Article
Computer Science, Artificial Intelligence
Li Yan, Zheng Pei
Summary: The notable characteristic of large-scale linguistic decision-making problems is the abundance of decision makers using fuzzy linguistic representation models, leading to different linguistic assessments. A novel linguistic decision-making method based on the voting model is proposed to handle multi-linguistic assessments in large-scale linguistic decision-making problems.
Article
Automation & Control Systems
Enrique Herrera-Viedma, Ivan Palomares, Cong-Cong Li, Francisco Javier Cabrerizo, Yucheng Dong, Francisco Chiclana, Francisco Herrera
Summary: The article provides an overview of fuzzy and linguistic decision-making trends, studies, methodologies, and models developed in the last 50 years. It discusses core decision-making frameworks and new complex decision-making frameworks that have emerged in recent years. The challenges associated with these frameworks and key guidelines for future research in the field are highlighted.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Haiming Liang, Xia Chen, Cong-Cong Li, Hengjie Zhang
Summary: This paper introduces a method for comparing linguistic distribution assessments using stochastic dominance, and designs a consensus reaching resolution framework based on linguistic stochastic dominance. Empirical studies show that the proposed method outperforms traditional methods and provides more accurate individual ranking results.
INFORMATION FUSION
(2021)
Review
Green & Sustainable Science & Technology
Ali Azam, Ammar Ahmed, Muhammad Sajid Kamran, Li Hai, Zutao Zhang, Asif Ali
Summary: The study visualized the development trends and current research status of mechanical energy harvesting (MEH) through bibliometric analysis, identifying main research areas, current knowledge status and hotspots. Analysis of grants and collaborating countries highlighted the favorable policy support from China, the USA, England, and South Korea for MEH research development.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2021)
Article
Computer Science, Artificial Intelligence
Yixin Zhang, Zhinan Hao, Zeshui Xu, Xiao-Jun Zeng, Xinxin Xu
Summary: This study aims to develop a process-oriented probabilistic linguistic decision-making framework to solve multi-attribute decision making problems. By introducing parameters, improving decision rules, determining attribute weights, applying the framework to solve specific problems, and conducting discussion and comparative analysis to validate its effectiveness.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Automation & Control Systems
Xiaomei Mi, Huchang Liao, Xiao-Jun Zeng, Abdullah Al-Barakati
Summary: Probabilistic linguistic preference relation (PLPR), expressed in probabilistic linguistic term sets, is a flexible tool for experts to express linguistic preferences in pairwise comparisons. This study introduces a simplified PLPR based on reference objects to address the issue of missing elements in PLPRs when dealing with a large number of objects. A consistency measure is also introduced to assess the reliability of the simplified PLPR. An illustrative example demonstrates the efficiency and validity of the simplified PLPR in decision making.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Environmental Sciences
Chao Xu, Tong Yang, Kai Wang, Lin Guo, Xiaomin Li
Summary: This study uses bibliometric analysis to reveal the research progress and focus in the field of coal and gas outburst from the aspects of time distribution, cooperation network, and keywords. The study found that China, Australia, and the USA are the main research countries, while China University of Mining and Technology, Chongqing University, and Henan Polytechnic University are the main research institutions. The findings can provide scientific reference for coal and gas outburst research.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2023)
Article
Computer Science, Interdisciplinary Applications
Yuzhu Wu, Qin Ran
Summary: This paper investigates linguistic multiple attribute decision making with flexible linguistic expressions and proposes a solution based on interval estimations. Numerical and comparative analysis supports the feasibility of this approach.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Yuzhu Wu, Yuan Gao, Bowen Zhang, Witold Pedrycz
Summary: This paper proposes a minimum information-loss transformation framework to support the useful fusion of heterogeneous distributed information in linguistic group decision making. By defining distributed linguistic distance measurements, the information loss among heterogeneous distributed linguistic preference information can be measured, and several minimum information-loss transformation models are proposed. The flexibility of distributed linguistic information is studied through numerical examples and comparative analyses to justify the effectiveness of the proposed models.
INFORMATION FUSION
(2023)
Article
Computer Science, Artificial Intelligence
Walayat Hussain, Muhammad Raheel Raza, Mian Ahmad Jan, Jose M. Merigo, Honghao Gao
Summary: This article proposes an SLA violation risk mitigation model that uses OWA and LSTM for complex QoS prediction. The approach considers all possible attitudinal behavior of the service provider and provides intelligent recommendations for mitigating actions.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Automation & Control Systems
Yuzhu Wu, Yucheng Dong, Jindong Qin, Witold Pedrycz
Summary: This article introduces the concept of flexible linguistic preference relations (FLPRs) and proposes a method to rank alternatives based on FLPRs by exploring linguistic distribution (LD) and priority-based approximation (PA). By approximating FLPRs to distribution linguistic preference relations (DLPRs) and deriving priority vectors with desired properties, the proposed method is illustrated through a comparative analysis of priority vectors derived from different types of linguistic preference relations.
IEEE TRANSACTIONS ON CYBERNETICS
(2021)
Article
Computer Science, Artificial Intelligence
Sihai Zhao, Yucheng Dong, Siqi Wu, Luis Martinez
Summary: This paper systematically studies the linguistic scale consistency issues in multi-granularity decision making contexts. Necessary and sufficient conditions for consistent multi-granularity representation and a sufficient condition for the consistent multi-granularity aggregation are analytically presented. An attitude-based linguistic representation method (ALRM) is proposed to improve the consistent multi-granularity ranking, showing advantages over traditional linguistic approach in detailed numerical analysis and simulation experiments.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Biswajit Sarkar, Animesh Biswas
Summary: This article introduces a new family of linguistic Pythagorean fuzzy aggregation operations and discusses their necessary properties. A methodology for addressing multi-criteria group decision-making problems is proposed using weighted distance measures and entropy measures. Aggregation is done at the final stage to obtain the final ranking of alternatives.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2021)
Review
Psychology, Multidisciplinary
Zhibin Peng, Zhiyong Hu
Summary: This study reviewed linguistic research published in SSCI and A & HCI journals using CiteSpace software. The results showed that COVID-19-related linguistic research is topically limited and insufficient attention has been given to certain theories and methods in exploring pandemic discourses and texts.
FRONTIERS IN PSYCHOLOGY
(2022)
Article
Automation & Control Systems
Cong-Cong Li, Yucheng Dong, Witold Pedrycz, Francisco Herrera
Summary: This article proposes a continual PIS-learning-based consensus approach in linguistic group decision making. The approach updates personalized individual semantics using a consistency-driven methodology, and detects the consensus process through consensus measurement and feedback recommendation.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Computer Science, Cybernetics
Miriam Edith Perez-Romero, Victor G. Alfaro-Garcia, Jose M. Merigo, Martha Beatriz Flores-Romero
Summary: The objective of this study is to introduce the covariance ordered weighted logarithmic averaging (Cov-OWLA) operators for decision-making processes in uncertain environments. An illustrative example using real-world data shows a positive linear relation between the introduced variables. The aggregation using Cov-OWLA operators demonstrates a better fit compared to traditional methods.
CYBERNETICS AND SYSTEMS
(2023)
Article
Business
Leslier Valenzuela-Fernandez, Ignacio Munoz Quezada, Jose M. Merigo
Summary: This research presents a bibliometric analysis of advertising trends in the business area and provides valuable information for future research. The study shows a growing trend in the number of advertising publications and identifies the most cited and influential journals in the field, such as the Journal of Advertising and the Journal of Advertising Research.
JOURNAL OF GLOBAL SCHOLARS OF MARKETING SCIENCE
(2023)
Article
Economics
Dejian Yu, Libo Sheng, Shunshun Shi
Summary: This research offers a comprehensive examination of the Journal of Forecasting (JoF) from two perspectives. It analyzes the number of publications, citations, and content of 1403 indexed articles from 1982 to 2019, as well as the knowledge flow between JoF and other categories. The research presents a novel and comprehensive framework for journal evaluation, providing insights into the historical patterns, current developments, and future dynamics of JoF.
JOURNAL OF FORECASTING
(2023)
Correction
Business
Alicia Mas-Tur, Norat Roig-Tierno, Shikhar Sarin, Christophe Haon, Trina Sego, Mustapha Belkhouja, Alan Porter, Jose M. Merigo
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
(2023)
Article
Business, Finance
Emili Vizuete-Luciano, Oktay Guzel, Jose M. Merigo
Summary: This study utilizes bibliometric analyses to examine the research field of Pay-What-You-Want (PWYW) pricing strategy. The analysis identifies influential cited works and authors, and reveals the intellectual and thematic structure of the field. Based on the findings, future research paths are suggested.
JOURNAL OF REVENUE AND PRICING MANAGEMENT
(2023)
Article
Computer Science, Artificial Intelligence
Dejian Yu, Tianxing Pan, Zeshui Xu, Ronald R. R. Yager
Summary: In recent years, the OWA operator has received increasing attention in the academic community. Growth curve analysis, commonly used in ecosystem studies, suggests that this trend will continue. However, previous literature has not provided a comprehensive overview of the field's development and evolution. This study employed classic main path analysis and its variations on a citation network of 1474 papers to uncover the development trajectories and research topics of OWA. The findings reveal the pervasive presence of weight generation and operator generalization in the OWA domain, the dynamic and multi-period nature of the multiple criteria decision-making process, and the incorporation of theories like social network theory and expanded applications of the OWA operator.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Computer Science, Information Systems
Dejian Yu, Zhaoping Yan
Summary: This article evaluates 1446 articles related to the PageRank (PR) algorithm and explores the overall development trend of the PR domain through main path analysis (MPA). By identifying leading papers through two main paths, the backbone of the PR field is outlined. Four main subareas are investigated, including accelerating PR computation, comprehensive applications of PR, research on academic impact assessment, and age preference in network evolution. The article discusses research findings and future directions of the PR field, providing insight into the knowledge evolution of the PR field over the past two decades through MPA.
JOURNAL OF INFORMATION SCIENCE
(2023)
Article
Computer Science, Artificial Intelligence
Walayaty Hussain, Jose M. Merigo, Jaime Gil-Lafuente, Honghao Gao
Summary: Neural network methods are widely used in business for prediction, clustering, and risk management. This paper proposes a neural network sorting mechanism to handle complex nonlinear relationships in datasets, reducing computational complexities. The research opens up a new area for complex nonlinear predictions with large datasets.
Article
Industrial Relations & Labor
Dejian Yu, Bo Xiang
Summary: The purpose of this study is to comprehensively review the field of human resource management (HRM) and employment relations (ERs) and explore its knowledge map, knowledge evolution trends and paths, and paradigm shifts. The study applies the Structural Topic Model combined with Word2vec to analyze 23,786 papers from 29 important journals in the field from 1990 to 2021. The findings include the identification of sixteen research topics, the mapping of topic popularity trends over time, and the exploration of research topic evolution from a semantic perspective.
INTERNATIONAL JOURNAL OF MANPOWER
(2023)
Article
Computer Science, Interdisciplinary Applications
Dejian Yu, Anran Fang
Summary: This paper provides a systematic review of the knowledge trajectory and structure of the supply chain integration (SCI) field. The study identifies three distinct knowledge development trajectories and reveals three critical subfields. It highlights the lack of unified conclusions regarding the definition, content, and dimensions of SCI, as well as the long-standing research elements of influencing factors and performance consequences. The study also emphasizes the importance of building theoretical models, integrated systems, and applying blockchain technology to improve SCI.
JOURNAL OF ENTERPRISE INFORMATION MANAGEMENT
(2023)
Article
Computer Science, Artificial Intelligence
Dejian Yu, Bo Xiang
Summary: Artificial Intelligence (AI) has significantly impacted various aspects of social life. This study analyzed 177,204 documents published from 1990 to 2021 in AI research and used the LDA model to extract 40 topics from the abstracts. The study identified 7 subfields in the AI field and aggregated the results to understand research characteristics from different perspectives. These findings are valuable for researchers and institutions in selecting research directions and for newcomers to comprehend the dynamics of the field.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Dejian Yu, Anran Fang, Zeshui Xu
Summary: This study utilizes topic models to extract ten crucial scientific topics from a large dataset of fuzzy research articles and thoroughly discusses their characteristics and trends over time at both journal and country/region levels. The findings provide valuable insights into the distribution and future development of fuzzy research topics.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Dejian Yu, Yan Liu, Zeshui Xu
Summary: Since the proposal of the preference ranking organization method for enrichment evaluations (PROMETHEE), numerous papers have been published in WoS-indexed journals, covering applications across various fields. This paper aims to present the dynamic evolution of knowledge regarding cognitive structure and transmission trajectories from a longitudinal perspective. By retrieving 1351 documents from 1982 to 2021, the interrelationships and characteristics of essential themes, as well as key thematic areas formed through their evolution, were explored. The most critical milestone documents were identified and presented chronologically, serving as an entry point for newcomers in this domain.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Walayat Hussain, Jose M. Merigo, Honghao Gao, Asma Musabah Alkalbani, Fethi A. Rabhi
Summary: Due to the lack of a common framework, the selection of cloud providers and allocation of marginal resources have become complicated. Existing frameworks have ignored the complex nonlinear relationships between service evaluation criteria, leading to ineffective decision-making systems. This paper proposes a centralized framework that considers consumer's customized priority criteria, determines relative importance, and intelligently assigns weights to each criterion. The framework enables optimal service provider selection and wise resource management, fostering sustainable and trusted relationships.
IEEE TRANSACTIONS ON SERVICES COMPUTING
(2023)
Article
Management
Christian A. Cancino, Jose M. Merigo, David Urbano, J. Ernesto Amoros
Summary: This study analyzes the journals and universities that published research on entrepreneurship and innovation by Ibero-American authors between 1986 and 2015. The results show that the most outstanding researchers in the region are mainly from Spain and Portugal. Spanish researchers, in particular, are the most productive and influential authors in the region. A small group of researchers from Chile, Argentina, and Mexico are also highly influential. Latin American researchers need to deepen their international academic networks.
JOURNAL OF SMALL BUSINESS MANAGEMENT
(2023)
