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This paper overviews an assortment of recent research work undertaken on recommender system models based on using multiple views of user and item-related data across the recommendation process. A summary of representative literature on multi-view recommender approaches is provided, describing their main characteristics, such as: their potential to overcome most common shortcomings in conventional recommender systems, as well as the use of data science, learning techniques and aggregation processes to combine information stemming from multiple views. A tabular summary is provided to facilitate the comparison of the similarities and differences among the surveyed works, along with commonly identified directions for future research in the topic. [ABSTRACT FROM AUTHOR]

Abstract: The process of assessing individual authors should rely upon a proper aggregation of reliable and valid papers’ quality metrics. Citations are merely one possible way to measure appreciation of publications. In this study we propose some new, SJR- and SNIP-based indicators, which not only take into account the broadly conceived popularity of a paper (manifested by the number of citations), but also other factors like its potential, or the quality of papers that cite a given publication. We explore the relation and correlation between different metrics and study how they affect the values of a real-valued generalized h-index calculated for 11 prominent scientometricians. We note that the h-index is a very unstable impact function, highly sensitive for applying input elements’ scaling. Our analysis is not only of theoretical significance: data scaling is often performed to normalize citations across disciplines. Uncontrolled application of this operation may lead to unfair and biased (toward some groups) decisions. This puts the validity of authors assessment and ranking using the h-index into question. Obviously, a good impact function to be used in practice should not be as much sensitive to changing input data as the analyzed one. [Copyright &y& Elsevier]

Quality is an essential determinant of the success of every type of software and social Web applications are not an exception. It is therefore of great importance that the examination of the degree to which social Web applications meet predefined requirements related to particular facets of quality is performed effectively and frequently. With an objective to facilitate evaluation procedure and enable comparison of social Web applications at all levels of the quality model, we initiated a research into development of a methodology that will aggregate quality requirements into a single score. The work presented in this paper draws on the employment of the logic scoring of preference (LSP) method and outlines only some parts of the aforementioned methodology. After identifying quality attributes that constitute the requirement tree, elementary criteria for both objective and subjective performance variables were introduced. As a follow up, field experts were included in the study in order to determine weights of performance variables within particular performance subsystem. Finally, the appropriate logic aggregation operators were selected based on the relevance of performance variables. [ABSTRACT FROM AUTHOR]

Aggregating various decision information provided by a group of decision makers (DMs) into an integrated one is essential for seeking the optimal solution. This paper aims to propose an effective method for information aggregation in group decision-making (GDM) in an uncertain environment. The approach introduces a family of incentive-induced cluster-based uncertain ordered weighted averaging (II-CUOWA) operators from the perspective of incentive management. Specifically, the II-CUOWA operator is first introduced, involving the definition, the clustering method of judgment information, the calculation method of position weights, and several mathematical properties. Then, the study delves into the exploration of generalized formulas for the II-CUOWA operator, as well as discussing special cases achievable by adjusting internal parameters. Finally, this paper outlines the aggregation process of II-CUOWA operators when addressing GDM problems, accompanied by a practical example illustrating its application and validity in employees' performance assessment. The results show that II-CUOWA operators not only highlight the distributed structure of decision information but also possess the capability to reward or penalize alternatives, thereby guiding their development by considering the manager's incentive preference. The proposed method enriches the methodology of GDM theory from a novel research perspective and provides a solution to practical GDM problems. [ABSTRACT FROM AUTHOR]

Intuitionistic fuzzy sets have been widely studied and applied as an important means of dealing with information uncertainty. However, the existing intuitionistic fuzzy sets and their extension methods are limited and single in their fuzzy spatial representation of information. Under this environment, this paper proposes a new generalized fuzzy set, called qth Rung Root Orthopair Fuzzy Sets (q-RROFS). Since the q-RROFS can adjust the range of fuzzy space expression by the parameter q, it is superior to intuitionistic fuzzy sets, SR-fuzzy sets, and CR-fuzzy sets. We give some definitions and properties of q-RROFS and give their proofs. Under the q-RROFS, we give its operations and properties and introduce four new weighted aggregation operators, namely, qth Rung Root Orthopair Fuzzy-weighted average operator (q-RROFWA), qth Rung Root Orthopair Fuzzy-weighted geometric operator (q-RROFWG), qth Rung Root Orthopair Fuzzy-weighted power average operator (q-RROFWPA), and qth Rung Root Orthopair Fuzzy-weighted power geometric operator (q-RROFWPG). We discuss the properties of these operators in detail and follow the proof procedure. Then, we give a Multi-criteria decision-making approach under q-RROFS. Finally, we illustrate the effectiveness and applicability of the proposed methodology through practical application examples and comparisons with other methods. [ABSTRACT FROM AUTHOR]

The research on decision making driven by product rankings faces challenges due to the rise of extensive positive reviews and the widespread distribution of electronic word of mouth (eWOM) across multiple platforms. There is a limited body of research that examines the impact of platform credibility on the quality of product rankings. Hence, based on the basic uncertain linguistic information (BULI), which enables simultaneous representation of information and its credibility, we investigate the development of a ratings aggregation approach for cross-platform distribution (CPD) with the aim of facilitating decision-making processes, focusing specifically on the aspect of credibility. To begin with, this paper introduces the concept of BULI as a means to effectively represent both product ratings and their corresponding levels of credibility. Subsequently, we proceeded to devise the BULI-based aggregation functions that are well suited for the aggregation of CPD ratings and that can be degraded to the existing operator. In addition, we develop a credibility evaluation index system and credibility calculation model for the platform in order to derive a product BULI matrix consisting of ratings and their corresponding levels of credibility. In this study, we propose two models, namely the feature information-based user weighting model and the BULI distance measure-based technique for order preference by similarity to an ideal solution (BULI-TOPSIS) model, to enhance the product ratings aggregation approach for decision-making purposes. The utilization of the proposed method is exemplified through the case study of passenger car ranking, showcasing its practicality and efficiency. [ABSTRACT FROM AUTHOR]

Trust network analysis has been widely applied in various fields, such as group recommendation, group decision-making and other related areas. In this paper, we focus on obtaining the complete trust network in which experts express their trust relationships for another with a single linguistic term or distribution assessments of a linguistic term set. We first discuss the conditions of obtaining the complete trust network, and the propagation and aggregation of the trust relationships with a single linguistic term. Since the linguistic term set may be symmetric and uniform, symmetric and non-uniform, or asymmetric and non-uniform, we translate linguistic terms into numerical indexes and define the propagation operator based on the semantics of the linguistic term and the Archimedean t-norm. The propagation result is translated to 2−tuple linguistic model because it may not exist in the initial linguistic term set. Some properties are proposed to verify that the proposed operator is compatible with human thought. Then the 2−tuple distribution assessments on a linguistic term set are defined, and the other aggregation operator is proposed to propagate linguistic distribution assessment trust relationships. The second aggregation operator focuses on both the aggregation of linguistic terms and symbolic proportions of linguistic terms and is a generalization of the first operator. Finally, a numerical example of CouchSurfing comparative analyses further demonstrates that the proposed operators are effective and reasonable, and can consider the different semantics of a linguistic term in practical application. [ABSTRACT FROM AUTHOR]

To analyze the past, present and future of a particular research field, classic papers are usually studied because they identify the highly cited papers being a relevant reference point in that specific research area. As a result of the possible mapping between high quality research and high citation counts, highly cited papers are very interesting. The objective of this study is to use the H-classics method, which is based on the popular h-index, to identify and analyze the highly cited documents published about aggregation operators in the research area of group decision making. According to the H-classics method, this research area is represented by 87 citation classics, which have been published from 1988 to 2014. Authors, affiliations (universities/institutions and countries), journals, books and conferences, and the topics covered by these 87 highly cited papers are studied. [ABSTRACT FROM AUTHOR]

Cubical fuzzy numbers offer an improved framework for assessing uncertain factors in decision-making. The accumulation of cubical fuzzy opinions is pivotal in every group decision-making process. This research paper focuses on creating innovative cubical fuzzy Bonferroni mean geometric aggregation operators and their weighted adaptations. We build these operators upon the principles of cubical fuzzy Einstein operations. The proposed operators can capture intricate relationships among evaluated cubical fuzzy factors. We formally demonstrate and validate the desirable properties of these introduced operators. In addressing the complexities of multiple criteria group decision-making, we present a Cubical Fuzzy Complex Proportional Assessment (CF-COPRAS) method as a practical solution. Determining the weights of criteria holds significant importance in any decisionmaking methodology. In this paper, we employ the intercriteria correlation (CRITIC) method to establish objective criteria weights. To showcase the effectiveness of our developed method, we apply it to a real-world financial investment risk management scenario within a cubical fuzzy context. Subsequently, we conduct a sensitivity analysis and comparative evaluation to demonstrate the efficacy of our proposed approach. [ABSTRACT FROM AUTHOR]

The main aim of this paper is to present a new multi-attribute decision-making (MADM) approach for solving the problems under the uncertain and complex environment. The key challenges during any MADM problem are how to quantify the objective uncertainty information in the data and how to aggregate such collective information. To answer this, in this paper, we utilize the concept of the bipolar fuzzy information to mark the information in terms of the positive and negative support. To aggregate this different information, we propose some power aggregation operators based on the Aczel-Alsina operators (AAO). The AAOs are the generalized t-norm based operations with an additional parameter to analyze the influence of the expert preferences. Based on these AAO and bipolar fuzzy information, we stated bipolar fuzzy AA power weighted averaging and geometric operators and investigate their features. Later, based on these operators, we establish a MADM algorithm to solve the decision-making problems. The applicability of the stated algorithm is demonstrated through a case study related to quantum computing. The comparative studies and advantages of the study are also analyzed with the various prevailing theories. [ABSTRACT FROM AUTHOR]

Intuitionistic fuzzy (IF) sets are valuable tools for describing uncertain information in Multi-Criteria Group Decision Making (MCGDM), where the elements have degrees of membership and non-membership. IF aggregation operator is a popular data processing method that can be used for data dimensionality reduction, feature extraction, data compression, and so on. Some existing MCGDM techniques based on IF aggregation operators have been criticized for reasons that include disregarding the comprehensive correlations of the criteria and ignoring the monotonicity of the decision information. This paper aims to construct some IF aggregation operators based on Yager's triangular norms and Heronian mean to shed light on decision-making issues. At first, some novel IF operations such as Yager sum, Yager product, and Yager scalar multiplication on IFSs are presented. Based on these new operations, the generalized IF Yager Heronian average (GIFYHA) operator and the generalized IF Yager weighted Heronian average (GIFYWHA) operator are proposed and their corresponding properties are also proved in detail. Then, an improved MCGDM algorithm is constructed that relies on suggested operators. Its effectiveness and applicability are verified by applying it to select the best location for a company. In addition, the sensitivity of the parameters in the proposed operator to decision findings is also discussed. Finally, the comparative analysis of the proposed operator with the existing operators shows that the proposed operator is suitable for aggregating IF information with correlations both on "non-empty lattice" and total orders on IF values. [ABSTRACT FROM AUTHOR]

With the development of deep learning, deep neural methods have been introduced to boost the performance of Collaborative Filtering (CF) models. However, most of the models rely solely on the user-item heterogeneous graph and only implicitly capture homogenous information, which limits their performance improvement. Although some state-of-the-art methods try to utilize additional graphs to make up, they either merely aggregate the information of multiple graphs in the step of initial embedding or only merge different multi-graph information in the step of final embedding. Such one-time multi-graph integration leads to the loss of interactive and topological information in the intermediate process of propagation. This paper proposes a novel Multi-Graph iterative fusion Recommendation Framework (MGRF) for CF recommendation. The core components are dual information crossing interaction and multi-graph fusing propagation. The former enables repeated feature crossing between heterogeneous nodes throughout the whole embedding process. The latter repeatedly integrates homogeneous nodes as well as their topological relationships based on the constructed user-user and itemitem graphs. Thus, MGRF can improve the embedding quality by iteratively fusing user-item heterogeneous graph, user-user and item-item homogeneous graphs. Extensive experiments on three public benchmarks demonstrate the effectiveness of MGRF, which outperforms state-of-the-art baselines in terms of Recall and NDCG. [ABSTRACT FROM AUTHOR]

p , q -quasirung orthopair fuzzy set (p , q -QOFS) is an extension of the q -rung orthopair fuzzy set (q -ROFS) theory used to represent uncertainty and vagueness in decision-making processes. The paper aims to utilize robust sine-trigonometric operational laws to investigate the group decision-making process within the p , q -QOFSs framework. The p , q -QOFS possess a distinctive characteristic of handling uncertain information by utilizing a wider space for membership representation compared to q -ROFS. Consequently, the current paper has been categorized into three distinct phases. In the initial phase, novel operational laws will be presented for p , q -QOFSs. The fundamental concept behind these newly proposed operations is to integrate the properties of the sine function, which include being periodic and symmetric about the origin, into the decision-making process for objects. Then, based on these laws, several operators for aggregating information will be obtained, along with their necessary properties and relationships. Lastly, an algorithm will be presented for interpreting the problem of multi attribute group decision-making (MAGDM), utilizing the operators, and demonstrating it with an illustrative example. A comprehensive comparative analysis will be conducted with some of the existing methods to uncover their impacts. [ABSTRACT FROM AUTHOR]

Motivated by the concepts of low carbon and environmental protection, electric vehicles have received much attention and become more and more popular all around the world. The expanding demand for electric vehicles has driven the rapid development of the charging pile industry. One of the prominent issues in charging pile industry is to determine their sites, which is a complex decision-making problem. As a matter of factor, the process of charging piles sites selection can be regarded as multi-attribute group decision-making (MAGDM), which is the main topic of this paper. The recently proposed linguistic spherical fuzzy sets (LSFSs) composed of the linguistic membership degree, linguistic abstinence degree and linguistic non-membership degree are powerful tools to express the evaluation information of decision makers (DMs). Based on the concept of LSFSs, we introduce probabilistic multi-valued linguistic spherical fuzzy sets (PMVLSFSs), which can describe DMs' fuzzy evaluation information in a more refined and accurate way. The operation rules of PMVLSFSs are also developed in this article. To effectively aggregate PMVLSFSs, the probabilistic multi-valued linguistic spherical fuzzy power generalized Maclaurin symmetric mean operator and the probabilistic multi-valued linguistic spherical fuzzy power weighted generalized Maclaurin symmetric mean are put forward. Based on the above aggregation operators, a new method for MAGDM problem with PMVLSFSs is established. Further, a practical case of suitable site selection of charging pile is used to verify the practicability of this method. Lastly, comparative anal-ysis with other methods is performed to illustrate the advantages and stability of proposed method. [ABSTRACT FROM AUTHOR]

This paper proposes interval valued pentagonal fuzzy neutrosophic set in view with the combination of pentagonal fuzzy sets, single valued neutrosophic set and the interval valued neutrosophic set. Interval valued pentagonal fuzzy neutrosophic weighted geometric averaging operator is defined based on the operational laws, a theorem and some of its properties have been established by the proposed operator and also on the score and accuracy function. By assigning different weights to various features in accordance with each choice, the aggregation of the interval valued pentagonal fuzzy neutrosophic set is used on an example to validate the efficiency. Finally, a multi-attribute decision-making environment is examined using the proposed methodology's ranking order and collective ratings of each attribute's values for various alternatives. [ABSTRACT FROM AUTHOR]

Precision agriculture refers to the use of advanced technologies and data analytics to optimize farming practices, maximize crop yields, and reduce resource wastage. In precision agriculture monitoring, unmanned aerial vehicles (UAVs) have emerged as a valuable tool for large-scale implementation. The selection of suitable UAVs is a critical aspect of precision agriculture and is deliberated as significant multi-attribute group decision-making (MAGDM) problem. In decision issues, the evaluation of experts about any available options plays an important role. In this context, interval-valued q-rung orthopair fuzzy set (IVq-ROFS) is a useful tool allowing experts to provide their evaluations in a wider space and better deal with incomplete information. This paper aims to develop a new approach to determining expert weights using distance and similarity measures for interval-valued q-rung orthopair fuzzy numbers (IVq-ROFNs). To achieve this, an average group assessment based on individual assessments is constructed. The experts' weights were then established by computing similarity measures between the individual evaluations and the average group assessment. Moreover, the technique for order of preference by similarity to the ideal solution (TOPSIS) method is extended to address the MAGDM problem under IVq-ROF information. An example involving UAV selection for precision agriculture is provided to demonstrate the validity of the suggested method, and the impact of parameters on decision results is discussed. Finally, a comparative analysis with existing approaches demonstrates the superiority and advantages of the proposed technique. [ABSTRACT FROM AUTHOR]

The Heating, Ventilation, and Air Conditioning (HVAC) control system is in charge of the building's energy efficiency. Indoor energy consumption trends can be intelligently monitored and minimized. Occupancy data is essential for saving a significant amount of energy. This energy footprint can play an important part in modern smart buildings to improve indoor green environments while lowering costs. Traditional energy monitoring and control systems can be enhanced by installing an occupancy monitoring system, which consists of a network of sensors and cameras. In this paper, we offer a novel and innovative Convolutional Neural Network (CNN) based on real-time camera occupancy detection and recognition algorithms across various types of sensors, which provides realistic low-cost energy-saving solutions with robust Graphical Processing Units (GPUs). Decisionmaking tools can be used to select the appropriate occupancy detection and recognition alternative for indoor environment and energy monitoring and management. In this research work, we develop the\Fermatean Fuzzy Prioritized Weighted Average (FFPWA) operator and Fermatean Fuzzy Prioritized Weighted Geometric (FFPWG) operator". In the end, we give an algorithm for an Intelligent Decision Support System (IDSS) using proposed Aggregation Operators (AOs) to compare our CNN based method with other existing sensors techniques. [ABSTRACT FROM AUTHOR]

The past decade has witnessed the rapid growth in the hesitant fuzzy domain. To provide an in-depth discussion, based on the retrieved 1080 papers from the Web of Science (WoS), this paper first investigates what topics are the research interested in and how they have changed over the past decade in the hesitant fuzzy area. Main path analysis is adopted further to identify the most influential connections between papers and analyze the knowledge diffusion trajectories in the past decade, including the local and global main paths and the key-route main path. The results show that there are five hot topics which are decision-making, information measure, aggregation operator, extensions of HFS and preference relation. Among them, decision-making and extensions of HFS are two topics that are beginning more and more prevalent. The result of main path analysis consolidates the findings, showing that the application of hesitant fuzzy theory in the decision-making field has been and remains the most popular, and the object of the research has shifted to different types of HFSs being hesitant fuzzy linguistic term set (HFLTS) the most remarkable one. This paper not only provides a framework to investigate the research in a certain field, but also provides an in-depth summary of the field to provide theoretical guidance for researchers and practitioners. [ABSTRACT FROM AUTHOR]

Complex a-rung orthopair fuzzy soft (CQROFS) set is very famous and effective tool for depicting complex uncertain information because it is the combination of the complex q-rung orthopair fuzzy set (CQROFS) and soft set (SS), in which the truth grade and falsity grade are expressed by complex numbers. Furthermore, Einstein's t-norm and t-conorm are very useful and more dominant tools in aggregating the collection of information into a singleton one, where the algebraic t-norm and t-conorm are the specific case of the Einstein t-norm and t-conorm. In this paper, we aim to propose the complex q-rung orthopair fuzzy soft (CQROFS) information which is the modification or generation of the complex Pythagorean and complex intuitionistic fuzzy soft sets. Some specific operational laws are also derived for the proposed work based on algebraic t-norm and t-conorm. Additionally, we also develop the Einstein's operational laws in the presence of the CQROFS set. Consequently, based on the Einstein operational laws and CQROFS information, we derive the CQROFS Einstein weighted geometric (CQROFSEWG), CQROFS Einstein ordered weighted geometric (CQROFSEOWG), and CQROFS Einstein hybrid geometric (CQROFSEHG) operators. Some notable properties and valuable results are also explored. Moreover, we develop a multi-attribute decision-making (MADM) method based on the derived operators for CQROFS set. Finally, we do a comparative analysis between the proposed work and some existing operators to show the advantages and supremacy of the developed method. [ABSTRACT FROM AUTHOR]

The core contribution of this study is to develop a novel generalized idea of q-rung orthopair probabilistic hesitant fuzzy rough set (q-ROPHFRS) which is hybrid structure of the q-rung orthopair fuzzy set, probabilistic hesitant fuzzy set, and rough set. The q-ROPHFRS covers the positive and negative membership grades in the form of probabilistic hesitant fuzzy rough information to address the uncertainties in real-world decision-making problems. This paper proposes a list of novel q-rung orthopair probabilistic hesitant fuzzy rough averaging/geometric aggregation operators to handle the uncertainty effectively and reliably to aggregate the uncertain information under q-ROPHFRSs. Several interesting elementary properties have been investigated. Furthermore, a novel multi-attribute decision-making approach based on the proposed aggregation information is presented. Finally, a numerical application regarding selecting a medical oxygen supplier to the hospital is presented to illustrate the consistency of the developed decision-making technique. The comparison of the proposed technique with q-rung orthopair probabilistic hesitant fuzzy rough TOPSIS shows that the proposed multi-criteria decision-making methodology is reliable and effective in addressing the uncertainty in real-world decision-making problems. [ABSTRACT FROM AUTHOR]

The linguistic 𝑞-rung orthopair fuzzy (L𝑞-ROF) set is an important implement in the research area in modelling vague decision information by incorporating the advantages of 𝑞- rung orthopair fuzzy sets and linguistic variables. This paper aims to investigate the multicriteria decision group decision making (MCGDM) with L𝑞-ROF information. To do this, utilizing Hamacher 𝑡-norm and 𝑡-conorm, some L𝑞-ROF prioritized aggregation operators viz., L𝑞- ROF Hamacher prioritized weighted averaging, and L𝑞-ROF Hamacher prioritized weighted geometric operators are developed in this paper. The defined operators can effectively deal with different priority levels of attributes involved in the decision making processes. In addition, Hamacher parameters incorporated with the proposed operators make the information fusion process more flexible. Some prominent characteristics of the developed operators are also wellproven. Then based on the proposed aggregation operators, an MCGDM model with L𝑞-ROF context is framed. A numerical example is illustrated in accordance with the developed model to verify its rationality and applicability. The impacts of Hamacher and rung parameters on the achieved decision results are also analyzed in detail. Afterwards, a comparative study with other representative methods is presented in order to reflect the validity and superiority of the proposed approach. [ABSTRACT FROM AUTHOR]

In contemporary healthcare systems, the selection and judicious choice of the right medical device becomes paramount consideration for accurate diagnosis, patient care, mitigate risk and cost incurred on diagnosis of disease. Moreover, medical devices are requisite indispensable tools to help healthcare professionals for effective diagnosis, selection of the right treatment amongst different options, and continuous monitoring of the patient. Thus, to select the best manufacturing company is as equally important to achieve the desired goals. This paper presents a decision making algorithm based on fuzzy theory that helps the decision maker in selecting the most suitable worldwide medical device manufacturer. Fuzzy theory is extensively used in managing databases and analyzing data to summarize information and draw meaningful conclusions. This paper utilizes the aggregation operators within the context of the complex q-rung orthopair fuzzy environment by utilizing Yager t-norm and t-conorm operations. Some aggregation operators are applied, namely, complex q-rung orthopair fuzzy Yager weighted geometric operator, complex q-rung orthopair fuzzy Yager ordered weighted geometric operator, complex q-rung orthopair fuzzy Yager weighted averaging operator and complex q-rung orthopair fuzzy Yager ordered weighted averaging operator. Additionally, the notable properties of these operators are proved. These aggregation operators are generated specifically for multi-criteria decision-making. Decision making algorithm is used to choose the most relevant medical device manufacturer (alternative) to validate its efficacy. The results demonstrate the versatility of parameters, adaptability to multi-criteria decision making, and efficiency of the proposed aggregation operators and algorithm. Test with specific criteria is also conducted to verify the legality and validity of the decision making algorithm. At last, the acquired results underwent a comparative analysis against various established methodologies, confirming the method's capacity to produce precise and accurate outcomes. • Multi criteria decision making algorithm is devised in fuzzy environment. • Aggregation operators are introduced and notable properties are investigated. • Test with specific criteria is conducted for the validity of proposed operators. • The algorithm is utilized in medical industry to choose best device manufacturer. [ABSTRACT FROM AUTHOR]

Selecting the right educational vendors in the Metaverse, particularly those utilizing extended reality (XR), is crucial for creating an engaging and immersive learning environment. Careful vendor selection ensures the delivery of high-quality XR educational content, considering factors such as seamless integration with different XR devices and addressing associated challenges, which can affect the comfort and well-being of learners.This paper proposes a decision-making model to address the complex task of selecting educational vendors within the Metaverse with a focus on extended reality (XR). Recognizing the challenges inherent in such selections, this work presents a consensus-based measurement of alternatives and ranking according to compromise solution (MARCOS) model, incorporating single-valued neutrosophic (SVN) information and SVN Dombi-Archimedean weighted aggregation operators. Attribute weights crucial for decision-making are determined through an optimization tool employing cross-entropy measures with partial weight information. The evaluation encompasses nine criteria, including cost, security, privacy, pedagogical alignment, configuration, maintenance, level of control, output tools, degree of immersion, availability of XR learning contents, and time. Four leading vendors including Victory XR, ARuVR, zSpace, and Varjo Technologies are assessed, with zSpace emerging as the most favorable alternative with the highest overall utility value of 0.6889. The paper further conducts sensitivity analysis and performance comparisons to establish the robustness of the ranking results, providing a comprehensive methodology for educational stakeholders addressing the evolving dynamics of the Metaverse. • A decision-support model is suggested for ranking XR educational vendors under metaverse. • Some robust Dombi-Archimedean weighted aggregation operators are presented under SVN context. • A consensus based SVN-MARCOS methodology is proposed with partial weights information of attributes. [ABSTRACT FROM AUTHOR]

The aggregation of fuzzy information is a key process of MCDM methods used to deal with uncertainties. A class of aggregation methods for fuzzy information is fuzzy distance aggregation operators in which distance values are argument variables. This class of aggregation operators permits us to consider an ideal alternative in the aggregation process. But these operators fall in dealing with situations among which distance values cannot be aggregated directly. Therefore, we proposed some distance induced fuzzy operators in which distance values are order-inducing variables so that we can also consider an ideal alternative but take other parameters, also including distance values, as argument variables in an aggregation process. We analyzed the concepts, properties, and generalized forms of proposed distance induced fuzzy operators in this paper. Moreover, aiming at multi-criteria group decision-making (MCGDM), three frameworks of MCGDM methods with the proposed distance induced fuzzy operators are constructed for three different MCGDM situations, respectively. At the end of this paper, an example is used to explain the detailed processes of using the proposed frameworks to make a decision. [ABSTRACT FROM AUTHOR]

It is still very difficult to exploit possibilistic concepts to identify the strict parameters of vehicular ad-hoc networks (VANET) while minimizing the dispersion of its constraints. In this paper, as a first contribution, an empirical study is presented within the framework of the development of a predictive model based on a linear regression model to interpret the phenomenon of VANET network congestion in a precise way by minimizing the dispersion of predicted outputs. The objective of the model developed is twofold: (1) to model the interactions between the traffic variables involved in the model and to exploit possibilistic concepts to identify the constraints of the VANET network based on a linear optimization under constraints, (2) to anticipate with precision the probability of occurrence of an unforeseen incident on VANET traffic. In addition, the traffic data involved in our model are observed vehicle speed, predicted travel time, observed travel time, and delay time. In addition, the accuracy of the prediction is proven by checking the relevance of the model according to the goodness of fit and the statistical significance of each explained variable. Our second contribution focuses on the design of a new technique for collecting, aggregating, and predicting real-time traffic flow. The main objective is to optimize the prediction error rate under strict conditions whose traffic parameters have unstable values. For this, we propose a traffic flow prediction approach based on fuzzy logic and regression analysis. The approach incorporates approved traffic parameters with the appearance of fuzzy least squares. To increase the prediction accuracy of the traffic flow and then arrive at a quadratic resolution under constraints, we integrate the Fuzzy Support Vector Regression for VANET networks (FSVRNET) model and Unified Particle Swarm Optimization (UPSO) algorithm in our approach. The chosen approach aims to model the interactions between traffic data observed from multiple data sources (e.g., connected loop detectors) to adjust the stability of traffic parameters in the prediction process. Our experimental study shows a strong correlation between the predicted data and the actual state of the VANET traffic flow. In addition, the prediction error of regression analysis is significantly reduced. Prediction performance using UPSO-FSVRNET is better with the proposed test set, as assumed by most identification methods. [ABSTRACT FROM AUTHOR]

In the context of the increasingly competitive post-epidemic social marketing era, the sustainable development of supply chain finance is also facing the impact of more complex risk factors. However, the issue concerning how to scientifically and reasonably select the appropriate applicant company for financing cooperation on the basis of risk evaluation remains unresolved. For the smooth operation of banks' sustainable supply chain finance (SSCF) business, this paper aims to develop a novel multi-attribute group decision-making (MAGDM) method considering the robustness and comprehensiveness among risk attributes and decision-makers (DMs). First, based on the idea that quantitative assessment values and qualitative linguistic sets can more accurately express uncertain risk information and importance degree of indicators, a novel concept is proposed that combines the probabilistic linguistic term set (PLTS) with the plithogenic set to evaluate the risk attributes of each alternative. Additionally, risk decision matrices evaluated by DMs are integrated and transformed through the operation of PLTS scoring function aggregation operators and the plithogenic contradiction degree. For deriving the integrated weight information of attributes, the objective attribute weights are determined on the basis of the subjective weights by adjusting coefficients. Then, the ranking order of alternatives is generated by applying the VIKOR method and constructing a MAGDM model with plithogenic PLTSs. Finally, the solving of a practical example concerning the selection of financing objects for SSCF oriented by risk evaluation verifies the effectiveness of the proposed model, where five risk indicators are developed, and the superiority of our studies is demonstrated by comparing with the existing TOPSIS and ELECTRE methods. [ABSTRACT FROM AUTHOR]

Fuzzy sets have undergone several expansions and generalisations in the literature, including Atanasov's intuitionistic fuzzy sets, type 2 fuzzy sets, and fuzzy multisets, to name a few. They can be regarded as fuzzy multisets from a formal standpoint; nevertheless, their interpretation differs from the two other approaches to fuzzy multisets that are currently available. Hesitating fuzzy sets (HFS) are very useful if consultants have hesitation in dealing with group decision-making problems between several possible memberships. However, these possible memberships can be not only crisp values in [0,1], but also interval values during a practical evaluation process. Hesitant bipolar valued fuzzy set (HBVFS) is a generalization of HFS. This paper aims to introduce a general framework of multi-attribute group decision-making using social network. We propose two types of decision-making processes: Type-1 decision-making process and Type-2 decision-making process. In the Type-1 decision-making process, the experts' original opinion is proces for the final ranking of alternatives. In Type-2 decision making processs, there are two major aspects we consider. First, consistency tests and checking of consensus models are given for detecting that the judgments are logically rational. Otherwise, the framework demands (partial) decision-makers to review their assessments. Second, the coherence and consensus of several HBVFSs are established for final ranking of alternatives. The proposed framework is clarified by an example of software packages selection of a university. [ABSTRACT FROM AUTHOR]

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In modern society, people concern more about the evaluation of medical service quality. Evaluation of medical service quality is helpful for medical service providers to supervise and improve their service quality. Also, it will help the public to understand the situation of different medical providers. As a multi-criteria decision-making (MCDM) problem, evaluation of medical service quality can be effectively solved by aggregation operators in interval-valued q-rung dual hesitant fuzzy (IVq-RDHF) environment. Thus, this paper proposes interval-valued q-rung dual hesitant Maclaurin symmetric mean (IVq-RDHFMSM) operator and interval-valued q-rung dual hesitant weighted Maclaurin symmetric mean (IVq-RDHFWMSM) operator. Based on the proposed IVq-RDHFWMSM operator, this paper builds a novel approach to solve the evaluation problem of medical service quality including a criteria framework for the evaluation of medical service quality and a novel MCDM method. What's more, aiming at eliminating the discordance between decision information and weight vector of criteria determined by decisionmakers (DMs), this paper proposes the concept of cross-entropy and knowledge measure in IVq-RDHF environment to extract weight vector from DMs' decision information. Finally, this paper presents a numerical example of the evaluation of medical service for hospitals to illustrate the availability of the novel method and compares our method with other MCDM methods to demonstrate the superiority of our method. According to the comparison result, our method has more advantages than other methods. [ABSTRACT FROM AUTHOR]

In multiple attribute group decision-making (MAGDM), a consensus exists that the psychological factors of decision makers (DMs) generally influence their choice of optimal alternatives. To quantify well the influence of DMs' risk attitudes on the group decision-making process, this paper develops a new approach to MAGDM by introducing DMs' utility functions into the aggregation process. First, the ordered weighted utility distance (OWUD) operator is constructed by introducing DMs' utility functions into the distance operator. As a novel operator, the OWUD operator not only satisfies both the basic features of distance measures and the desirable properties of aggregation operators, but also has some unique characteristics. Then, the linear risk tolerance (LRT) utility function is further used in the OWUD operator as the basic utility function, and a specific form of the OWUD operator, which is named as the ordered weighted-linear risk tolerance utility distance (OW-LRTUD) operator, is proposed. The OW-LRTUD operator can quantify well the influence of DMs' risk attitudes on the group decision-making process. It also has an analytically tractable form and includes many well-known distance measures and aggregation operators, and thus can be applied to many different kinds of decision-making problems. Subsequently, to determine the weights of the OW-LRTUD operator, an extension of the maximum Bayesian entropy method is proposed by incorporating the prior known weight information and different risk attitudes of all individual DMs. An approach to MAGDM based on the OW-LRTUD operator and the proposed weight determination method is developed. An application for determining the optimal alternative in the investment selection is provided to show the feasibility, effectiveness and robustness of our approach in practice, and the corresponding sensitive analysis illustrates the influence of DMs' risk attitudes on the group decision-making process. • This paper develops a class of distance operators, named as the ordered weighted utility distance operators, which can quantify the influence of DM's risk attitudes or the satisfaction degrees on the group decision-making process. • New weighting models for both attributes and DMs are provided respectively by combining the prior known weight information and subjective considerations of all individual DMs. • It is shown that the Arrow-Pratt measure of absolute/relative risk aversions of DMs significantly impact the optimal option of alternatives by numerical experiments and sensitivity analysis. [ABSTRACT FROM AUTHOR]

T-Spherical fuzzy sets provide a robust framework for handling uncertainty in real-world decision-making. This paper addresses the construction of T-Spherical fuzzy Heronian mean averaging aggregation operators using novel Hamacher operations. The study mathematically verifies the properties of these operators and develops new distance measures such as Hamming, Euclidean and Jaccard for quantifying dissimilarity between T-Spherical fuzzy numbers. Furthermore, the TOPSIS method is implemented using the proposed operators and distance measures to solve a practical decision-making problem in selecting the best transportation firm for medical waste disposal. A sensitivity analysis is made to assesses the efficacy of the proposed operators, while a comparison study with existing methods is exhibited to establish their efficiency. • Novel Hamacher operational laws for T-spherical fuzzy (T-SF) numbers. • New distance measures: Hamming, Euclidean, Jaccard and T-SF Heronian mean averaging operators. • TOPSIS method using proposed operators and distance measures. • Effective handling of complexities and uncertainties. • Example: selecting best transportation firm. [ABSTRACT FROM AUTHOR]

The aim of this paper is to introduce the concept of polytopic fuzzy sets as an extension of spherical fuzzy sets, picture fuzzy sets, and q -rung orthopair fuzzy sets. We define some basic operations and aggregation operators on polytopic fuzzy sets. Based on these operators, we present three new methods to deal with the multiple-attribute decision-making problem which cannot be solved with the help of existing aggregation operators. Finally, a numerical example is given to illustrate the validity of the proposed approach. [ABSTRACT FROM AUTHOR]

The ability of q-rung dual hesitant fuzzy sets (q-RDHFSs) in dealing with decision makers' fuzzy evaluation information has received much attention. This main aim of this paper is to propose new aggregation operators of q-rung dual hesitant fuzzy elements and employ them in multi-attribute decision making (MADM). In order to do this, we first propose the power dual Maclaurin symmetric mean (PDMSM) operator by integrating the power geometric (PG) operator and the dual Maclaurin symmetric mean (DMSM). The PG operator can reduce or eliminate the negative influence of decision makers' extreme evaluation values, making the final decision results more reasonable. The DMSM captures the interrelationship among multiple attributes. The PDMSM takes the advantages of both PG and DMSM and hence it is suitable and powerful to fuse decision information. Further, we extend the PDMSM operator to q-RDHFSs and propose q-rung dual hesitant fuzzy PDMSM operator and its weighted form. Properties of these operators are investigated. Afterwards, a new MADM method under q-RDHFSs is proposed on the basis on the new operators. Finally, the effectiveness of the new method is testified through numerical examples. [ABSTRACT FROM AUTHOR]

The new operations based on the Aczel–Alsina t-norm and t-conorm show the advantage of flexible operations by adjusting different parameter values. Motivated based on the new operations, we propose the Aczel–Alsina operations and weighted aggregation operators of neutrosophic Z-numbers (NZNs) to solve the flexible decision-making problem by adjusting different parameter values depending on the decision maker's preference under the environment of NZNs. To do so, this paper first proposes the Aczel–Alsina t-norm and t-conorm operations of NZNs and develops the NZN Aczel–Alsina weighted arithmetic averaging (NZNAAWAA) and NZN Aczel–Alsina weighted geometric averaging (NZNAAWGA) operators to aggregate NZNs. Then, a multiple attribute decision-making (MADM) method is developed by the NZNAAWAA and NZNAAWGA operators under the NZN environment. Finally, a numerical example is provided to verify the influence of different parameter values on decision-making results. Compared with existing MADM methods, the new MADM method shows its feasibility and flexibility in MADM applications. Moreover, the new MADM approach can not only extend the existing MADM methods, but also overcome the lack of decision-making flexibility of the existing MADM methods. [ABSTRACT FROM AUTHOR]

This paper is devoted to the research of full implication triple I method under Pythagorean fuzzy environment. We first propose the concepts of Pythagorean t-norm, Pythagorean t-conorm, residual Pythagorean fuzzy implication operator (RPFIO) and Pythagorean fuzzy biresiduum. The full implication triple I method for Pythagorean fuzzy modus ponens (PFMP) and Pythagorean fuzzy modus tollens (PFMT) are also established. In addition, the properties of full implication triple I method of PFMP and PFMT models including the robustness, continuity and reversibility are analyzed. Finally, a practical problem is discussed to demonstrate the effectiveness of the Pythagorean fuzzy full implication multiple I method in medical diagnosis. The advantages of the new method are also explained. Overall, compared with the existing methods, the proposed methods are based on logical reasoning rather than using aggregation operators, so they can more accurately and completely express decision information. [ABSTRACT FROM AUTHOR]

Some aggregation operators with distance measures have been proposed, but distance measure values play the role of argument variables. In this paper, the induced ordered weighted averaging (IOWA) operator with the Hamming distance is proposed, termed as the distance induced ordered weighted averaging (DIOWA) operator. The most distinctive characteristic of the DIOWA operator is that the distance measure values play the role of order-inducing variables. Some properties and special cases of the DIOWA operator are analyzed. This new operator is further extended to the uncertain situations represented by interval numbers. A new multi-criteria group decision-making (MCGDM) method with the proposed operators in this paper is also studied. Finally, a numerical example about how to select the best candidate is given to show how to use the DIOWA operator with interval numbers in group decision making. [ABSTRACT FROM AUTHOR]

Heronian mean (HM) operators are powerful aggregation operators that consider the interrelationship of two input arguments. HM operators have been utilized to solve many multi-criteria decision-making (MCDM) problems under different environments. However, the existing HM does not take into consideration the overall interaction of input arguments. Therefore, to address this issue, the Shapley fuzzy measure (SFM) is combined with an improved generalized weighted HM (IGWHM) operator under an interval neutrosophic vague set (INVS). The INVS is structured into several membership degrees appropriate for dealing with incomplete and inconsistent information as well as reducing the information loss during the decision elicitations. This paper introduces two aggregation operators: interval neutrosophic vague Shapley improved generalized weighted HM (INVSIGWHM) and interval neutrosophic vague Shapley improved generalized geometric-weighted HM (INVSIGGWHM). Then, the three essential algebraic properties of the proposed aggregation operators are also discussed for both operators. Furthermore, based on the proposed INVSIGWHM and INVSIGGWHM operators, we suggest two multi-criteria decision-making methods in which information is given in interval neutrosophic vague. A numerical example of investment decision is presented to illustrate the effectiveness of developed methods. It is found that the car company is the best alternative among the others. Besides, sensitivity analysis using two existing parameters is performed on the proposed method to demonstrate the effect of ranking order on alternatives. From this analysis, it can be seen that the parameter p , q significantly influences the aggregation results. Finally, a comparative analysis is presented in two approaches: to validate the proposed method and to demonstrate the advantages. The comparative analysis shows that the proposed method is reliable and practical in solving decision-making problems involving the total interaction of decision-makers and criteria weights. The suggested decision-making framework under INVS has effectively handled the overall criteria interactions. In future, the proposed method can be integrated with other neutrosophic sets. [ABSTRACT FROM AUTHOR]

The harmful waste products, beverages, chemical agents that are discharged or emitted by industrial processes are categorized as industrial waste (IW). IW is one of the most severe problems facing governments around the world due to its environmental effect and severity. So the aim of this paper is to develop a method for selecting industrial waste management technique (IWMT) for industry. Dombi Norms were commonly referred to as Dombi operations, but their applications might be better expressed if they are presented with a new level of flexibility within the general parameter. Dombi operations have not yet been applied in non-linear Diophantine fuzzy sets (N-LDFSs) in a suitable form. Therefore we apply the Dombi operators on complex N-LDFS (CN-LDFS) and develop a generalized area of research in decision making problems (DMPs). The majority of decision-making challenges in the actual world contain a wide range of elements that must be taken into account. Making decision under such circumstances can frequently be challenging. To deal with such complicated issues, we require multi-attribute group decision-making (MAGDM) techniques. The aims to discuss MAGDM issues, this paper will introduce a new COmbinative Distance-based ASsessment (CODAS) methodology. In this paper, we put forward three novel aggregation operators (AOs): complex non-linear Diophantine fuzzy Dombi weighted averaging (CN-LDFDWA), complex non-linear Diophantine fuzzy Dombi order weighted averaging (CN-LDFDOWA), and complex non-linear Diophantine fuzzy Dombi hybrid weighted averaging (CN-LDFDHWA). These operators are used in the operational parameter to help a successful solution of the problem. Due to the growing energy and material shortages, as well as to comply with environmental laws and regulations and to take advantage of disposal economics in the current context, industrial waste (IW) should be taken into consideration as one of the potential resources in the steel industry. Further, the proposed decision support model applied to solve a case study regarding to IWMT for Pakistan steel mill corporation (PSMC) under complex N-LDF Dombi operators. The proposed method is more effective and reliable for decision making (DM) model, as compared to the existing method. • We developed aggregation operators (AOs) for CN-LDF Dombi Norms to solve MAGDM issue. • We developed CN-LDFDWA AOs based on CODAS method with all unknown information. • An example regarding to the selection of best IW technique for PSMC is provided. • Comparison provided the feasibility of our work and results are discussed based on ranking. [ABSTRACT FROM AUTHOR]

The decision-maker's judgment deviates from uncertain attribute information will lead to decision risk in multiple attribute decision making (MADM), and different aggregation approaches result in different risk levels. This paper aims to study the risk levels of aggregation operators in MADM with uncertain attribute information. We use the signal detection theory to characterize the decision-maker's noisy perceptions of multiple attributes to present his/her judgment deviation. Then, we establish the aggregation models to aggregate these perceptions based on the weighted averaging (WA) and the ordered weighted averaging (OWA) operators. Furthermore, a risk measurement model of each aggregation approach is constructed to measure the risk levels of the commission risk (CR), the omission risk (OR), and the overall risk. A numerical example is used to verify the validity of the proposed model, while simulation experiments are designed to compare the risk levels of the WA and OWA operators. The results reveal that the overall risk level of the WA operator is higher than that of the OWA operator when judging the quality of the alternative with high standards; otherwise, the WA operator is lower. This finding provides a scientific reference for aggregation approach selection under uncertain information. Permanent link to reproducible Capsule: https://doi.org/10.24433/CO.0911610.v1. • We characterize a decision-maker's noisy perceptions of attributes. • We obtain the noisy perceptions of alternatives according to the characteristics of aggregation approaches. • We propose a risk measurement model of aggregation approaches for MADM. [ABSTRACT FROM AUTHOR]

Micro-mobility has become a mode of transportation since the 2010 s. Because this mode is relatively new, there are many unknowns, and the associated risks must be evaluated so they can be managed safely and effectively. This paper aims to introduce a decision support system (DSS) for ranking risk management alternatives in the micro-mobility sector under an interval-valued q-rung orthopair fuzzy (IVq-ROF) context. In this context, firstly IVq-ROF-interaction aggregation operators (AOs) are introduced for aggregating the information from IVq-ROF perspective. These AOs can provide a flexible mode to tackle realistic decision support problems within the IVq-ROF set (IVq-ROFS) context. Second, a definition of entropy and new entropies for IVq-ROFS is introduced. Third, a hybrid weight-determining formula is presented using an entropy-based weighting approach for the objective weight of criteria and a stepwise weight assessment ratio analysis (SWARA) method for the subjective weight of criteria. Fourth, the weighted integrated sum-product (WISP) approach is expanded from the IVq-ROF perspective. Further, the developed model is utilized in a case study of risk management alternative evaluation in the micro-mobility sector, which shows its applicability and feasibility under the IVq-ROF environment. Sensitivity and comparative investigations are studied to confirm the strength and stability of the obtained result. The best alternative has emerged as the state-private sector cooperation in successfully allocating the risk in the micro-mobility sector. The final results conclude that the developed methodology can provide more accurate decisions while considering multiple inducing indicators and input uncertainties. • A definition of entropy and new entropies for IVq-ROFS are introduced. • A hybrid weight-determining formula is presented. • The classic weighted sum-product (WISP) approach is expanded from IVq-ROFSs perspective. • A case study of risk allocation evaluation in the micro-mobility sector introduced. [ABSTRACT FROM AUTHOR]

The increasing complexity of decision-making environments has led to a rise in the involvement of decision-makers (DMs) in group decision-making problems. Clustering is widely used in large-scale group decision-making (LSGDM) to categorize DMs into smaller groups. Ensuring reasonable decision-making results requires providing explanations for the generated groups during the clustering process. To address the clustering problem in LSGDM within uncertain linguistic environments, this paper proposes a conceptual clustering method based on the linguistic concept lattice. The method efficiently manages comparable and incomparable linguistic information. To achieve interpretable clustering results for DMs, attribute and expert induction matrices are first introduced. Cluster stability analysis is then employed to automatically determine the optimal number of clusters. Second, linguistic truth-valued aggregation operators are proposed to aggregate the linguistic evaluation information of DMs in each cluster. In addition, a consensus reaching process is conducted within each cluster, and a feedback mechanism is established to iteratively update clusters when consensus cannot be reached. Finally, numerical examples and comparative analyses are presented that verify the effectiveness of the proposed approach in effectively addressing the LSGDM problem within uncertain linguistic environments. • Using conceptual clustering for the LSGDM problem. • Linguistic concepts are provided as explanations for the clustering results. • The comparable and incomparable linguistic information is handled in LSGDM. • A novel linguistic concept lattice-based LSGDM approach is proposed. [ABSTRACT FROM AUTHOR]

The q-rung orthopair uncertain linguistic set, which combines the q-rung orthopair fuzzy set and uncertain linguistic variable, can simultaneously represent the quantitative and qualitative information given by experts. In the procedure of addressing q-rung orthopair uncertain linguistic information, to eliminate extreme evaluation values for arguments and capture the multilayer heterogeneous relationship among the membership function and attributes, q-rung orthopair uncertain linguistic interaction power partitioned Maclaurin Symmetric mean operators are presented in this paper. Firstly, we introduce the interaction operational rules between q-rung orthopair uncertain linguistic sets. Then, we integrate the power average operator and partitioned Maclaurin Symmetric mean (PMSM) operator into a framework and present the power PMSM (PPMSM) operator. Furthermore, we embed the PPMSM operator into the q-rung orthopair uncertain linguistic sets based on the interaction operational laws and propose the q-rung orthopair uncertain linguistic interaction power partitioned Maclaurin Symmetric mean operator and its weighted form. Meanwhile, we analyze some properties and special cases of the proposed operators. Afterward, we report an algorithm that we developed for handling multi-attribute decision-making problems based on the proposed operator. Finally, we conduct case studies and comparison analysis over existing methods to demonstrate the effectiveness and superiority of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The aim of this paper is to develop the calculus of hesitant fuzzy numbers (HFNs), have been recently proposed as the newest extension of hesitant fuzzy sets. At first, based on the willingness of decision maker to each part of HFNs, a new method has been proposed to compare them. Then, several t-norm and t-conorm-based aggregation operators of HFNs, i.e., algebraic t-norm and t-conorm, Einstein t-norm and t-conorm, Hamacher t-norm and t-conorm, Frank t-norm and t-conorm have been defined, and some of their mathematical properties are also discussed. As the special cases of the above t-norm and t-conorm-based aggregation operators of HFNs, Archimedean t-norm and t-conorm-based HFN weighted averaging operator, Archimedean t-norm and t-conorm-based HFN weighted geometric operator, Archimedean t-norm and t-conorm-based HFN ordered weighted averaging operator, and Archimedean t-norm and t-conorm-based HFN ordered weighted geometric operator have been proposed. The new problem of improving the process of educational activities under the Covid-19 epidemic conditions, for instance, has been defined as a multi-attribute group decision-making (MAGDM) problem, in which students are its options, courses are its criteria, and teachers are members of the decision-making team. Then, the scores of final exams and teachers' assessments merged together as HFNs, and a new method has been proposed based on the before mentioned operators to solve the resulting MAGDM problem. A numerical example, the results of which are also analyzed, is responsible for explaining what is proposed in this article. Finally, subsequent studies in this area are briefly stated. [ABSTRACT FROM AUTHOR]

In recent years, surprising amounts of news, messages, and reviews of products and services are generated in the online social media. Several efforts are being dedicated to detecting topics, as well as mining opinions in these unstructured texts. There are several approaches that compute opinion polarity, and some of them consider topics for their textual analysis. Nevertheless, discovering topics in opinions continues being challenging; due to opinions are generally short and informally written. Besides, opinions do not have a defined structure in several paragraphs; they are presented most of the time as a composition in a paragraph. In this paper, we propose a method to detect polarity in opinions by topics. Our proposal contributes to the fuzzy polarity calculation of detected topics in Spanish opinions. This method is comprised of three main steps: (1) preprocessing, (2) topic detection and (3) fuzzy polarity detection. It is important to notice the added values of this paper are: (1) the topic detection proposal based on the semantic processing when applying the clustering algorithm on opinion sentences, and (2) the evaluation of different aggregation operators for determining the opinion polarity from a fuzzy logic perspective. Aiming at assessing the quality of the resultant polarity detection by topics, we have conducted two main experiments over the Spanish corpus of opinions about Andalusian Hotels from the TripAdvisor site. The results have shown that our method is able to detect the topics correctly, as well as calculating their opinion polarities. [ABSTRACT FROM AUTHOR]

Copyright of Informatica (03505596) is the property of Slovene Society Informatika 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 proposes an approach to linguistic multiple attribute group decision-making (MAGDM) problems with 2-dimension multi-granularity linguistic information by adding a subjective imprecise estimation of reliability of the linguistic assessment information for expressing fuzzy information appropriately. To avoid the drawback of traditional transformation functions of multi-granularity linguistic information (MGLI) defined in the basic linguistic term set or 2-tuple linguistic information, in this paper, decision information is fully expressed by using 2-dimension interval type-2 trapezoidal fuzzy number (2DIT2TFN) representation model under multi-granular linguistic contexts. Firstly, the definition, operational laws, and ranking method of 2DIT2TFNs are proposed. Then 2-dimension interval type-2 trapezoidal fuzzy ordered weighted average (2DIT2TFOWA) operator and quasi-2-dimension interval type-2 trapezoidal fuzzy ordered weighted average (quasi-2DIT2TFOWA) operator are put forward. Moreover, we apply new operators to developing approach to MAGDM problem with linguistic term set of different odd cardinalities. Finally, a numerical example related to health management is provided to illustrate the utility and effectiveness of the developed method. We also make comparisons between the methods proposed in this paper and some existing ones to confirm its feasibility and rationality. The main contribution of this paper possesses three points: (1) proposing the 2DIT2TFNs, which can reflect the evaluation on objects more reasonably; (2) developing some new aggregation operators under 2-dimension interval type-2 trapezoidal fuzzy environment; and (3) applying new operators to developing approach to MAGDM problem with MGLI. [ABSTRACT FROM AUTHOR]

Multi-criteria decision making (MCDM) technique and approach have been a trending topic in decision making and systems engineering to choosing the probable optimal options. The primary purpose of this article is to develop prioritized operators to multi-criteria decision making (MCDM) based on Archimedean t-conorm and t-norms (At-CN&t-Ns) under interval-valued dual hesitant fuzzy (IVDHF) environment. A new score function is defined for finding the rank of alternatives in MCDM problems with IVDHF information based on priority levels of criteria imposed by the decision maker. This paper introduces two aggregation operators: At-CN&t-N-based IVDHF prioritized weighted averaging (AIVDHFPWA), and weighted geometric (AIVDHFPWG) aggregation operators. Some of their desirable properties are also investigated in details. A methodology for prioritization-based MCDM is derived under IVDHF information. An illustrative example concerning MCDM problem about a Chinese university for appointing outstanding oversea teachers to strengthen academic education is considered. The method is also applicable for solving other real-life MCDM problems having IVDHF information. [ABSTRACT FROM AUTHOR]

The fuzzy aggregation information plays an important role in the group decision support system under the interval-valued hesitant fuzzy information and interval-valued probabilistic hesitant fuzzy information. Therefore, in this paper, we develop a new approach of the interval-valued hesitant fuzzy Frank aggregation (IVHFFA) and interval-valued probabilistic hesitant fuzzy aggregation (IVPHFFA) operators. First, we define some operational laws of IVHEs and IVPHFEs by using Frank t-norm and t-conorm. Furthermore, we develop a series of IVHFFA and IVPHFFA operators based on these operational laws under the IVHF and IVPHF information. Also, discuss some fundamental properties and relations of the proposed aggregation operators for IVHF and IVPHF information. In order to implement the proposed aggregation operators of IVHF and IVPHF information in group decision-making problem, we construct general algorithms for multi-attribute group decision-making problem based on the proposed IVHFFA and IVPHFFA. Finally, from the comparative and sensitivity analysis, the proposed fuzzy decision-making model is more effective and reliable as compared with existing method. [ABSTRACT FROM AUTHOR]

In this paper, we propose new aggregation operators for multi-criteria decision making under linguistic settings. The proposed operators are based on two sets of criteria weights. Besides the primary conventional criteria weights, we introduce a method to deduce secondary criteria weights from the criteria evaluations, which reflect the role of the different criteria in discriminating among the alternatives. The properties of the proposed operators are investigated. An approach for the application of the said operators in a group multi-criteria decision making problem is presented. Following the same, the proposed operators are applied in a case study on supplier selection. The empirical validation of the proposed operators is performed on a set of 12 real datasets. Note : All usages of he, him, his in the paper, also refer to she, and her. [ABSTRACT FROM AUTHOR]

The ordered weighted average (OWA) is an aggregation operator that provides a parameterized family of operators between the minimum and the maximum. This paper presents the OWA weighted average distance operator. The main advantage of this new approach is that it unifies the weighted Hamming distance and the OWA distance in the same formulation and considering the degree of importance that each concept has in the analysis. This operator includes a wide range of particular cases from the minimum to the maximum distance. Some further generalizations are also developed with generalized and quasi-arithmetic means. The use of Bonferroni means under this framework is also studied. The paper ends with an application of the new approach in a group decision making problem with Dempster-Shafer belief structure regarding the selection of strategies. [ABSTRACT FROM AUTHOR]