Using self-powered sensors, traffic data may be collected continuously, efficiently, and sustainably once connected autonomous vehicles (CAVs) are a part of metaverse technology. Metaverse self-powered sensors can capture uninterrupted data that allow for activities such as the management of the traffic network, the optimization of transportation facilities, and the management of urban and intercity journeys to be performed. In addition, metaverse technology creates a new field of study. Evaluating the systems involved in current transportation activities together with the metaverse can increase the efficiency and sustainability of transportation. The main purpose of this study is to prioritize four alternatives of CAVs in metaverse with self-powered sensors using a novel decision making model. The proposed hybrid decision making framework includes two stages. In the first stage the fuzzy full consistency method (fuzzy FUCOM) is applied to find the weighting coefficients of criteria. In the second stage, a fuzzy non-linear model based on fuzzy Aczel-Alsina functions (fuzzy Aczel-Alsina weighted assessment - ALWAS method) is defined to rank the alternatives. Four alternatives are defined and evaluated using twelve different criteria under four headings, namely, technical advancement, environmental, implementation, and financial aspects. A case study has been created for the experts to evaluate the alternatives most effectively. The results of the study indicate that using self-powered sensors for integrating real-time traffic management in the metaverse is the most advantageous alternative.

Consensus reaching is essential in group decision-making (GDM) since it can mitigate conflicts between expert opinions and promotes the further implementation of decision-making results. Meanwhile, interaction between experts commonly occurs within social networks and in practical GDM problems. Therefore, it neecessary to consider the trust relationship between experts and utilize it to facilitate the consensus-reaching process (CRP). However, most existing social network-based GDM studies mainly use local measures (e.g., degree centrality) to determine the importance of experts, which cannot reflect their actual influence on a global topological structure. To address this issue, we propose a novel consensus-reaching strategy from the perspective of complex network analysis. First, the Extended Comparative Linguistic Expressions with Symbolic Translation (ELICIT) is adopted to flexibly facilitate the expression of experts’ uncertain evaluations. The hybrid centrality is then defined to determine the influence of experts in the social network by considering both node importance and edge weight. Since experts with greater influence have stronger information propagation capabilities, hybrid centrality is utilized to guide the CRP, which can better reflect information flows in the social network. Additionally, the BWM-CRITIC weighting method is developed to reflect the significance and relationship among criteria. Finally, we verify the effectiveness and superiority of the proposed method by means of a case study on a sustainable supplier selection problem.

One of the most widely adopted approaches to define weights for Ordered Weighting Averaging (OWA) operators consists of using biparametric linear increasing fuzzy linguistic quantifiers. However, several shortcomings appear when using these quantifiers because depending on the values of these parameters, the aggregations could be biased or the extreme values might be completely ignored. In this contribution, the use of Extreme Values Reductions (EVRs) as fuzzy linguistic quantifiers is proposed to define weights for OWA operators in order to provide more realistic aggregations. First, the impact of the parameters of these linear fuzzy linguistic quantifiers in the OWA aggregations is studied. After that, EVR-OWA operators are introduced as those OWA operators whose weights are computed by using an EVR as fuzzy linguistic quantifier. It will be shown that when using EVR-OWA operators to fuse information, the aggregations are non-biased, take into account more information and the intermediate values are prioritized before the extreme ones. After proposing several families of EVRs, the generalising potential of the EVR-OWA operators is shown by proving that every family of symmetric weights for OWA operators that prioritize the intermediate information are the weights obtained from a certain EVR. Finally, an illustrative example is provided.

A fuzzy extension of a Multi-Criteria Decision Analysis (MCDA) method implies a choice of an approach to estimating corresponding functions of fuzzy variables and a method for ordering alternatives based on ranking of fuzzy quantities. The objective of this paper is the development and comparison of Fuzzy MCDA (FMCDA) models, which represent different approaches to fuzzy extensions of an ordinary MCDA method. To do so, different approaches to assessing functions of fuzzy numbers are considered along with several methods for ranking of fuzzy numbers. Distinctions in ranking alternatives, including the number and significance of distinctions based on a granulation of the output information, are explored for different FMCDA models by using Monte Carlo simulating input scenarios of fuzzy multi-criteria problems. In addition, both intra-distinctions and inter-distinctions are explored. According to the results, distinctions in ranking alternatives by different FMCDA models may be considered as significant both for ranking and choice multi-criteria problematiques. This research is of fundamental and applied importance and has no analogues.

This study develops a power-average-operator-based hybrid multiattribute online product recommendation model that considers the consumer's risk attitude to rank categoric product options as a complement to existing recommender systems. Online production recommendation plays a key role in the development of e-commerce, and can greatly improve consumers' shopping experiences. However, few online shopping sites provide interactive decision aids for consumers such that they can articulate their preferences towards multiple selection attributes with the purpose of mitigating choice difficulty and improving decision quality. Additionally, consumers' risk attitudes to online shopping dramatically impact their product choices. In the model proposed in this paper, the risk attitude-based power average (RAPA) operator is used to integrate the risk attitude of the decision-maker into the information fusion process of multiple attribute decision-making. Subsequently, the risk attitude function, with several basic types, is introduced to quantify the risk attitude of the decision-maker for use in the RAPA operator. A proportional hesitant fuzzy 2-tuple linguistic term set (PHF2TLTS) is constructed by incorporating a binary of linguistic information aiming to comprehensively analyze the hybrid product information. With a focus on the information fusion process, the proportional hesitant 2-tuple linguistic RAPA operator and weighted proportional hesitant 2-tuple linguistic RAPA operator are introduced to aggregate a given set of PHF2TLTSs. The validity of the proposed model is demonstrated using an illustrative example, a comparison with existing approaches and detailed explanations of the performance differences.

Consensus Reaching Processes (CRPs) deal with those group decision-making situations in which conflicts among experts' opinions make difficult the reaching of an agreed solution.This situation, worsens in largescale group decision situations, in which opinions tend to be more polarized, because in problems with extreme opinions it is harder to reach an agreement.Several studies have shown that experts' preferences may not always follow a linear scale, as it has commonly been assumed in previous CRP.Therefore, the main aim of this paper is to study the effect of modeling this nonlinear behavior of experts' preferences (expressed by fuzzy preference relations) in CRPs.To do that, the experts' preferences will be remapped by using nonlinear deformations which amplify or reduce the distance between the extreme values.We introduce such automorphisms to remap the preferences as Extreme Values Amplifications (EVAs) and Extreme Values Reductions (EVRs), study their main properties and propose several families of these EVA and EVR functions.An analysis about the behavior of EVAs and EVRs when are implemented in a generic consensus

The qualitative flexible multiple criteria method (QUALIFLEX) is a convenient outranking technique to handle multicriteria decision-making (MCDM) problems due to its less complexity and high applicability, while the multiple criteria hierarchy process (MCHP) allows decision makers to deal with the hierarchical structure of criteria set where decision makers can even estimate results for a particular subcriterion at some intermediate level of the hierarchy. The main focus of our study is the amalgamation of the MCHP and QUALIFLEX methodology with special emphasis on modeling interaction among the criteria using the concept of bipolar Choquet integral. To give the decision makers more freedom for expressing their cognition about membership and nonmembership grades, the q-rung orthopair fuzzy (q-ROF) environment is adopted to expresses the criteria measurement. To facilitate this, it is first proposed a revised closeness index for q-ROF to identify the appropriate ordering. Further, it is aimed to establish a new framework by implementing stochastic multiobjective acceptability analysis in our proposed extended QUALIFLEX method to take into account a variety of parameters compatible with the descriptive information regarding the relative importance and interaction of different criteria provided by the decision maker. Finally, a numerical example based on the supplier selection problem is presented to illustrate the proposed methodology in the decision problem.

Consensus reaching models are widely used to derive a representative solution in group decision-making problems. Current models present limitations regarding the achievement of the agreement and keeping enough consistency for achieving valid solutions. Therefore, this paper proposed a new consensus model based on the deviation degree of two fuzzy preference relations (FPRs), in which a novel consistency index (CI) is defined to measure whether an FPR is of acceptable consistency. Additionally, an interindividual similarity index (ISI) is devised to measure the consensus degree of two FPRs. In the proposed consensus reaching process, ISI is also used to guide the two most incompatible decision-makers (DMs) to modify their judgments. The proposed iterative consensus reaching algorithm is convergent, CI preservation. After that, a stationary vector method is adopted to determine DMs’ weights for the aggregation process based on DMs’ opinion transition probabilities. Finally, an illustrative example and comparative analysis is given to demonstrate the effectiveness of the proposed model.

Group recommender systems (GRSs) recommend items that are used by groups of people because certain activities, such as listening to music, watching a movie, dining in a restaurant, etc., are social events performed by groups of people sharing their tastes, and their choices affect all of them. GRSs help groups of people making choices in overloaded search spaces according to all group members preferences. A common GRS scheme aggregates users preferences to generate a group preference profile. However, the aggregation process may imply a loss of information, negatively affecting different properties of the GRS such as diversity of group recommendations, which is an important quality factor because of such recommendations are targeted to groups formed by users with individual and possibly conflicting preferences. To avoid and manage the loss of information caused by aggregation, this paper proposes to keep all group members preferences by using hesitant fuzzy sets (HFSs) and interpreting such information like the group hesitation about their preferences that will be used in the group recommendation process. To evaluate the performance and rank quality of the HFS GRS proposal, a case study is carried out.

Recommender systems aim at filtering large amounts of information for users, providing them with those pieces of information which better meet their preferences or needs. Such systems have been traditionally used in diverse areas, such as e-commerce or tourism. Within this context, group recommender systems address the problem of generating recommendations for groups of users who might have different interests. Although different aggregation processes have been extensively utilized in real-life applications to generate group recommendations, such processes do not guarantee that the list of products recommended to the group reflect a high agreement level among its members' individual preferences. Given the need for considering the added value of obtaining group recommendations under a high agreement level, this paper presents a novel group recommender system methodology that attempts to reach a high level of consensus among individual recommendations of group members. To do this, and inspired by existing group decision-making approaches in the literature, a consensus reaching process is carried out to bring such individual recommendations closer to each other before delivering the group recommendations.

With the extraordinary spread of computers and sensors, enormous amounts of data are generated every day in a range of areas-search engines, social media, healthcare organizations, insurance companies, financial industry, retail, and many others.Data science refers to the theories, methods, and applications for extracting previously unavailable and potentially highly useful information from data.This field has evolved as a hybrid of research in data mining, machine learning, computational intelligence, databases, algorithms, statistics, operations research, visualization, privacy, and security.It is helping us make sense out of vast quantities of information.However, how to use these data by an effective and ethical way is a significant challenge to science and to society as a whole.Intelligent techniques, including artificial intelligence, neural networks, fuzzy logic, granular computing, rough sets, expert systems, case-based reasoning, evolutionary algorithms, and swarm computing, have been successfully applied in many fields including data science.This special issue is devoted to the use of intelligent techniques for data science that reflects their current development obtained from selected papers submitted to the 8th International Conference on Intelligent Systems and Knowledge Engineering (ISKE2013) held in Shenzhen, People's Republic of China, during November 20-23, 2013.This issue encompasses seven papers that present the application of different intelligent techniques to different data science problems ranging from recommender systems to recognition processes passing by others such as activity simulation, fuzzy trading systems, deep learning, and incremental learning.The paper coauthored by Wei Wang, Guangquan Zhang, and Jie Lu investigates the collaborative filtering with an entropy-driven user similarity in recommender systems.It aims at improving recommendation performance, by means of a novel

The necessity of dealing with uncertainty in real world problems has been a long-term research challenge that has originated different methodologies and theories. Fuzzy sets along with their extensions, such as type-2 fuzzy sets, interval-valued fuzzy sets, and Atanassov's intuitionistic fuzzy sets, have provided a wide range of tools that are able to deal with uncertainty in different types of problems. Recently, a new extension of fuzzy sets so-called hesitant fuzzy sets has been introduced to deal with hesitant situations, which were not well managed by the previous tools. Hesitant fuzzy sets have attracted very quickly the attention of many researchers that have proposed diverse extensions, several types of operators to compute with such types of information, and eventually some applications have been developed. Because of such a growth, this paper presents an overview on hesitant fuzzy sets with the aim of providing a clear perspective on the different concepts, tools and trends related to this extension of fuzzy sets.

Recommendation systems are a clear example of an e-service that helps the users to find the most suitable products they are looking for, according to their preferences, among a vast quantity of information. These preferences are usually related to human perceptions because the customers express their needs, taste, and so forth to find a suitable product. The perceptions are better modeled by means of linguistic information due to the uncertainty involved in this type of information. In this article, we propose a content-based recommendation model that will offer a more flexible context to improve the final recommendations where the preferences provided by the sources will be modeled by means of linguistic variables assessed in different linguistic term sets. The proposal consists of offering a multigranular linguistic context for expressing the preferences instead of forcing users to use a unique scale. Then the content-based recommendation model will look for the most suitable product(s), comparing them with the customer(s) information according to its resemblance. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 419–434, 2007.

Before implementing a design of a large engineering system different design proposals are evaluated. The information used by experts to evaluate different options may be vague and/or incomplete. Although different probabilistic tools and techniques have been used to deal with these kinds of problems, it seems better to use the fuzzy linguistic approach to model vagueness and the Dempster-Shafter theory of evidence for modeling incompleteness and ignorance. In the evaluation of alternative designs, different criteria can be considered. In this article an evaluation process is developed in terms of Safety and Cost analysis. Both criteria involve uncertainty, vagueness, and ignorance due to their nature. Therefore, we propose an evaluation process defined in a linguistic framework where both criteria will be conducted in different utility spaces, i.e., in a multigranular linguistic domain. Once the evaluation framework has been defined, we present an evaluation process based on a Multi-Expert Multi-Criteria decision model that will be able to deal with multigranular linguistic information without loss of information in order to evaluate different design options for an engineering system in a precise manner. Accordingly, we propose the use of a multigranular linguistic model based on the Linguistic Hierarchies presented by Herrera and Martínez (“A model based on linguistic 2-tuples for dealing with multigranularity hierarchical linguistic contexts in multi-expert decision-making.” IEEE Trans Syst Man Cybern B 2001;31(2):227–234). © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 1161–1194, 2005.

This special issue encompasses eight papers devoted to recent developments in the field of preference modeling.The issue originates from presentations at the EUROFUSE Workshop on Preference Modeling and Applications (EUROFUSE 2001) that was held in Granada, Spain, April 25-27, 2001.These eight original contributions have been revised thoroughly and expanded to become the articles currently presented in this issue.Preference modeling is a fundamental step in solving problems in various fields such as economics, medical diagnosis, information retrieval, decision theory, etc.Most of these problems take place in a complex environment where uncertain and imprecise knowledge and possibly vague preferences have to be considered.To face such complexity, preference modeling requires the use of specific techniques and concepts that allow the available information to be represented appropriately.As is well known, the fuzzy approach has led to considerable advances in preference modeling, mainly because the use of fuzzy techniques increases the reliability and flexibility of decision models.The present issue includes different proposals of fuzzy preference modeling in several application fields.The first group of articles is focused on the study of different aspects of preference modeling in Multicriteria Decision Making