Imprecise Information Research Articles

Application of GRA Method in Multi-Attribute Decision Making Problem Base on Picture Fuzzy Choquet Integral Information

Picture fuzzy set (PFS) is the generalize structure over existing structures of fuzzy sets to arrange uncertainty and imprecise information in the decision making problem. Picture fuzzy set is a direct extension of the intuitionistic fuzzy set (IFS) that can model uncertainty in situations with multiple types of answers, such as yes, no, abstain, and refuse. In this paper, we propose Choquet integral (CI) for the picture fuzzy set (PFS). Then, we defined some basic operational laws for picture fuzzy set. Also, based on the defined operational laws, we proposed picture fuzzy averaging (PFA) and picture fuzzy Choquet integral weighted averaging operator (PFCIWAO) along with their basic properties. Also, we propose the normalized Hamming distance and normalized Euclidean distance for the picture fuzzy numbers. Viewing the electiveness of the picture fuzzy set, we proposed a decision-making approach for the multi-criteria decision-making problems. We also propose the GRA method using Choquet integral for dealing uncertainty in decision making problems under picture fuzzy information. Lastly, we illustrate an example show the effectiveness and reliability of the proposed method.

A new MAGDM method with 2-tuple linguistic bipolar fuzzy Heronian mean operators.

In this article, we introduce the 2-tuple linguistic bipolar fuzzy set (2TLBFS), a new strategy for dealing with uncertainty that incorporates a 2-tuple linguistic term into bipolar fuzzy set. The 2TLBFS is a better way to deal with uncertain and imprecise information in the decision-making environment. We elaborate the operational rules, based on which, the 2-tuple linguistic bipolar fuzzy weighted averaging (2TLBFWA) operator and the 2-tuple linguistic bipolar fuzzy weighted geometric (2TLBFWG) operator are presented to fuse the 2TLBF numbers (2TLBFNs). The Heronian mean (HM) operator, which can reflect the internal correlation between attributes and their influence on decision results, is integrated into the 2TLBF environment to analyze the effect of the correlation between decision factors on decision results. Initially, the generalized 2-tuple linguistic bipolar fuzzy Heronian mean (G2TLBFHM) operator and generalized 2-tuple linguistic bipolar fuzzy weighted Heronian mean (G2TLBFWHM) operator are proposed and properties are explained. Further, 2-tuple linguistic bipolar fuzzy geometric Heronian mean (2TLBFGHM) operator and 2-tuple linguistic bipolar weighted geometric Heronian mean (2TLBFWGHM) operator are proposed along with some of their desirable properties. Then, an approach to multi-attribute group decision-making (MAGDM) based on the proposed aggregation operators under the 2TLBF framework is developed. At last, a numerical illustration is provided for the selection of the best photovoltaic cell to demonstrate the use of the generated technique and exhibit its adequacy.

Interval‐Valued q‐Rung Orthopair Fuzzy QUALIFLEX Decision Analysis Method with Dombi Operators

As a new and useful decision tool, interval‐valued q‐rung orthopair fuzzy set (IVq‐ROFS), has strong ability to address uncertain or imprecision information in practical decision‐making processes. This paper develops a new interval‐valued q‐rung orthopair fuzzy QUALIFLEX decision analysis method with Dombi operators for handling the group decision‐making problems with IVq‐ROFSs data. We also conduct a case study on the health management of hypertensive patients for demonstrating the feasibility and superiority of the developed method. The main contributions of this paper are as follows: (1) we first propose interval‐valued q‐rung orthopair fuzzy Dombi operational laws and investigate their desirable properties; (2) using the developed Dombi operational laws, we develop an interval‐valued q‐rung orthopair fuzzy Dombi weighted average (IVq‐ROFDWA) operator, and we prove that the IVq‐ROFDWA operator satisfies the properties of idempotency, commutativity, boundedness, and monotonicity; (3) we introduce the concept of interval‐valued q‐rung orthopair fuzzy ideal group opinions with the help of IVq‐ROFDWA operator and propose a new weight‐determining method for identifying the weights of experts; (4) we develop a new interval‐valued q‐rung orthopair fuzzy distance measure. With the help of the developed distance measure, we calculate interval‐valued q‐rung orthopair fuzzy concordance/discordance index under each permutation, and then a new interval‐valued q‐rung orthopair fuzzy QUALIFLEX decision analysis method is developed for addressing the complex multiattribute group decision‐making problems with IVq‐ROFSs.

Novel EDAS Methodology Based on Single‐Valued Neutrosophic Aczel‐Alsina Aggregation Information and Their Application in Complex Decision‐Making

In this article, we proposed an extended EDAS (Evaluation based on Distance from Average Solution) method based on the single‐valued neutrosophic (SVN) Aczel‐Alsina aggregation information. The fundamental concept of a single‐valued neutrosophic (SVN) set is a universal mathematical tool for effectively managing uncertain and imprecise information. To accomplish our goal, we first extend the Aczel‐Alsina t‐norm and t‐conorm to SVN scenarios and introduce a few new SVN operations on which we construct novel SVN aggregation operators. Furthermore, a decision support strategy is built in the SVN framework using the EDAS methodology and the suggested Aczel‐Alsina aggregation operators. This method computes the aggregated outcomes of each investigated alternative, as well as their score values. Finally, to demonstrate the functionality of the developed SVN‐ EDAS, an application has been made related to the role of commercial banks in providing loans to their customers, which has recently affected our world, and the results are compared with other existing methods. The results suggest that the proposed method may overcome the inadequacies of the existing decision method’s lack of decision flexibility by using SVN aggregation operators.

New Product Design Using Chebyshev’s Inequality Based Interval-Valued Intuitionistic Z-Fuzzy QFD Method

In Quality function deployment (QFD) approach, customers tend to express their needs in linguistic terms rather than exact numerical values and these needs generally contain vague and imprecise information. To overcome this challenge and to use the method more effectively for complex customer-oriented design problems, this paper introduces a novel intuitionistic Z-fuzzy QFD method based on Chebyshev’s inequality (CI) and applies it for a new product design. CI provides the assignment of a more objective reliability function. The reliability value is based on the maximum probability obtained from CI. Then, the expected values of lower and upper bounds of interval-valued intuitionistic fuzzy (IVIF) numbers are determined. A competitive analysis among our firm and competitor firms and an integrative analysis for the different functions of QFD is presented. The proposed Z-fuzzy QFD method is applied to the design and development of a hand sanitizer for struggling with COVID-19.

Site Selection for an Offshore Wind Power Station Under a Fuzzy Environment

The Malaysian government’s support for offshore wind power production has led to an increase in a few proposals. An important factor in the overall efficiency of any offshore wind farm is the site selection process, which is a multi-criteria decision-making (MCDM) task. However, classical MCDM techniques often fail to choose a suitable site because of three main challenges. First, compensation is regarded as a problem in the processing of information. Second, data usage and data leakage are often ignored in the decision-making process. Third, interaction difficulty in fuzzy environments is easily ignored. This study provides a framework for making site selection decisions for offshore wind farms while addressing the constraints. Fuzzy VIKOR is used in the second stage of the AHP process to analyze the site’s results with respect to evaluation criteria for offshore wind farms. A comprehensive index system, which incorporates the veto criteria and evaluation criteria for selecting offshore wind power station sites, is devised. Then, the system is used to transmit imprecise information to decision makers by using a triangular fuzzy set. Likelihood-based valued comparisons indicate that imprecise choice information can be correctly used, and issues of information loss can be logically avoided. A case study of Malaysia is used to demonstrate the validity and practicality of the site selection technique. This research offers a theoretical basis for accurate offshore wind power evaluation in Malaysia.

An Optimization Design Method of Express Delivery Service Based on Quantitative Kano Model and Fuzzy QFD Model

Service quality is the soul of express enterprises forever. It is of great practical significance for winning customer satisfaction, improving the market competition, and realizing sustainable performance. Unlike tangible products, express delivery service has the characteristics of intangibility, heterogeneity, indivisibility, and instability. While customer demands are complex and changeable and unpredictable, incorporating complex customer requirements into service design has been a growing interest of researchers and practitioners. This paper proposed an optimization design framework based on the quantitative Kano (QKNO) and the fuzzy quality function deployment (FQFD) to effectively achieve the best matching of enterprise service elements under the uncertainty and imprecise judgment information. An empirical study is conducted to verify the feasibility of the proposed approach. The results show that the framework could guide the express company to prioritize the enterprise service elements to maximize customer satisfaction and provide a reasonable budget allocation scheme to set the best resources match. It has theoretical and practical meaning for express enterprises to implement customization service strategy and improve service quality under the limited budget, which could be further extended to other service industries to make optimization decisions.

Superpixel-Based Weighted Collaborative Sparse Regression and Reweighted Low-Rank Representation for Hyperspectral Image Unmixing

Sparse unmixing with a semisupervised fashion has been applied to hyperspectral remote sensing imagery. However, the imprecise spatial contextual information, the lack of global feature and the high mutual coherences of a spectral library greatly limit the performance of sparse unmixing. In order to address these prominent problems, a new paradigm to characterize sparse hyperspectral unmixing is proposed, namely, the superpixel-based weighted collaborative sparse regression and reweighted low-rank representation unmixing (SBWCRLRU). In this method, the weighted collaborative sparse regression explores the pixels shared the same support set to help the sparsity of abundance fraction, and the reweighted low rank representation minimizes the rank of the abundance matrix to promote the spatial consistency of the image. Meanwhile, superpixel segmentation is adopted to cluster the pixels into different spatial homogeneous regions to further improve the unmixing performance. Extensive experiments results conducted on both synthetic and real data demonstrate the effectiveness of the proposed SBWCRLRU. It can not only improve the performance of hyperspectral unmixing but also outperform the existing sparse unmixing approaches.

A novel decision making technique based on spherical hesitant fuzzy Yager aggregation information: application to treat Parkinson's disease

<abstract><p>The concept of spherical hesitant fuzzy set is a mathematical tool that have the ability to easily handle imprecise and uncertain information. The method of aggregation plays a great role in decision-making problems, particularly when there are more conflicting criteria. The purpose of this article is to present novel operational laws based on the Yager t-norm and t-conorm under spherical hesitant fuzzy information. Furthermore, based on the Yager operational laws, we develop the list of Yager weighted averaging and Yager weighted geometric aggregation operators. The basic fundamental properties of the proposed operators are given in detail. We design an algorithm to address the uncertainty and ambiguity information in multi-criteria group decision making (MCGDM) problems. Finally, a numerical example related to Parkinson disease is presented for the proposed model. To show the supremacy of the proposed algorithms, a comparative analysis of the proposed techniques with some existing approaches and with validity test is presented.</p></abstract>

A novel picture fuzzy Aczel-Alsina geometric aggregation information: Application to determining the factors affecting mango crops

<abstract><p>Picture fuzzy (PF) sets are extremely reasonable to represent the uncertain, imprecise, and inconsistent information that exists in scientific and engineering fields. To meet decision makers' preference selection, the operational flexibility of aggregation operators shows its importance in dealing with the flexible decision-making problems in the PF environment. With assistance from Aczel-Alsina operations, we introduce the aggregation strategies of PFNs. We initially broaden the Aczel-Alsina norms to PF situations and present a few new operations of PFNs in view of which we build up a few new PF aggregation operators, for instance, the PF Aczel-Alsina weighted geometric, order weighted geometric, and hybrid weighted geometric operators. Furthermore, a decision support approach has been developed using the proposed aggregation operators under the PF environment. In this method, the aggregated results of each evaluated alternative are determined, and their score values are obtained. Then, all alternatives were ranked in decreasing order, and the best one was determined based on the highest score value. An illustrative example related to mango production is presented to investigate the most influential factor that resulted in mango production minimization. Finally, a comparison study was conducted on the proposed decision support method and the existing relative techniques. The result shows that the proposed method can overcome the insufficiency of lacking decision flexibility in the existing MAGDM method by the PF weighted geometric aggregation operators.</p></abstract>

Neutrosophic Image Segmentation: An Approach for the Treatment of Uncertainty in Multimodal Information Systems

Information explosion in this era has led to the proliferation of digital data in form of images, text, video, and audio. Uncertainty is a major issue in information access and retrieval models, and incomplete information needs to be treated in information systems because imprecision indicates the existence of a value that cannot be measured. There is no denial of the fact that uncertainty puts a hindrance in obtaining information in real-time systems, and as per knowledge rarely does any study of information retrieval using image segmentation treat imprecise and inconsistent information inherited in information systems. This work proposes to transform images in the neutrosophic domain for the treatment of uncertainty that persists in information recovery. Later, the image is segmented using the neutrosophic segmentation algorithm and its results are compared with the Modified Fuzzy c-Means segmentation algorithm, which is the earlier used segmentation algorithm in information systems. The experiment is conducted on a variety of multimodal images from the Berkeley Segmentation Dataset and Benchmark, showing the effectiveness of the proposed method for information systems. The proposed image segmentation using neutrosophy seems to yield a smaller error of 0.011, but the error obtained using the fuzzy c-means (MFCM) method is 0.13, which is larger than the proposed approach. The work also demonstrates how well neutrosophic segmentation can segment images having different noise levels as well as clean images. The results show that the proposed algorithm yields the most accurate segmented image for feature extraction which can be utilized while designing effective information systems.

An Extended Fuzzy CPT-TODIM Model Based on Possibility Theory and its Application to Air Target Dynamic Threat Assessment

Dynamic target threat assessment can be regarded as a classical multiple attribute group decision-making (MAGDM) issue. Triangular fuzzy numbers (TFNs) can fully describe imprecise and fuzzy information for target threat assessment. Thus, to consider the changing trend of target detection information in a certain period and the influence of commanders’ psychological factors on target threat assessment results, this study proposes an extended CPT-TODIM method based on possibility theory (PT-CPT-TODIM) to solve target threat assessment. Of note, the CRITIC method is used to determine the attribute weights of the probability mean matrix and the probability standard deviation matrix, which can make the target threat assessment result more rational. A numerical example and comparison analysis demonstrate the efficiency and feasibility of the newly proposed CPT-TODIM method.

Decision Analysis Approach Based on 2‐Tuple Linguistic m‐Polar Fuzzy Hamacher Aggregation Operators

This research article is devoted to presenting the concept of 2‐tuple linguistic m‐polar fuzzy sets (2 TL m FSs) and introducing some fundamental operations on them. With 2 TL m FSs, we shall be able to capture imprecise information with high generality. With the appropriate operators, we shall be able to apply 2 TL m FSs in decision‐making efficiently. The aggregation operators that we propose are the 2 TL m F Hamacher weighted average (2 TL m FHWA) operator, 2 TL m F Hamacher ordered weighted average (2 TL m FHOWA) operator, 2 TL m Hamacher hybrid average (2 TL m FHHA) operator, 2 TL m F Hamacher weighted geometric (2 TL m HWG) operator, 2 TL m Hamacher ordered weighted geometric (2 TL m HOWG) operator, and 2 TL m F Hamacher hybrid geometric (2 TL m FHHG) operator. We investigate their properties, including the standard cases of monotonicity, boundedness, and idempotency. Then we develop an algorithm to solve multicriteria decision‐making problems formulated with 2 TL m F information. The 2 TL m F data in multiattribute decision‐making are merged with the help of aggregation operators, and we consider the particular instances of the 2 TL m FHA and 2 TL m FHG operators. The influence of the parameters on the outputs is explored with a numerical simulation. Moreover, a comparative study with existing methods was performed in order to show the applicability of the proposed model and motivate the discussion about its virtues and advantages. The results confirm that the model here developed is reliable for decision‐making purposes.

Robust tube-based LPV-MPC for autonomous lane keeping

This paper proposes a control architecture for autonomous lane keeping by a vehicle. In this paper, the vehicle dynamics consist of two parts: lateral and longitudinal dynamics. Therefore, the control architecture comprises two subsequent controllers. A longitudinal model predictive control (MPC) makes the vehicle track the desired longitudinal speeds that are assumed to be generated by a speed planner. The longitudinal speeds are then passed to a lateral MPC for lane keeping. Due to the dependence of the lateral dynamics on the longitudinal speed, they are represented in a linear parameter-varying (LPV) form, where its scheduling parameter is the longitudinal speed of the vehicle. In order to deal with the imprecise information of the future longitudinal speed (the scheduling parameter), a bound of uncertainty is considered around the nominal trajectory of the future longitudinal velocities. Then, a tube-based LPV-MPC is adopted to control the lateral dynamics for attaining the lane keeping goal. In the end, the effectiveness of the proposed methods is illustrated by carrying out simulation tests.

Mobile Application for Incident Reporting

In the Philippines, reporting an incident always depends on self-reporting to the nearest law enforcer's office or calling a channel using a mobile phone. 911 is the National Emergency hotline to get assistance when an emergency occurs. However, the emergency hotline operated by the Emergency Network Philippines (ENP), cannot retrieve the reporter's location details immediately. Only when the reporters describe the exact location clearly. Yet, many circumstances that the reporters do not know when they are, or sometimes they have imprecise position information. Then, the law enforcers team may not be able to come to the right place efficiently on time. The incident reporting application incorporates the three types of incidents, classified as public disturbance, ordinance violation, and crime incident. To report an incident the application will automatically get the latitude and longitude of the mobile user or an option to manually pinned the location on the google map include also the incident type, description, and photos will be sent to the nearest barangay responder officer. The barangay responder officer able to request a backup officer, the rescue emergency unit such as a hospital ambulance or firefighters, or transfer a report to the nearest police station. The system also manages web admin for responder locations and generates statistical reports including charts and graphs. The positive feedback of the participants during the evaluation stage signifies that the application was accepted as tested and verified by the evaluation results.

A Multicriteria Approach to Modelling Pandemic Response under Strong Uncertainty: A Case Study in Jordan

In responding to the COVID-19 pandemic, evidence-based policymaking and risk mitigation have been confronted with limited decision-making mechanisms under conditions of increased uncertainty. Such methods are particularly called for in contexts where reliable data to a large extent are missing and where the chosen policy would impact a variety of sectors. In this paper, we present an application of an integrated decision-making framework under ambiguity on how to contain the COVID-19 virus spread from a national policy point of view. The framework was applied in Jordan and considered both local epidemiologic and socioeconomic estimates in a multistakeholder multicriteria context. In particular, the cocreation process for eliciting attitudes, perceptions, and preferences amongst relevant stakeholder groups has often been missing from policy response to the pandemic, even though the containment measures’ efficiency largely depends on their acceptance by the impacted groups. For this, there exist several methods attempting to elicit criteria weights, values, and probabilities ranging from direct rating and point allocation methods to more elaborated ones. To facilitate the elicitation, some of the approaches utilise elicitation methods whereby prospects are ranked using ordinal importance information, while others use cardinal information. Methods are sometimes assessed in case studies or more formally by utilising systematic simulations. Furthermore, the treatment of corresponding methods for the handling of the alternative’s values has sometimes been neglected. We demonstrate in our paper an approach for cardinal ranking in policy decision making in combination with imprecise or incomplete information concerning probabilities, weights, and consequences or alternative values. The results of our cocreation process are aggregated in the evaluation of alternative mitigation measures for Jordan, showcasing how a multistakeholder multicriteria decision mechanism can be employed in current or future challenges of pandemic situations, to facilitate management and mitigation of similar crises in the future, in any region.

A New Integrated FUCOM–CODAS Framework with Fermatean Fuzzy Information for Multi-Criteria Group Decision-Making

Smartphones have become an inevitable part of every facet of modern society. The selection of a particular smartphone brand from multiple options that are available is a complex and dynamic decision-making problem, involving multiple conflicting criteria that are associated with imprecise asymmetric information imposed by the uncertainty of the consumers. In this paper, we propose a novel hybrid full consistency method (FUCOM) and a combinative distance based assessment (CODAS) based on the multi-criteria group decision-making (MAGDM) framework in the Fermatean fuzzy (FF) domain for smartphone brand selection. We derive the criteria using the UTAUT2 (unified theory of acceptance and ese of technology) model. A group of 15 decision makers (DMs) participated in our study. We compare 14 leading smartphone brands in India and find that the brands having superior features of a good quality and selling a brand image at a affordable price outperform other smartphones. To check the validity of our framework, we compare the results using extant multi-criteria decision-making (MCDM) models. We observe our model provides a consistent solution. Furthermore, we carry out a sensitivity analysis for ascertaining the robustness and stability of the results generated by our model. The results of the sensitivity analysis show that our proposed framework delivers a stable and robust solution.

Adaptive query relaxation and top‐ k result sorting of fuzzy spatiotemporal data based on XML

With the increasing popularity of Extensible Markup Language (XML) for data representation, there is a lot of interest in searching XML data. Due to the structural heterogeneity of XML, it is daunting for users to formulate exact queries and search for accurate answers. Especially when performing queries on fuzzy spatiotemporal data, the imprecision and uncertainty of information make the amount of query results is small or even empty. In this case, query relaxation is necessary. Existing efforts are mainly made on expanding simple queries. Once there are multiple conditions in the query, the existing algorithms often have poor performance. To address this problem, we first propose an adaptive query relaxation algorithm based on XML, which can handle complex queries efficiently. The approach does not depend on any specific domain or user, it can adaptively relax the initial query requirements, and classify the results into two sets after query relaxation. Moreover, the algorithm prestores the potential matching objects in the auxiliary structure, which provides support for efficient query. In addition, after query relaxation, we present the result sorting algorithm to address the problem of information overload. Finally, we report experimental results to show that our algorithms are superior to previous approaches.

Fuzzy logic expert system for evaluating the activity of university teachers

Assessing the performance of academics at different levels is increasingly difficult to achieve using traditional methods based mainly on numerical scores in evaluating teaching and research activity. The indexing of academic performance in various international databases with impact indices at different scales has led to the need for advanced computer models, such as expert systems based on fuzzy logic, proposed in this research, which address the evaluation of teachers even in the face of imprecise information and under conditions of uncertainty. In this research, as a contribution and novelty, a fuzzy logic model was developed in which an algorithm was simulated and implemented in Matlab using the Mandami toolkit, which allows inference of the rules of fuzzy logic and visualization. 3D. The system implementation was done by software in Matlab environment, using systems with fuzzy Mandami logic. The result of this pilot study was to test and validate the proposed model through a graphical interface, giving the results according to minimum criteria and with additional explanations.

Calculation of centroid of high dimensional fuzzy number and application

In this paper, the conception of centroid of $n$-dimensional fuzzy number is introduced viaregarding its membership function as the density function on its support set, and some properties of it are obtained. Compared with the mean of the multi dimensional fuzzy number, the centroid takes into account the overall relationship between the edge membership functions of the membership function of the multi dimensional fuzzy number. Therefore, it can approximate (characterize) the fuzzy number more objectively and reasonably than using the mean of the multi dimensional fuzzy number. The most important work of this paper is that for two special kinds of multi dimensional fuzzy numbers (fuzzy $n$-cell numbers and fuzzy $n$-ellipsoid numbers), we respectively give calculation formulas, which can be used conveniently in application since the formulas are based on a definite integral of the level set functions of the multi dimensional fuzzy number on the unit interval $[0,1]$, rather than the multiple integral of the membership function of the multi dimensional fuzzy number itself on its support set. Then, by using the calculation formulas, we obtain another special property of the centroid for fuzzy $n$-cell number and fuzzy $n$-ellipsoid number. Finally, as an example of application, by using the centroid of multi dimensional fuzzy number, we define a fuzzy order on $n$-dimensional fuzzy number space, which can be used to rank uncertain or imprecise multichannel digital information.