impact as a rating enhancer on TripAdvisor, with two or more constructs necessary to trigger the goal. This research also contributes by applying a fuzzy set approach to the extracted user-generated content (UGC) regarding AI hotels on TripAdvisor and revealing the necessary and sufficient conditions through the identified constructs that most contribute to high online ratings. [ABSTRACT FROM AUTHOR]

As a significant part of modern decision science, multi-attribute decision-making (MADM) has attracted the interests of more and more researchers. In the process of decision analysis, decision-makers or experts usually give decision information in the forms of preference order, utility values or preference relation. Utility values, i.e. attribute values, generally include real-values, interval-values and linguistic-values. This article studies ternary hybrid decision matrices (THDMs), which contain the above three types of attributes. Firstly, we give a calculation method for attribute weight vectors in THDMs, and transform THDMs into fuzzy preference relations (FPRs). Secondly, we consider additive consistency of induced FPRs and introduce an inconsistency repairing method. Thirdly, we propose an MADM method for THDMs in view of (T, S)-fuzzy rough sets (FRSs) with FPRs. Finally, we give an example to explain the raised method in detail and conduct comparative analysis to illustrate the rationality and practicability of our raised method. [ABSTRACT FROM AUTHOR]

The importance of environmentally sustainable behavioral intentions (ESBI) in tourism is widely acknowledged, yet its role in geotourism is uncertain. Based on norm activation theory, behavioral reasoning theory, and complexity theory, this study used structural equation modeling and fuzzy set qualitative comparative analysis (fsQCA) to analyze data from 455 tourists at global geoparks in China. Results indicated that responsibility ascription, attitude, values, and place attachment influence ESBI. fsQCA identified four configuration combinations, confirming complexity theory principles. This paper addressed gaps in the literature and provided practical implications for geoparks. [ABSTRACT FROM AUTHOR]

The task scheduler belongs to the NP-complete problem, so it is very challenging in the cloud environment to develop one with reasonable performance and computation speed. Several studies take into account some important factors of the users or providers when addressing cloud task scheduling. This paper models cloud task scheduling as a Multi-objective Optimization Problem (MOP) that maximizes load balancing and execution times. Based on ring topology, we present a new multi-objective particle swarm optimization approach that utilizes intuitionistic fuzzy set to enhance evenness and diversity. The diversity and spread of the Pareto solution is adjusted using a Balanced Intuitionistic Fuzzy Crowding-Distance (BIF-CD) and Intuitionistic Fuzzy non-dominance sorting (IF-dominance). In order to identify the best compromise solution and in decision space and adjust evenness in objective space, ring topology is employed to identify a larger number of Pareto-optimal solutions. Experimental results for 25 benchmark multi-objective functions demonstrate the superiority of the proposed IF-MO-Ring-PSO over four state-of-the-art algorithms. We compare different quantitative measures to assess the uniformity and quality of Pareto fronts (PFs) found by our compared methods. The performance of the proposed method is evaluated on the benchmark test suites ZDT, DTLZ, CF, mDTLZ, and MMF, using the delta and space metrics and the progression metrics. According to the proposed method, ZDT test suite reduced space and delta metric by 13.36 and 15.11% respectively compared to other methods. The Wilcoxon's test and two-sample Mann Whitney's test are used to analyze the performance of proposed method on CF test suit. The analysis shows that the proposed method has better ability to generate quality PF with uniformity on constraint test suits. In comparison to four scheduling algorithms, the proposed scheduler also shows better performance according to load balancing, makespan, and resource utilization based on two datasets (i.e., HCSP and GoCJ). [ABSTRACT FROM AUTHOR]

Humanity's efforts are manifested in the creation of novel solutions to complex problems in diverse fields. Traditional mathematical methods fail to solve real-world problems due to their complexity. Researchers have come up with new mathematical theories like fuzzy set theory and rough set theory to help them figure out how to model the uncertainty in these fields. Soft set theory is a novel approach to real-world problem solving that does not require the membership function to be specified. This aids in the resolution of a wide range of issues, and significant progress has recently been made. After Jun et al. came up with a hybrid system that combined fuzzy and soft set concepts, many people came up with hybrid ideas in different algebraic structures. In this paper, we introduce the concepts of subsystem and strong subsystem of a hybrid finite state machine (HFSM) and investigate a portion of their significant properties. We also provide an example that shows that every subsystem does not need to be a strong subsystem. Additionally, we study the cyclic subsystem of HFSMs and also obtain their equivalent results and examples. Finally, we define the notions of homomorphism of subsystems and strong subsystems of HFSMs. [ABSTRACT FROM AUTHOR]

The Type-2 fuzzy set is a fuzzy set with fuzzy membership degrees. This set is used when accurately determining the membership degree of a fuzzy set is challenging. It has been observed that higher-type fuzzy sets improve accuracy. However, to use fuzzy sets of higher types in deterministic space, the type needs to be reduced. Another critical challenge is ensuring hardware implementation capability and optimal performance in real-time applications while using fuzzy techniques. Memristor structures are emerging hardware platforms with biological similarities to the human nervous system, and its nanoscale implementation and low power consumption, making them suitable for hardware implementation. This paper introduces various approaches to implementing a fuzzy system with type-2 membership fuzzy sets, and for the first time, demonstrates the utilization of memristor structures to reduce the type. The suggested circuits allow the membership functions to have any shape and resolution, and the implementation results demonstrate the efficiency of the proposed hardware. The main goal of this paper was to showcase a hardware implementation that incorporates on-chip training, allowing adaptability to the environment without dependence on the host system (In-Situ Training). The ArC One hardware platform is used to demonstrate the results experimentally. In modelling and classification, the simulation and experimental results show an increase in accuracy more than 2% has been achieved, compared to previous works. [ABSTRACT FROM AUTHOR]

• Proposed fuzzy small displacement torsor model for tolerance classification representation. • Established an assembly reliability index for evaluating assembly precision. • Developed constrained transformation method for efficient and high-accuracy calculation. • Proposed a novel fuzzy algorithm for assembly precision management. The assembly precision of a specified product is subject to epistemic uncertainty due to manufacturing and measurement errors. The efficient and accurate assembly precision management is essential for realizing smart production lines, which rely on the robust assembly precision analysis model and calculation method. Parametric models are extensively used for assembly precision analysis in industrial products, typically integrating worst-case and statistical methods for calculations. Nevertheless, the combined application of these methods presents inherent limitations. Therefore, a novel fuzzy algorithm for assembly precision management is proposed in this paper by using fuzzy sets to quantify epistemic uncertainty in assembly precision, which integrates the proposed fuzzy-based assembly precision analysis model and fuzzy-based calculation method. The proposed fuzzy small displacement torsor model is a fuzzy-based model for deviation representation and tolerance classification using decomposed fuzzy numbers, where an assembly reliability index is always established for the hierarchical evaluation and management of assembly precision. Subsequently, a comprehensive assembly precision analysis model is developed for precision prediction and contribution quantification by integrating the Jacobian model for deviation propagation. A new constrained transformation method is proposed as a fuzzy-based calculation method, offering efficient and highly accurate assembly precision computation. It accounts for torsor parameter constraints to ensure greater prediction accuracy than the worst-case method and improves computational efficiency compared to statistical methods. An assembly case of the centering pin mechanism is applied to verify the superiority of the proposed novel fuzzy algorithm compared with the Jacobian-Torsor model with worst-case or statistical methods. [ABSTRACT FROM AUTHOR]

Copyright of Scientia Iranica. Transaction E, Industrial Engineering is the property of Scientia Iranica and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper pays attention to the non-fragile observer-based H ∞ control problem of switched Takagi-Sugeno (T–S) fuzzy systems, where every subsystem is described by the T–S fuzzy model with local nonlinear terms. Different from the existing observer-based control strategy, a distinguishing feature of this paper is that constructed observers can make full use of current and past output measurements to enhance the performance of the observers in state estimation. However, the introduction of past output measurements has brought challenges to stability analysis and controller design. To tackle these difficulties and design a set of non-fragile fuzzy controllers to stabilize the systems, a new augmented state vector is constructed. First, a new non-fragile observer-based H ∞ control criterion is deduced based on the fuzzy Lyapunov function method. Then, the method of simultaneously solving observer and controller gains is obtained by introducing free matrix variables and using the linear matrix inequality approach. This solving method is more efficient than the traditional two-step method. Finally, two confirmatory instances are given. [ABSTRACT FROM AUTHOR]

As an essential tool for processing fuzzy or chaotic information, the main feature of the first-order Takagi–Sugeno (T–S) neuro-fuzzy model is utilizing a set of IF-THEN fuzzy rules to represent non-linear systems, showcasing commendable non-linear approximation ability and significant interpretability. However, the coexistence of linear rules and the affiliation function of fuzzy sets makes the integer-order gradient descent method (IOGDM), commonly used in training the first-order T–S neuro-fuzzy model, fail to accurately capture the intricate relationships among weights, resulting in the error function struggling to converge rapidly to low values. To enhance the convergence speed and training accuracy of the first-order T–S neuro-fuzzy model during the training process, a fractional-order gradient descent method (FOGDM) is proposed to update the fuzzy rule parameters and neural network weights of the model in this paper. By subdividing the gradient into fractional orders, FOGDM exhibits heightened flexibility in gradient adjustments, thus better capturing the complex non-linear relationships among parameters during the optimization process. The weak and strong convergence of the proposed approach is meticulously demonstrated in this paper, ensuring that the weight of error functions converges to a constant value and that the gradient of the error functions tends toward zero, respectively. Simulation results analysis indicates that, compared to IOGDM, FOGDM exhibits faster convergence speed and more significant generalization capabilities. [ABSTRACT FROM AUTHOR]

In real-world scenarios, datasets often lack full supervision due to the high cost associated with acquiring decision labels. Completing datasets by filling in missing labels is essential for preserving the valuable feature information of individual samples. Furthermore, in the era of big data, datasets tend to exhibit high dimensionality, which adds complexity to subsequent data processing. In this study, a new semi-supervised feature selection technique is introduced. Firstly, a fully supervised dataset is created by utilizing a local density decision-labeling algorithm to fill in missing decision labels within the semi-supervised dataset. Next, a fuzzy dependency-based feature selection approach is presented to find and keep the most pertinent characteristics for the finished datasets. Finally, the effectiveness and reliability of our proposed method are validated through a series of rigorous experiments. [ABSTRACT FROM AUTHOR]

As an effective tool to show the fuzziness of qualitative information, the probabilistic hesitant fuzzy set (PHFS) can utilize a group of membership degrees with a clear probability distribution to show the opinions of decision-maker (DM). Given this merit, many probabilistic hesitant fuzzy multi-criteria group decision-making (PHF-MCGDM) methods have been designed. However, most of the existing PHF-MCGDM methods have some limitations, including the difficulty of reflecting DMs' ambiguous and hesitant preferences for criteria weights and the inability to comprehensively show the impacts of DMs' irrational behaviors. To address these limitations, this paper develops a novel PHF-MCGDM method that integrates the defining interrelationships between ranked criteria (DIBR) approach and tri-reference point (TRP) theory. First, the PHF-DIBR approach is constructed to determine criteria weights by fully expressing DMs' ambiguous and hesitant preferences for the importance of criteria. Second, the novel probabilistic hesitant fuzzy correlation coefficient (NPHFCC) is developed for deriving the weights of DMs, which remedies the flaws of the existing correlation coefficients (CC). Moreover, TRP theory is used to describe the psychological behavior effects of DMs and derive the order of alternatives. Finally, the applicability of the proposed method is validated by the case about office flooring material selection, while the sensitivity and comparison analyses are also conducted to further prove its advantages and effectiveness. [ABSTRACT FROM AUTHOR]

Label distribution learning(LDL) has been received widespread attention as an effective learning paradigm in the field of data mining. However, existing feature selection algorithms for LDL are performed under static conditions or semi-dynamic conditions, and the dynamic information of data is not considered sufficiently. To address these issues, a feature selection algorithm for LDL under dynamic conditions is proposed, which takes full use of dynamic information from data. Specially, a novel rough set model called F-double-fuzzy rough set is created to handle dynamic LDL data, which is extended from F-fuzzy rough set. Then, F-fuzzy-condition entropy is defined to fuse information together in F-double-fuzzy rough set, which is considered as a measure for feature selection. Thirdly, a dynamic feature selection algorithm for LDL is proposed with the F-fuzzy-condition entropy. Fourthly, a novel method of covering set is proposed to simulate dynamic LDL data, and its merits are explained intuitively. At last, experiments on 14 datasets with six widely used metrics demonstrate that the performance of the proposed algorithm has advantages over the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

A circular Pythagorean fuzzy set is a very dominant and reliable technique for coping with uncertain and vague information in genuine life problems because it contains the membership function, non-membership function, and radius between both grades with a condition that is the sum of the pair will be contained in the unit interval. The objective of this article is to define some improved Dombi operational laws based on circular improved Pythagorean fuzzy values. Moreover, we discuss some drawbacks of the existing Dombi aggregation operators based on circular improved Pythagorean fuzzy numbers. We also defined some improved Dombi averaging and geometric aggregation operators and their properties. Later on, a technique of evaluation based on distance from the average solution is stated by using the proposed operators. Additionally, we resolve the problem of policy analysis and decision support systems based on multi-attribute decision-making problems for initiated techniques to enhance the worth of the derived theory. Finally, we illustrate some examples for showing the supremacy and validity of the proposed theory with the help of comparative analysis between proposed ranking values and some existing ranking values. [ABSTRACT FROM AUTHOR]

This paper presents an original and comprehensive investigation into the Taxonomic Measure of Development (TMD), introduced by Hellwig in 1968, enriching both its theoretical foundations and practical applications. It provides an overview of various variants of the Hellwig method, including their extensions and applications, while also exploring recent trends across multiple research domains. Primarily developed as a method for multidimensional analysis, TMD has evolved into a pivotal tool in multi-criteria decision-making. It is widely used for evaluating and ranking alternatives, particularly in the analysis of complex socio-economic phenomena and decision-making scenarios involving multiple criteria. This study systematically reviews the original algorithm and its subsequent extensions and modifications, including adaptations for fuzzy sets, intuitionistic fuzzy sets, and interval-valued fuzzy sets. Furthermore, it explores an integrated multi-criteria approach based on Hellwig's method and its practical applications across various domains. This paper introduces an original approach by conducting a detailed, step-by-step analysis of the TMD framework. This process-oriented analysis is a novel contribution that sets this study apart from typical reviews based on statistical or bibliometric data. By examining key steps in the TMD framework—such as data collection, criterion weighting, data normalization, ideal value determination, distance calculation, and normalization factor—this paper highlights the method's versatility in addressing complex, real-world decision-making problems. Although similar to the widely used Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method in its reliance on distance to evaluate alternatives, Hellwig's approach is unique in focusing exclusively on proximity to an ideal solution, without considering distance from a negative ideal. This distinctive emphasis has led to numerous adaptations and extensions that address specific issues such as criterion dependencies, uncertainty, and rank reversal. The findings underscore the continued relevance of the Hellwig method, its recent extensions, and its growing international recognition. [ABSTRACT FROM AUTHOR]

The risk of both fatal accidents and non-fatal injuries in the construction industry is significantly high in most countries. To reduce this construction safety risk, the proper use of personal protective equipment (PPE) is one of the major measures on the jobsite. In this research, in order to comprehensively assess the PPE plans, a three-phase framework was proposed to identify the optimal solution for PPE planning from a set of alternatives. As a result, four main criteria and fifteen sub-criteria were identified based on a systematic literature review, and a decision-making model integrating the analytic network process (ANP) and VIekriterijumsko KOmpromisno Rangiranje was developed. As the assessment information in the survey was incomplete and vague, the fuzzy sets theory was adopted to transform the linguistic terms into fuzzy numbers for evaluation. The model further calculated the weight of each criterion and prioritized the potential PPE plan alternatives. Finally, the presented model was implemented in a case study to verify its feasibility and applicability for practical construction management. The proposed method enables the selection of the most compromising solution as the optimal PPE plan. This research assists decision-makers and safety planners at construction workplaces to improve the overall safety performance and reduce accident risks, which significantly contributes to construction safety management and practice. [ABSTRACT FROM AUTHOR]

In today's highly competitive business environment, firms strive to maximize profitability by minimizing or eliminating disruptions and failures to maintain a competitive edge. This study focuses on evaluating risks in a hydraulic pump factory as a means to achieve sustainable growth. To accomplish this, a team of experts was formed to identify potential errors, utilizing a combination of risk priority number criteria weighted by Fuzzy Shannon's entropy and a fusion of multi‐criteria decision‐making and failure mode and effects analysis for evaluating and ranking failures. Moreover, the study emphasizes the importance of considering the interaction among risk assessment indicators, the inclusion of cost of failure, and modeling under fuzzy uncertainty circumstances, as they have a notable impact on the final ranking of failures to be processed for risk mitigation action planning. This research brings a new dimension to enhance the overall effectiveness of risk assessment by aggregation, as evidenced by a novel use of data classification in machine learning and correlation in statistics. The findings indicate that the aggregated ranking data series is best matched and most influenced by the weighted aggregated sum product assessment method, with the highest rate of recall and precision accomplished. [ABSTRACT FROM AUTHOR]

As China's urban digitalization strategy advances to a new stage, the urgent issue of promoting urban digitalization development by optimizing the business environment requires immediate attention. Scholars have illustrated the positive impact of urban digital development on both the environment and sustainable growth. In our study, we utilized the "Technology-Organization-Environment (TOE)" framework to analyze a series of exemplary urban digitalization cases in China from 2021 onward. This analysis was conducted using the fuzzy set qualitative comparative analysis (fsQCA) method. Our primary findings reveal that government policies, digital talents, and digital infrastructure are key drivers in advancing urban digitalization. Specifically, we identified four distinct pathways, all involving various combinations of government environment, digital talents, and digital infrastructure, leading to significant urban digitalization. These insights uncover the connection between the business environment and urban digital growth. This relationship not only offers valuable guidance for future urban digital initiatives but also supports the broader goals of urban environmental sustainability. [ABSTRACT FROM AUTHOR]

A robust scheduling optimization method for wind–fire storage system distribution based on the mixed carbon trading mechanism is proposed to improve the rationality of carbon emission quota allocation while reducing the instability of large-scale wind power access systems. A hybrid carbon trading mechanism that combines short-term and long-term carbon trading is constructed, and a fuzzy set based on Wasserstein measurement is proposed to address the uncertainty of wind power access. Moreover, a robust scheduling optimization method for wind–fire storage systems is formed. Results of the multi scenario comparative analysis of practical cases show that the proposed method can deal with the uncertainty of large-scale wind power access and can effectively reduce operating costs and carbon emissions. [ABSTRACT FROM AUTHOR]

Prior studies have typically examined the individual antecedents of entrepreneurial passion, but overlooked the complex interactions among those determinants. This study addresses this gap in the literature by using a fuzzy set qualitative comparative analysis to explore the combinations of educational, structural, and relational support and entrepreneurial self-efficacy that drive two types of entrepreneurial passion; namely, obsessive and harmonious passion. We tested our propositions by reference to a sample of 304 young individuals who had graduated from or were studying at business and engineering schools in France. Data were collected between November 2020 and January 2021. Our findings challenge the net effect approach and indicate that passion is, instead, shaped by several combinations of support mechanisms and individual traits. Specifically, obsessive and harmonious passion are triggered by one common path and two discrete configurations of factors. These findings have important implications for theory and practice. [ABSTRACT FROM AUTHOR]

As significant vehicles for applying fuzzy set theories, fuzzy inference systems (FISs) have been widely utilized in artificial intelligence. However, challenges such as computational complexity and subjective design persist in FIS implementation. To address these issues, this paper introduces the fuzzy inference system based on axiomatic fuzzy sets (FIS-AFSs), which includes a fuzzy rule base and a fuzzy inference engine. This system eliminates the need for subjective decisions in the selection of fuzzification and defuzzification methods. The theoretical foundation of the approach involves defining the multi-dimensional vague partition (VP) of the multi-dimensional universe using an overlap function to aggregate one-dimensional VPs. Additionally, an axiomatic fuzzy set (AFS) on the multi-dimensional universe is defined. Building on this foundation, algorithms for single-input single-output (SISO) and multi-input single-output (MISO) fuzzy inference are developed using AFSs, eliminating the need for subjective fuzzy implication operators. The FIS-AFSs, with its universal approximation property and theoretical approximation precision, are then analyzed. Experimental tests are conducted to evaluate the approximation capabilities of the FIS-AFSs. Results from both theoretical analysis and experimental testing demonstrate that FIS-AFSs can achieve high approximation precision. [ABSTRACT FROM AUTHOR]

The state of relaxation is an emotional condition that may be detected through various physical indicators in humans, such as sweat gland intensity, heart rate, breathing patterns, muscle tension and brain activity. Observation using physical patterns is challenging because each human has their own distinct pattern. In order to address this problem, neurologists are employing electroencephalography (EEG) to monitor the brain activity of patients. During this observation, neurologists depend on their own intuition to determine the relaxed state. However, the traditional observation method has drawbacks because each neurologist has their own subjective interpretation, which might lead to ambiguities. Thus, neurologists necessitate an automated recognition system capable of suggesting states of relaxation. To better assess the relaxed state, we divided the alpha band into two parts alpha sub-band: high and low alpha band. In order to obtain Spatio-Temporal features, both signals are transformed by wavelet. The ReliefF is used to select the features toward obtain optimal features. The maximum amplitude and standard deviation are two optimal features utilised as input to Mamdani-Intuitionistic Fuzzy Rules Set. The proposed approach is developed by integrating the fuzzy rules concept of Mamdani and Intuitionistic. In order to validate our model, we are collaborating with three neurologist experts and utilising majority decision to provide label annotation. According to this annotation, our model is performing well with an accuracy score of 92.45%. This investigation employs the DEAP public dataset. The level of accuracy seen in all examined subjects remained consistently high. [ABSTRACT FROM AUTHOR]

The endless adverse effects of air pollution incidents have raised significant public concerns in the past few decades. The measure of air pollution, that is, the air quality index (AQI), is highly volatile and associated with different kinds of uncertainties. Following this, the study and development of accurate fuzzy time series forecasting (TSF) methods for predicting the AQI have a significant role in air pollution control and management. Motivated by this, in this paper, a systematic study is made to evaluate the true potential of fuzzy TSF methods employing traditional fuzzy set (TFS), intuitionistic fuzzy set (IFS), hesitant fuzzy set (HFS), and neutrosophic fuzzy set (NFS) in forecasting the AQI. Two novel high‐order fuzzy TSF methods, TFS‐multilayer perceptron (MLP) and HFS‐MLP, are proposed employing TFS and HFS in which ratio trend variation of AQI data is used instead of original AQI, MLP is used to model the fuzzy logical relationships (FLRs), and none/mean of aggregated membership values are used while modeling the FLRs using MLP. The results from the proposed fuzzy TSF methods are compared with recently proposed fuzzy TSF methods employing TFS, IFS, and NFS and six popular machine learning models, including MLP, support vector regression (SVR), Bagging Regressors, XGBoost, long‐short term memory (LSTM), and convolutional neural network (CNN). The "Wilcoxon Signed‐Rank test" and "Friedman and Nemenyi hypothesis test" are applied to the results obtained by employing different ratios in the train‐validation‐test to draw decisive conclusions reliably. The simulation results show the statistical dominance of the proposed TFS‐MLP method over all other crisp and fuzzy TSF methods employed in this paper. [ABSTRACT FROM AUTHOR]

AbstractThis research examines the motivations and barriers to healthy consumption among young adults. To empirically determine these factors, hypotheses were stated and the model validation was performed through the Fuzzy Sets approach. Results show a positive effect of food-related self-efficacy (FRSE), consumer healthy habits (CHH) and the willingness to pay for healthy eating (WPHE) on healthy eating intention (HEI) and the attitude toward personalized nutrition (APN). Customer health concerns (CHC) moderated the correlation between CHH, WPHE and HEI. APN mediated the relationship between CHH, FRSE, customer perceived barriers (CPB), WPHE and HEI. Therefore, this study ascertains the significant determinants of healthy consumption among young adults. [ABSTRACT FROM AUTHOR]

The resilient framework of Linguistic Intuitionistic Fuzzy Sets (LIFSs) allows for the representation and management of uncertainties related to intuitionistic judgments and linguistic expressions. Recent advances in passive and active safety systems have reduced highway fatalities. Autonomous vehicles can improve safety, efficiency, and mobility by navigating traffic without a driver. One of the primary benefits associated with this technology is that it reduces the number of traffic collisions that result in millions of fatalities and numerous injuries. In this research work, we devise two novel aggregation operators: the linguistic intuitionistic fuzzy Dombi ordered weighted averaging (LIFDOWA) operator and the linguistic intuitionistic fuzzy Dombi ordered weighted geometric (LIFDOWG) operator, and explore their fundamental structural properties. We provide innovative score and accuracy functions for multiple attribute decision-making (MADM) problems within the framework of LIF knowledge. Moreover, we use these techniques to develop a specialized algorithm for MADM issues that addresses the complexities arising from ambiguous data during the selection process. We also demonstrate the effectiveness of our proposed methods by applying them to solve the MADM scenario of selecting an optimal approach to improve the credibility of autonomous vehicle control systems on a commercial scale. In addition, we also compare and evaluate the authenticity and practicability of the newly designed techniques in comparison to existing methodologies. [ABSTRACT FROM AUTHOR]

In this paper, we defined various fuzzy topological spaces on fuzzy digraph and study interrelation between them. Using adjacency relation on fuzzy vertices and fuzzy edges of fuzzy digraph we defined, namely two types of fuzzy topologies, left(right) fuzzy vertex topology and left(right) fuzzy edge topology on fuzzy digraph, respectively. We have obtained results related to fuzzy topology on fuzzy subdigraph H of fuzzy digraph G. Separation axioms T0, T1 and T2 for these fuzzy topological spaces are studied. Some characterization results for fuzzy topological spaces related to isomorphic fuzzy digraphs are given. Further, we have shown that two fuzzy digraphs are isomorphic if and only if their corresponding fuzzy topologies are homeomorphic, this relation on set of fuzzy digraphs forms an equivalence relation. We have also defined and studied fuzzy bitopological spaces on fuzzy digraphs. [ABSTRACT FROM AUTHOR]

To address the uncertainty and imprecision in a complex system, the concept of the linear Diophantine fuzzy set (LDFS) was introduced, which is a fuzzy set extension that eliminates the constraints of current methodologies and gives the decision-maker complete freedom to select the grades, producing results that are expressive and flexible. A circular intuitionistic fuzzy set (C-IFS) is depicted by a circle with the membership and non-membership values as its center coordinates, addressing the ambiguity regarding membership and non-membership values. In this paper, we propose the notion of a circular linear Diophantine fuzzy set (C-LDFS) as a hybrid structure of both circular intuitionistic fuzzy sets and linear Diophantine fuzzy sets. In addition, some fundamental set operations on C-LDFSs are presented. Furthermore, a similarity metric for C-LDFS is also introduced in the paper. Additionally, we confirm the axiomatic requirement of the similarity measure and highlight a few of its traits. Finally, the proposed similarity metric is implemented in clinical decision-making to illustrate its efficacy. [ABSTRACT FROM AUTHOR]

The concept of an involution or anti‐involution is a self‐inverse linear mapping that plays a prominent role in the theory of algebraic structures, particularly rings, hyperrings, ordered semigroups, and ordered semihypergroups. Nowadays, the study of involutions in ordered hyperstructures is a particular area of research in the field of hyperstructure theory, especially in ordered hyperstructures. In this research, we apply the concept of fuzzy sets to anti‐involution hyperideals of ordered anti‐involution LA‐semihypergroups. We shall propose the mathematical formulation of fuzzy anti‐involution hyperideals in ordered anti‐involution LA‐semihypergroups and provide some examples. This concept is a novel extension of fuzzy involution ordered semigroups. [ABSTRACT FROM AUTHOR]

The concept of the Dual-hesitant fermatean fuzzy set (DHFFS) represents a significant advancement in practical implementation, combining Fermatean fuzzy sets and Dual-hesitant sets. This new structure uses membership and non-membership hesitancy and is more adaptable for arriving at values in a domain. Since it has the capability to treat multiple fuzzy sets over the degrees of membership and non-membership, the DHFFS greatly improves the flexibility of approaches to tackle multiple-criteria decision-making (MCDM) problems. By applying generalized T‐norm (T) and T‐conorm (T*) operation, improved union and intersection formulas are derived. The proposed work adopts Hamacher operations such as Hamacher T-conorm (HT*) and Hamacher T-norm (HT) that are more efficient than conventional techniques. New aggregation operators such as Hamacher weighted arithmetic, geometric, power arithmetic, and power geometric are developed for DHFFS. These operators are most beneficial when dealing with a MCDM issue. A case study is used to demonstrate the approachs' accuracy and effectiveness in real-world decision-making. The comparative and sensitivity analysis results show that these operators are more effective than traditional methods. These results show that the proposed methods are efficient and can be applied in large-scale decision-making processes, strengthening the solutions' practical implications. [ABSTRACT FROM AUTHOR]

The Pythagorean fuzzy set and interval-valued intuitionistic fuzzy set are the basis of the interval-valued Pythagorean fuzzy set (IVPFS) which offers an effective approach to addressing the complex uncertainty in decision-analysis processes, making it applicable across a broad spectrum of applications. This paper introduces several aggregation operators within the IVPF framework, such as the interval-valued Pythagorean fuzzy SS power weighted average operator, and the interval-valued Pythagorean fuzzy SS power geometric operator using the notion of power aggregation operators through Schweizer and Sklar (SS) operations. The existence of SS t-norms and t-conorms in the IVPF framework for addressing multi-attribute decision-making problems gives the generated operator's ability to make the information aggregation approach more adaptable compared to other current ones. The application of the proposed approach holds the potential to enhance crop yield, optimize resource utilization, and contribute to the overall sustainability of agriculture. Additionally, sensitivity and comparative analyses are provided to clarify the stability and dependability of the results acquired through this approach. [ABSTRACT FROM AUTHOR]

Intelligent manufacturing enterprises play a crucial role in the modern industrial system and are key to high-quality economic development. However, most current research on intelligent manufacturing technology innovation focuses on single variables, lacking a comprehensive analysis from a linkage grouping perspective. This paper constructs an analytical framework for the technology innovation path of intelligent manufacturing enterprises from three dimensions: technology level, organization level, and environment level. Six antecedent variables are selected: R&D investment, digital transformation, human capital structure, profitability, government support, and competitive position. Using the fuzzy sets of qualitative comparative analysis (fsQCA) methodology, the paper examines the technological innovation paths of intelligent manufacturing enterprises in China. The research indicates that no single antecedent variable is necessary for high technological innovation; instead, the innovation path results from the synergistic effect of multiple conditions. The study identifies three paths leading to high technological innovation in intelligent manufacturing:"Government and Human Resource driven types," "Environmental-Organizational linkage types,"and"Organizational Resilience dominant types." This analysis provides reference suggestions for enterprises to adopt suitable development strategies based on their competitive positions. [ABSTRACT FROM AUTHOR]

Intuitionistic fuzzy graphs (IFGs) are a powerful tool for modeling uncertainty and complex relationships. They offer versatile frameworks for addressing real‐world challenges. In this research, we have introduced intuitionistic fuzzy vertex order coloring (IFVOC) and analyzed the alpha‐strong (α˜str), beta‐strong (β˜str), and gamma‐strong (γ˜str) vertices through their degree. We explored important theorems based on the types of strong vertices, broadening the scope of our study. We analyzed multiple IFG products to determine the most optimal network based on some important metrics, including the weight and total number of α˜str vertices, the chromatic number, and the weight of the graph's minimum spanning tree. We systematically evaluate different types of IFG products, such as conormal, modular, residue, and maximal, and consider their implications for fuzzy graph structure and connectivity. We have investigated new metrics to measure the presence and importance of α˜str vertices in the graph, assessing both their frequency and overall impact. Additionally, we have investigated the impact of product operations on the significance of α˜str nodes and the resultant minimum spanning tree, providing insights into the overall robustness and efficiency of each product. We have explored how variations in product operations impact the distribution of α˜str vertices and the characteristics of the minimum spanning tree and chromatic number, elucidating the trade‐offs between different product options. We have modeled the product graph as a network to represent complex systems composed of interconnected entities. The outcomes of this research contribute to the advancement of IFG theory and its use in various fields, such as network design, strategic planning, and system analysis. [ABSTRACT FROM AUTHOR]

The concept of probabilistic interval preference ordering sets (PIPOSs) provides a scientific and intuitive framework for solving real-life multi-criteria group decision-making problems. In some areas such as investment decision-making and supplier selection, PIPOSs have a wider application space, and the development of similarity and distance measures based on PIPOSs holds great significance. Similarity measure is a basic and prominent tool for dealing with imperfect and ambiguous information in fuzzy sets, but it can also be used to deal with uncertain information in preference ordering. These metrics play an important role in the actual decision-making process, as they effectively quantify the degree of similarity between two PIPOSs, and further allow for the prioritization of different scenarios. In this article, we sort out the definitions and arithmetic rules of PIPOSs, and creatively propose several new similarity measures based on PIPOSs. Then, we propose a group decision-making method based on similarity measures and conduct a comparative study with three existing similarity measures to illustrate its advantages over existing metrics. Finally, we confirm its validity through numerical illustrations in the case study, and also conduct a comparative assessment to verify the scientific validity and effectiveness of the newly introduced measure against the existing metrics. [ABSTRACT FROM AUTHOR]

The problem of insecurity is a global phenomenon that has several forms like terrorism, banditry, kidnappings, etc. Insecurity has taken hold in the Sub-Saharan Region of West Africa, especially in Nigeria, for over two decades. Nigeria's security crisis is more pronounced in the Northern Region, with a new wave in the North-Central Region of Nigeria. It is herculean to assess insecurity in the North-Central Region of Nigeria because of the region's fuzzy or imprecise nature of insecurity. This constitutes the rationale for deploying the Fermatean fuzzy technique to assess insecurity due to the capacity of the Fermatean fuzzy scheme to handle imprecision. To this end, a new Fermatean fuzzy distance metric is presented to evaluate insecurity in the North-Central Region of Nigeria using a multi-criteria decision-making technique. To express the logic for creating the new Fermatean fuzzy distance metric, some existing Fermatean fuzzy distance metrics are discussed, along with their drawbacks. The mathematical properties of the new technique are discussed, and the new method is applied computationally to assess insecurity in the North-Central Region of Nigeria. The data for the security assessment are collected via Fermatean fuzzy linguistic variables using the opinions of security experts and analyzed using the technique for order of preference by similarity to ideal solution, which is a commonly used multi-criteria decision-making method. Finally, the numerical validity of the new technique is expressed with comparative results, and the finding shows the benefit of the new distance approach over the existing methodologies. The outcome of the work will provide reliable traveling advisories for safe voyages within the region. [ABSTRACT FROM AUTHOR]