Intuitionistic Fuzzy Information Research Articles

Selection of an Appropriate Global Partner for Companies Using the Innovative Extension of the TOPSIS Method with Intuitionistic Hesitant Fuzzy Rough Information

In this research, we introduce the intuitionistic hesitant fuzzy rough set by integrating the notions of an intuitionistic hesitant fuzzy set and rough set and present some intuitionistic hesitant fuzzy rough set theoretical operations. We compile a list of aggregation operators based on the intuitionistic hesitant fuzzy rough set, including the intuitionistic hesitant fuzzy rough Dombi weighted arithmetic averaging aggregation operator, the intuitionistic hesitant fuzzy rough Dombi ordered weighted arithmetic averaging aggregation operator, and the intuitionistic hesitant fuzzy rough Dombi hybrid weighted arithmetic averaging aggregation operator, and demonstrate several essential characteristics of the aforementioned aggregation operators. Furthermore, we provide a multi attribute decision-making approach and the technique of the suggested approach in the context of the intuitionistic hesitant fuzzy rough set. A real-world problem for selecting a suitable worldwide partner for companies is employed to demonstrate the effectiveness of the suggested approach. The sensitivity analysis of the decision-making results of the suggested aggregation operators are evaluated. The demonstrative analysis reveals that the outlined strategy has applicability and flexibility in aggregating intuitionistic hesitant fuzzy rough information and is feasible and insightful for dealing with multi attribute decision making issues based on the intuitionistic hesitant fuzzy rough set. In addition, we present a comparison study with the TOPSIS approach to illustrate the advantages and authenticity of the novel procedure. Furthermore, the characteristics and analytic comparison of the current technique to those outlined in the literature are addressed.

An extended group decision-making algorithm with intuitionistic fuzzy set information distance measures and their applications

The intuitionistic fuzzy set is a generalization of the fuzzy set that performs noticeably better in expressing and managing uncertainty. The amount that one intuitionistic fuzzy set differs from the others is given by the distance measure. Certain distance measures that have been suggested by the various researchers do not satisfy the axioms of distance measures and also be counter-intuitive circumstances. In this paper we present a novel distance measure for intuitionistic fuzzy sets that is based on the difference between the cross-evaluation factor’s minimum and maximum, the membership degree and non-membership degree, respectively. The proposed measure satisfies all the axiomatic properties and also resolves the counter-intuitive cases. Consequently, this study provides an efficient symmetric distance formula for determining the distance between the information contained by intuitionistic fuzzy sets. By using numerical examples, it is shown that the new measurement is reliable. Also, we provide pattern recognition algorithms and employ them to solve diagnostic-related problems in medicine.

Intuitionistic fuzzy local information C-means algorithm for image segmentation

Image segmentation allows us to separate an image into distinct, non-overlapping parts by utilizing specific features such as hue, texture, and shape. The technique is prevalent in different domains, including target detection, medical imaging, and pattern recognition owing to its importance in analyzing the image. The fuzzy C-means (FCM) algorithm is a popular method for image segmentation and pattern recognition. However, uncertainty and unknown noise in the data impair the effectiveness of the algorithm. Alternatively, uncertainty in real world can be addressed by the intuitionistic fuzzy set (IFS). This article presents a new approach to image representation using IFS and local information about the image. We introduce the concept of filtering into the intuitionistic fuzzy set and utilize a specially designed exponential distance for IFS. We propose the intuitionistic fuzzy local information C-means (IFLICM) algorithm. The goal of IFLICM is to increase the tolerance to noise and the maintain the details in image better than existing FCM variants. We test the performance of our algorithm on a public dataset and compare it with existing FCM methods and Double Deep-Image-Prior (Double-DIP). The experimental results demonstrate that IFLICM is highly effective in image segmentation and outperforms existing methods.

An integrated soft computing-based criteria interdependencies and intuitionistic fuzzy information to evaluate the urban concrete bridge maintenance models

An integrated soft computing-based criteria interdependencies and intuitionistic fuzzy information to evaluate the urban concrete bridge maintenance models

Evaluating the Websites of Academic Departments Using the GRA Method

The academic world heavily relies on search engine referrals, making SEO particularly relevant in this sector. To effectively improve page rankings, SEO encompasses two primary categories of factors: 'on-page' and 'off-page. ‘The on-page' factors are elements directly controlled on the website itself. These include strategic keyword usage, content quality, meta tags, URL structure, and internal linking. Conversely, 'off-page' factors involve external elements that influence a website's ranking. Examples include acquiring quality backlinks, maintaining a strong social media presence, and managing online reputation. To attain the maximum benefits from SEO, it is essential to consider relevant factors and criteria. Employing MCDM techniques allows website owners to evaluate and prioritize various SEO elements effectively, enabling a strategic and data-driven approach to improve their web content's search engine rankings. Today, with the advancement and widespread adoption of information systems, the quantity of websites has risen significantly. According to World Wide Web estimates based on the page index by search engines like Google and Bing, the total number of web pages has reached an impressive 4.48 billion. However, this sheer volume of websites makes it challenging for visitors to promptly find the information they are looking for. Thankfully, search engines play a crucial role in helping users access the relevant information they seek quickly and efficiently. The purpose of this study is to explore the challenges of multiple attribute decision-making when dealing with intuitionistic fuzzy information. In this scenario, the attribute weights are not entirely known, and the attribute values are represented by intuitionistic fuzzy numbers. To determine the attribute weights, an optimization model is constructed based on the traditional Grey Relational Analysis (GRA) method's fundamental principles. The proposed method involves calculating the Grey Relational degree between each alternative and the positive-ideal solution and negative-ideal solution. This degree is then used to define a relative relational degree, which enables the ranking of all alternatives simultaneously with respect to both the positive-ideal solution (PIS) and negative-ideal solution (NIS). Alternative taken as Performance criteria(C1), Design criteria (C2), Content criteria (C3), Meta tags criteria (C4), Backlink criteria (C5). Evaluation preference taken as Abdullah Gul University, Turkey (A1); Adana Science and Technology University, Turkey (A2); Aksaray University, Turkey (A3); Alanya Alaaddin Keykubat University, Turkey (A4); Anadolu University, Turkey (A5).

Two de-I-fuzzification procedures for intuitionistic fuzzy information

In this paper, two procedures are proposed that transform intuitionistic fuzzy information into fuzzy information. Using the results obtained with the de-I-fuzzification procedures, formulas for intuitionistic fuzzy entropy are constructed.

Group Decision-Making Method with Incomplete Intuitionistic Fuzzy Soft Information for Medical Diagnosis Model

The medical diagnosis of many critical diseases is difficult as it usually requires the combined effort of several doctors. At this time, the process of medical diagnosis is actually a group decision-making (GDM) problem. In group medical diagnosis, considering doctors’ weight information and fusing the interaction relation of symptoms remain open issues. To address this problem, a group decision-making method for intuitionistic fuzzy soft environments is proposed for medical diagnosis because the intuitionistic fuzzy soft set (IFSS) integrates the advantages of the soft set and intuitionistic fuzzy set (IFS). Intuitionistic fuzzy soft weighted Muirhead mean operators are constructed by combining Einstein operations with the Muirhead mean (MM) operator, and some properties and results are revealed. A group medical diagnosis model with unknown doctor weight information and incomplete intuitionistic fuzzy soft information is proposed. Similarity measures of the intuitionistic fuzzy soft matrix (IFSM) given by the doctors are used to estimate the incomplete information. To take into account the advantages of objective weight and subjective weight, the combined weights of doctors are calculated based on the IFSMs’ similarity measure and doctors’ grades. The developed operators are then used to combine the evaluation information and handle the correlation of input arguments in the group medical diagnosis process. Finally, a numerical problem is selected to illustrate the superiority of the proposed approach compared to related methods. The combined weights are determined to overcome the shortcomings of the single-weight method to some extent. Meanwhile, the proposed method is more comprehensive, and can provide more flexible and reasonable choices for group medical diagnosis problems.

Attribute reduction based on intuitionistic fuzzy dominance mutual information in intuitionistic fuzzy information systems

Intuitionistic fuzzy information system (IFIS) is an extension of fuzzy information system that can represent more uncertain information and more accurately describe the essence of fuzziness. Attribute reduction is an important problem in processing and analyzing IFISs. The article tries to propose an attribute reduction method in view of intuitionistic fuzzy dominance mutual information in IFISs, whose information values are intuitionistic fuzzy numbers. First, an intuitionistic fuzzy dominance relation is established in IFISs according to intuitionistic fuzzy dominance degrees, and the intuitionistic fuzzy information structure generated by the intuitionistic fuzzy dominance relation is constructed. Then, the intuitionistic fuzzy dominance entropy and its variations are researched, and some of their properties are discussed. Subsequently, an attribute reduction method and its algorithm based on intuitionistic fuzzy dominance mutual information are given. Furthermore, numerical studies and statistical tests are presented to evaluate the performance of the proposed method. Theoretical research and experiments show that the raised attribute reduction method is applicable to IFISs.

Parametric circular intuitionistic fuzzy information measures and multi-criteria decision making with extended TOPSIS

Parametric circular intuitionistic fuzzy information measures and multi-criteria decision making with extended TOPSIS

Interval-valued intuitionistic fuzzy AROMAN method and its application in sustainable wastewater treatment technology selection

In the theory of interval-valued intuitionistic fuzzy set (IVIFS), the rating/grade of an element is the subset of the closed interval [0, 1], therefore the IVIFS doctrine is more useful for the decision expert to present their judgments in terms of intervals rather than the crisp values. The present work develops an integrated decision-making methodology for evaluating sustainable wastewater treatment technologies within the context of IVIFS. The proposed decision-making framework is divided into three stages. First, some Yager weighted aggregation operators and their axioms are developed to combine the interval-valued intuitionistic fuzzy information. These operators can offer us a flexible way to solve the realistic multi-criteria decision-making problems under IVIFS context. Second, an extension of Symmetry Point of Criterion model is introduced to determine the criteria weights under IVIFS environment. Third, an integrated alternative ranking order model accounting for two-step normalization (AROMAN) approach is proposed from IVIF information perspective. Next, the practicability and efficacy of the developed model is proven by implementing it on a case study of sustainable wastewater treatment technologies evaluation problem with multiple criteria and decision experts. Finally, comparative analysis is discussed to illustrate the consistency and robustness of the obtained outcomes.

Location selection for offshore wind power station using interval-valued intuitionistic fuzzy distance measure-RANCOM-WISP method

The development opportunities and high-performance capacity of offshore wind energy project depends on the selection of the suitable offshore wind power station (OWPS) location. The present study aims to introduce a decision-making model for assessing the locations for OWPS from multiple criteria and uncertainty perspectives. In this regard, the concept of interval-valued intuitionistic fuzzy set (IVIFS) is utilized to express uncertain information. To quantify the degree of difference between IVIFSs, an improved distance measure is proposed and further utilized for deriving the objective weights of criteria. Numerical examples are discussed to illustrate the usefulness of introduced IVIF-distance measure. The RANking COMparison (RANCOM) based on interval-valued intuitionistic fuzzy information is presented to determine the subjective weights of criteria. With the combination of objective and subjective weights of criteria, an integrated weighting tool is presented to find the numeric weights of criteria under IVIFS environment. Further, a hybrid interval-valued intuitionistic fuzzy Weighted integrated Sum Product (WISP) approach is developed to prioritize the OWPS locations from multiple criteria and uncertainty perspectives. This approach combines the benefits of two normalization tools and four utility measures, which approves the effect of beneficial and non-beneficial criteria by means of weighted sum and weighted product measures. Further, the developed approach is applied to the OWPS location selection problem of Gujarat, India. Sensitivity and comparative analyses are presented to confirm the robustness and stability of the present WISP approach. This study provides an innovative decision analysis framework, which makes a significant contribution to the OWPS locations assessment problem under uncertain environment.

Schweizer-Sklar power aggregation operators based on complex intuitionistic fuzzy information and their application in decision-making

In 1960, Schweizer and Sklar introduced the novel Schweizer-Sklar t-norm and t-conorm which is used in the construction of aggregation operators. Schweizer-Sklar norms are more general than algebraic norms and Einstein norms. Additionally, computing the power operators based on the Schweizer-Sklar norms for complex Atanassov intuitionistic fuzzy (CA-IF) set is very awkward and complicated. In this manuscript, firstly, we propose the Schweizer-Sklar operational laws for CA-IF values, and secondly, we develop the CA-IF Schweizer-Sklar power averaging (CA-IFSSPA) operator, CA-IF Schweizer-Sklar power ordered averaging (CA-IFSSPOA) operator, CA-IF Schweizer-Sklar power geometric (CA-IFSSPG) operator, and CA-IF Schweizer-Sklar power ordered geometric (CA-IFSSPOG) operator. Some suitable and dominant properties for the above operators are also discussed. Furthermore, to simplify the above operators, we develop the procedure of decision-making technique, called multi-attribute decision-making (MADM) methods based on the proposed operators based on CA-IF values. Finally, we compare the proposed methods with some existing methods to describe the efficiency and capability of the discovered approaches by some examples.

Attitude-Based Intuitionistic Fuzzy Graph Model for Conflict Resolution With Soft Consensus: Application to Dam Construction Projects

Construction of dam projects built on international rivers usually faces many conflicts due to different interests, values, and preferences of social entities. The graph model for conflict resolution (GMCR) is a powerful and flexible tool to model and analyze strategic conflicts. In this study, we introduce a GMCR model to address construction conflicts among different social entities. To process uncertain and vague information in preferences, this study allows decision-makers to use intuitionistic fuzzy information to express their preference information. First, we define necessary definitions, including the intuitionistic relative certainty of preference (IRCP), intuitionistic satisficing threshold (IST), and intuitionistic unilateral improvement (IUI). When using a graph model to represent real-world disputes, a decision-maker (DM) may have different attitudes toward other DMs. To analyze the influence of attitudes, we originally introduced the concept of soft consensus in GMCR. Then, three kinds of attitudes, i.e., positive attitude, neutral attitude, and negative attitude, are identified. Next, we propose four intuitionistic stability definitions considering the attitudes of DMs. Finally, an illustrative example regarding the Xayaburi Dam dispute that occurred in Indochina Peninsula is provided to demonstrate the applicability of the proposed model.

Retracted: A New Approach to Model Machine Learning by Using Complex Bipolar Intuitionistic Fuzzy Information

Retracted: A New Approach to Model Machine Learning by Using Complex Bipolar Intuitionistic Fuzzy Information

Archimedean Heronian mean operators based on complex intuitionistic fuzzy sets and their applications in decision-making problems

In this article, we derive the Archimedean aggregation operators for complex intuitionistic fuzzy sets, for this, first, we evaluate some Archimedean operational laws based on complex intuitionistic fuzzy values and then we discuss their special cases because the Archimedean norms are the general form of all existing norms, for instance, algebraic, Einstein, Hamacher, and Frank operational laws. Furthermore, we present the complex intuitionistic fuzzy Archimedean Heronian aggregation operator and complex intuitionistic fuzzy weighted Archimedean Heronian aggregation operator. Several special cases and the basic properties of the above-proposed operators are also diagnosed, because proposing the Heronian mean operators based on Archimedean norms are very challenging and complicated tasks, because of their features and structure. Additionally, a decision-making process is developed under the identified operators by using complex intuitionistic fuzzy information. Finally, we illustrate several examples to show the multi-attribute decision-making technique is more flexible than the prevailing works with the help of sensitive analysis between explored and certain prevailing works.

Intuitionistic fuzzy geometric aggregation operators based on Yager’s triangular norms and its application in multi-criteria decision making

Intuitionistic fuzzy (IF) information aggregation in multi-criteria decision making (MCDM) is a substantial stream that has attracted significant research attention. There are various IF aggregation operators have been suggested for extracting more informative data from imprecise and redundant raw information. However, some of the aggregation techniques that are currently being applied in IF environments are non-monotonic with respect to the total order, and suffer from high computational complexity and inflexibility. It is necessary to develop some novel IF aggregation operators that can surpass these imperfections. This paper aims to construct some IF aggregation operators based on Yager’s triangular norms to shed light on decision-making issues. At first, we present some novel IF operations such as Yager sum, Yager product and Yager scalar multiplication on IF sets. Based on these new operations, we propose the IF Yaeger weighted geometric operator and the IF Yaeger ordered weighted geometric operator, and prove that they are monotone with respect to the total order. Then, the focus on IF MCDM have motivated the creation of a new MCDM model that relies on suggested operators. We show the applicability and validity of the model by using it to select the most influential worldwide supplier for a manufacturing company and evaluate the most efficient method of health-care disposal. In addition, we discuss the sensitivity of the proposed operator to decision findings and criterion weights, and also analyze it in comparison with some existing aggregation operators. The final results show that the proposed operator is suitable for aggregating both IF information on “non-empty lattice" and IF data on total orders.

Exploring static rebalancing strategies for dockless bicycle sharing systems based on multi-granularity behavioral decision-making

In the continuously evolving context of urbanization, more people flock to cities for job opportunities and an improved quality of life, resulting in undeniable pressure on transportation networks. This leads to severe daily commuting challenges for residents. To mitigate this urban traffic pressure, most cities have adopted urban dockless bicycle sharing systems (UDBSS) as an effective measure. However, making accurate decisions regarding UDBSS demand in different city locations is crucial, as incorrect choices can worsen transportation problems, causing difficulties in finding bicycles or excessive deployments leading to disorderly accumulation. To address this decision-making challenge, it is essential to consider uncertain factors like daily weather, temperature, and workdays. To tackle this effectively, we construct an adjustable multi-granularity (MG) complex intuitionistic fuzzy (CIF) information system using complex intuitionistic fuzzy sets (CIFSs). This system objectively determines classification thresholds using an evaluation-based three-way decision (TWD) method, creating adjustable MG CIF probability rough sets (PRSs). Additionally, to recognize the irrationality of decision-makers (DMs), we propose a method that combines prospect theory (PT) with regret theory (RT), providing a more comprehensive understanding of the influence of DMs' psychological factors on decision outcomes. Building upon these foundations, we present static rebalancing strategies for UDBSS based on MG PRSs and prospect-regret theory (P-RT) within the CIF information system. Finally, using UDBSS data collected from various sensors, we conduct experimental analysis to verify its feasibility and stability. In summary, this approach considers residents’ daily usage preferences, including bicycles utilization and return, with the aim of minimizing unmet resident demands and predicting usage patterns for the next day. It effectively addresses the issue of UDBSS distribution inefficiencies and holds a significant advantage in prediction, making it suitable for broader applications in transportation systems and contributing to the establishment of more advanced modern intelligent transportation systems (MITSs) in the future.

An Interactive Data-Driven Multiple-Attribute Decision-Making Technique With Interval-Valued Intuitionistic Fuzzy Information for Satisfaction Evaluation of Public Art in Urban Squares

An Interactive Data-Driven Multiple-Attribute Decision-Making Technique With Interval-Valued Intuitionistic Fuzzy Information for Satisfaction Evaluation of Public Art in Urban Squares

A quantitative risk assessment framework for reverse logistics of plastic packaging wastes via a multi-stage intuitionistic fuzzy information: a case study of plastic recycling industry in Thailand

A quantitative risk assessment framework for reverse logistics of plastic packaging wastes via a multi-stage intuitionistic fuzzy information: a case study of plastic recycling industry in Thailand

Linguistics complex intuitionistic fuzzy aggregation operators and their applications to plastic waste management approach selection

<p>Linguistic complex intuitionistic fuzzy aggregation operators are a novel idea for the description of intuitionistic fuzzy information, where linguistic complex concepts are used to describe membership and non-membership. This can convey the hazy information more clearly, and some findings on linguistic complex intuitionistic fuzzy aggregation operators have been attained. Nonetheless, several linguistic complex intuitionistic fuzzy aggregation operators in the literature are founded on conventional operational principles, which have certain limitations when used to multi-attribute group decision-making (MAGDM). In this study, we presented some improved operating rules based on linguistic complex intuitionistic fuzzy variables (LCIFVs) and changed their features to address these issues. Next, we created a few aggregation operators, such as the enhanced linguistic complex intuitionistic fuzzy weighted average (LCIFWA) and the linguistic complex intuitionistic fuzzy ordered weighted averaging (LCIFOWA) operator, to fuse the decision information represented by LCIFVs. We also demonstrated that they had a few favorable qualities. We introduced many novel approaches to address the MAGDM issues in the context of the linguistic complex intuitionistic fuzzy environment, based on the LCIFOWA and linguistic complex intuitionistic fuzzy ordered weighted geometric (LCIFOWG) operators. In short, we employed few real-world scenarios to demonstrate the viability and soundness of the suggested techniques through comparison with other approaches. This unique technique has been applied to plastic waste management selection, and the results are more accurate than the previously used materials and methods.</p>