In the process of multi-UAV cooperative decisionmaking, the information evolution model plays a key role in the communication and fusion of information among multiple agents. However, traditional information fusion models, such as the DeGroot model and the Hegselmann-Krause bounded confidence (HK) model in opinion dynamics, have certain limitations in handling complex noisy environments, adjusting weights dynamically, and processing linguistic information. To address these challenges, this paper proposes an information fusion model that integrates the social network mechanism, the hyperbolic tangent function, and the Monte Carlo method. In each round of information iteration, the model uses the social network structure to simulate the communication relationships among agents. Specifically, the social network is represented as a binary adjacency matrix. Then, a distance measure between linguistic information is defined. Based on this distance, the hyperbolic tangent function is used to determine the weights of agents during the evolution process dynamically. To fully evaluate the model’s performance under noise, Monte Carlo simulations are used for statistical analysis. A case of multi-UAV cooperative reconnaissance is presented to validate the effectiveness of the model. In addition, detailed comparisons with several existing models are conducted. The experimental results show that the proposed model demonstrates clear advantages in terms of anti-interference capability, stability, and adaptability to various linguistic information environments.

In opinion dynamics (OODs), the DeGroot and Hegselmann–Krause (HK) bounded confidence models are foundational tools for studying information evolution. However, both models have unavoidable limitations, particularly in group decision-making scenarios. This article proposes a novel OODs model that integrates the strengths of both the DeGroot and HK models within a unified framework. The proposed model balances ultimate consensus and diversity without requiring a subjectively chosen threshold by introducing an improved hyperbolic tangent function. Adjusting the function’s parameter enables a smooth transition between the DeGroot and HK models, enhancing adaptability across various scenarios. To determine the weights of agents during information evolution, we develop a calculation method based on a distance measure. Furthermore, the model’s properties are thoroughly analyzed through theoretical derivations. The model is extended to the linguistic environment, aligning with natural expression habits in real-world contexts. Comprehensive examples and comparisons validate the proposed model’s effectiveness, demonstrating its superiority and robustness.

Large-Scale Group Decision-Making, propelled by the advent of Large Language Models and the imperative for assertiveness in decision-making processes, emerges as a pivotal area of research. This paper navigates through the landscape of Large-Scale Group Decision-Making, delineating its significance across diverse domains, including social networks and e-democracy. Amidst its nascent status, Large-Scale Group Decision-Making encounters formidable challenges, particularly in information management and fostering consensus among a multitude of experts. This paper aims to illuminate the goals and hurdles facing Large-Scale Group Decision-Making approaches through an exhaustive review of contemporary literature and methodologies. By confronting these challenges head-on, Large-Scale Group Decision-Making holds the potential to redefine decision-making paradigms, bolster assertiveness, and elevate collective problem-solving capabilities in an increasingly interconnected world.

The science of science practice requires the analysis of large and complex bibliometric data. Traditional data exporting from companies’ websites is not sufficient, so APIs are used to access a larger corpus. Therefore, this study aims not only to establish a taxonomy but also to offer a comparative analysis of 44 bibliographic APIs from various non-profit and commercial organizations, analyzing their characteristics and metadata with descriptive analysis, their possible bibliometric analyses, and the interoperability of the APIs across four different data categories: general, content, search, and query modes. The study found that Clarivate Analytics and Elsevier offer highly versatile APIs, while non-profit organizations, such as OpenCitations and OurResearch promote the Open Science philosophy. Most organizations offer free access to APIs for non-commercial purposes, but some have limitations on metadata retrieval. However, CrossRef, OpenCitations, or OpenAlex have no restrictions on the metadata retrieval. Co-author analysis using author names and bibliometric evaluation using citations are the types of analyses that can be done with the data provided by most APIs. DOI, PubMedID, and PMCID are the most versatile identifiers for extending metadata in the APIs. Semantic Scholar, Dimensions, ORCID, and Embase are the APIs that offer the most extensibility. Considering the obtained results, there is no single API that gathers all the information needed to perform any bibliometric analysis. Combining two or more APIs may be the most appropriate option to cover as much information as possible and enrich reports and analyses. This study contributes to advancing the understanding and use of APIs in research practice.

Information that people rely on is often uncertain and partially reliable. Zadeh introduced the concept of Z-numbers as a more adequate formal construct for describing uncertain and partially reliable information. Most existing applications of Z-numbers involve discrete ones due to the high complexity of calculating continuous ones. However, the continuous form is the most common form of information in the real world. Simplifying continuous Z-number calculations is significant for practical applications. There are two reasons for the complexity of continuous Z-number calculations: the use of normal distributions and the inconsistency between the meaning and definition of Z-numbers. In this paper, we extend the triangular distribution as the hidden probability density function of triangular Z-numbers. We add a new parameter to the triangular distribution to influence its convexity and concavity, and then expand the value's domain of the probability measure. Finally, we implement the basic operations of triangular Z-numbers based on the extended triangular distribution. The suggested method is illustrated with numerical examples, and we compare its computational complexity and the entropy (uncertainty) of the resulting Z-number to the traditional method. The comparison shows that our method has lower computational complexity, higher precision and lower uncertainty in the results.

Achieving a good agreement level between decision-makers is a crucial point to solve group decision-making processes. Habitually, it is the duty of the moderator, a person in charge of assuring that the consensus reaching process is conducted correctly. This person also offers advice to the decision-makers with the objective that they modify their assessments and narrow their differences. A great number of theoretical models have been introduced to help or substitute the moderator’s tasks. However, few of them have been implemented in practice. In this study, we present a web group decision support system helping, and even substituting, the moderator’s functions during the whole decision-making process. This system is based on the granular computing paradigm to improve both the consistency and the consensus achieved between the decision-makers. In addition, the system improves them with the possible minimum adjustment, i.e., it tries to modify the decision-makers’ assessments as minimum as possible. This group decision support system provides a web interface allowing to conduct group decision-making processes in which the decision-makers do not have the choice to meet together physically.

The appearance of novel Web technologies and the high amount of information available on the Internet make it difficult for a traditional group decision making method to work correctly. In this kind of environment, the debates become more complicated. For instance, new information arises at any time and some of the discussed information becomes old. Therefore, this situation requires the developing of novel group decision making methods that allow experts to carry out group decision making processes in dynamic contexts. Although there already exists methods that takes into account a variable set of alternatives, there is few research when this variation is present on the criteria values also. In this paper, a novel group decision making method that takes into account scenarios where the amount of criteria and alternatives vary over time is presented. Experts can also select the subset of alternatives and criteria that they want to provide information for. This way, they do not have to provide information for those situations they do not have knowledge about. Moreover, multi-granular fuzzy linguistic modelling methods are used for the experts to select the preference representation that they prefer.

The commonly used preference elicitation method in decision making is the one using pairwise comparison between alternatives. In this kind of decision making scenario, an essential issue requiring attention is that of consistency, particularly in decision problems with numerous alternatives. Consistency is usually linked to the transitivity concept, which is modeled in several diverse ways. Given the importance of avoiding conflicting opinions in decision making, in this study, we propose an approach to improve the consistency when reciprocal preference relations are used. On one hand, consistency is modeled in terms of the multiplicative transitivity property. On the other hand, information granularity is used to introduce and develop the concept of interval reciprocal preference relations in which the entries are constructed as intervals in place of single numeric values. This provides the necessary flexibility to improve the consistency. To illustrate and test the performance of the approach that is proposed here, an example is given.

Body balance is related to different pathologies and its improvement has been shown to help with injuries' recovery and prevention. There are several methods to assess the body balance in a clinical environment, and their quickness and reliability are becoming important. However, traditional procedures have some limitations or disadvantages. Thus, the use of mHealth technologies is considered here to perform the body balance assessment in a reliable and within everyone's reach way. This work presents mBalance, a mHealth system to assess body balance by using a mobile device and a BOSU ball. The mobile accelerometer register the movement and the information is processed and managed to determine the subject's body balance level. In order to show the interest on the mBalance system, the System Usability Scale was used to know the expert opinions.

Internet has experienced a deep change. Now, people can communicate among themselves and share information and experiences thanks to Web 2.0 technologies. One way of storing all these information in a sorted way implies the use of Fuzzy Ontologies. In this paper, a novel and automatic method that is capable of retrieving and storing information coming from Internet users has been designed. Thanks to our method, it is possible to create knowledge databases that store knowledge coming from the consensus of a set of users. Thanks to group decision making methods, it is possible for all these users to debate and reach an agreement. Fuzzy Ontologies provide us with a trustful environment that we can use to store all the retrieved information. They also allow other users to consult and get advantage of the stored knowledge. Multi-granular fuzzy linguistic modelling methods are used in order to create an user-friendly interface for the users to debate and perform queries.

The appearance of Fuzzy Ontologies has improved the way that crisp ontologies use to represent information. Thanks to them, it is possible to represent information in an imprecise and linguistic way using linguistic modelling and fuzzy sets. Nevertheless, there are still several restrictions that must be overcome. For instance, ontology queries must be performed using a linguistic label set with an specific granularity value. In environments where several people have to deal with the same ontology, this can be an inconvenient since the chosen representation does not have to be suitable for all of them. In this paper, multi-granular fuzzy linguistic modelling methods have been used in order to deal with heterogeneous information coming from different sources and to allow experts to choose the linguistic label set that better fits their needs.

With the appearance of Web 2.0 technologies, the way Internet is conceived has dramatically changed. Internet users have begun to perform a more active role in providing and sharing the information available on the Web. Thanks to mobile technologies, users can access the Internet from their smartphones at any time, independently of their location. Moreover, users gather in communities where they can communicate and share information. These communities can hold a high number of people and, consequently, a large amount of information need to be managed. Nevertheless, as the information tends to be disorganized, it is difficult for users to manage them properly to make the most of it. Therefore, there is a necessity of tools that can help users to organize and manage in a proper way the large amount of available information. In this paper, we propose the use of a novel group decision making approach to allow a high number of people to communicate among themselves and reach conclusions in a regulated way. As a high amount of people usually implies too much information, we propose the use of Fuzzy Ontologies as a way to deal with it in an organized way.

In the literature, we find that the consensus models proposed for group decision making problems are guided by consensus degrees and/or similarity measures and/or consistency measures . When we work in heterogeneous group decision making frameworks, we have importance degrees associated with the experts by expressing their different knowledge levels on the problem. Usually, the importance degrees are applied in the weighted aggregation operators developed to solve the decision situations. In this paper, we study another application possibility, i.e., to use heterogeneity existing among experts to guide the consensus model. Thus, the main goal of this paper is to present a new consensus model for heterogeneous group decision making problems guided also by the heterogeneity criterion. It is also based on consensus degrees and similarity measures, but it presents a new feedback mechanism that adjusts the amount of advice required by each expert depending on his/her own relevance or importance level.

The aim of this paper is to present a new mobile group decision making model to deal with heterogeneous information and changeable decision contexts. This model takes into account that experts have different backgrounds and knowledge levels, allowing to use different preference representations as fuzzy preference relations or linguistic preference relations with multigranular linguistic information. Furthermore, we allow to introduce some changes on the alternatives of the problem at every stage of the decision process. To do that: i) a mobile implementation is proposed to reduce the number of changes and ii) a mechanism to insert/remove alternatives is included in the model. Finally, our new decision model incorporates a feedback mechanism that sends recommendations to the experts in order to quickly obtain a high consensus level.

In this contribution, we present a new application based on fuzzy linguistic information to evaluate the quality of digital libraries. The quality evaluation of digital libraries is defined using users' perceptions on the quality of digital services provided through their Web sites. We assume a fuzzy linguistic modeling to represent the users' perception and apply automatic tools of fuzzy computing with words based on some weighted aggregation operators to compute global quality evaluations of digital libraries.

The aim of this paper is to present a decision support system model with two important characteristic: 1) mobile technologies are applied in the decision process and 2) the set of alternatives is not fixed over time to address dynamic decision situations in which the set of solution alternatives could change throughout the decision-making process. We implement a prototype of such mobile decision support system in which experts use mobile phones to provide their preferences anywhere and anytime. To get a general system, experts' preferences are assumed to be represented by different preference representations: 1) fuzzy preference relations; 2) orderings; 3) utility functions; and 4) multiplicative preference relations. Because this prototype incorporates both selection and consensus processes, it allows us to model group decision-making situations. The prototype incorporates a tool for managing the changes on the set of feasible alternatives that could happen throughout the decision process. This way, the prototype provides a new approach to deal with dynamic group decision-making situations to help make decisions anywhere and anytime.

Group decision making (GDM) refers to the selection of an alternative from a set of feasible alternatives that better satisfies some criteria according to a group of individuals (experts). There exist several different models to simulate GDM processes, but many of those models do not usually take into account some dynamical aspects of real decision processes. For example, those models normally do not allow the experts set to change during the process (adding or removing experts), the alternatives to change (incorporating or discarding alternatives) or even to change the criteria. In this work we present a new model which allows to undertake GDM situations in which a large number of individuals (for example an on-line community) has to choose among different alternatives. To be able to obtain a good solution of consensus, the group of experts will be firstly simplified into a smaller group (using a simple clustering technique and a kind of trust network) which can then discuss about best solution to be selected.

Web 2.0 Communities are a quite recent phenomenon with its own characteristics and particularities (possibility of large amounts of users, real time communication...) and so, there is still a necessity of developing tools to help users to reach decisions with a high level of consensus. In this contribution we present a new consensus reaching model with linguistic preferences designed to minimize the main problems that this kind of organization presents (low and intermittent participation rates, difficulty of establishing trust relations and so on) while incorporating the benefits that a Web 2.0 Community offers (rich and diverse knowledge due to a large number of users, real-time communication).