Article
Computer Science, Artificial Intelligence
Sha Fan, Haiming Liang, Yucheng Dong, Witold Pedrycz
Summary: This paper proposes a method based on personalized individual semantics for multi-attribute group decision making (MAGDM) model with flexible linguistic expression. The method takes into account the preferences of individuals and the impact of personalized semantics, and performs collective ranking for individual rankings.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Automation & Control Systems
Feifei Jin, Shuyan Guo, Yuhang Cai, Jinpei Liu, Ligang Zhou
Summary: This paper proposes a 2-tuple linguistic decision-making method that incorporates a consistency adjustment algorithm and a 2-tuple linguistic data envelopment analysis model. It aims to retain the decision makers' initial preference information and improve the consistency and weight generation for alternatives with 2-TLPRs.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Yuan Gao, Yao Li, Zhineng Hu, Cong-Cong Li
Summary: Consistency measurement is a crucial issue in linguistic decision making. Existing literature often overlooks the fact that the same word can have different meanings for different individuals. This study proposes a novel approach that incorporates personalized individual semantics into distributed linguistic representations to improve consistency.
INFORMATION FUSION
(2022)
Article
Automation & Control Systems
Yifang Tao, You Peng, Yuheng Wu
Summary: This paper proposes a novel extended IFPRs, called linguistic dual hesitant fuzzy preference relations (LDHFPRs), to deal with the problem of group decision-making. The LDHFPRs use a set of ordered linguistic terms to describe the preferred and non-preferred evaluation information, capturing the uncertainty and hesitance of each decision maker better. Additive consistency conditions are constructed, and a maximum consistency linear programming model is developed to handle the inconsistent LDHFPRs. Furthermore, a consensus reaching process that focuses on minority but important individual opinions is established.
INTERNATIONAL JOURNAL OF FUZZY SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Prasenjit Mandal, Sovan Samanta, Madhumangal Pal
Summary: The traditional group decision-making models are not suitable for many alternatives, as experts prefer giving preference information based on pairwise comparison of alternatives. This study focuses on developing a new model that manages many alternatives by clustering similar alternatives and using a hierarchical clustering method. The proposed model uses a Pythagorean linguistic preference relation (PLPR)-based approach for comfortable decision preferences and a consistency matrix-based group recommendation and feedback mechanism.
Article
Mathematics
Feifei Jin, Chang Li, Jinpei Liu, Ligang Zhou
Summary: This study proposes a novel decision support model based on consistency adjustment and consensus adjustment algorithms to solve group decision-making problems with distribution linguistic data. The method improves the consistency of DLFPRs through various algorithms and enhances the credibility of decision results by considering expert consensus levels.
Article
Computer Science, Artificial Intelligence
Shu-Ping Wan, Jia Yan, Jiu-Ying Dong
Summary: This paper introduces a new PIS-based approach for selecting COVID-19 surveillance plans, improving consistency by considering personalized individual semantic and minimizing preference adjustments to enhance consensus levels in CRP.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Automation & Control Systems
Yuanhang Zheng, Zeshui Xu, Witold Pedrycz
Summary: This article proposes a new hesitant fuzzy linguistic method to handle hesitant and uncertain preference information provided by decision makers, improving the consistency of preference matrices by characterizing them with granular linguistic preference matrices. By designing a multiplicative consistency index, calculating thresholds, constructing models, and developing algorithms, the method integrates assessment information to derive final decision-making results, demonstrating its reasonability and validity through comparative studies and simulation experiments.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Hai Wang, Dejian Yu, Zeshui Xu
Summary: The paper presents a new process to support the determination of consensus thresholds using a random consensus index for inducing risk preference. A new group decision-making approach is introduced based on probabilistic linguistic preference relations, measuring consensus by the probability of acceptable consensus in a set of preference relations. The proposed approach is effective in cases of low consistency and consensus among experts, as demonstrated in a case study on departure audit in China.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Automation & Control Systems
Zhen Zhang, Zhuolin Li
Summary: Consistency and consensus are crucial in linguistic group decision making, where personalized individual semantics play a significant role. This study focuses on developing models to control consistency and reach consensus, introducing the concept of personalized individual semantics to improve decision making processes.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Feifei Jin, Meng Cao, Jinpei Liu, Luis Martinez, Huayou Chen
Summary: This paper presents a novel method for group decision-making using probabilistic linguistic terms, including a consistency-adjustment algorithm and a trust relationship-driven expert weight determination model. By redefining the consistency of PLPRs and developing a new distance calculation method, the decision consistency and expert weights are improved to determine a reliable ranking of alternatives in a social network environment.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Interdisciplinary Applications
Huimin Zhang, Yiyi Dai
Summary: This paper introduces new distance and entropy measures for hesitant fuzzy linguistic term sets (HFLTSs) and hesitant fuzzy linguistic preference relations (HFLPRs) and proposes an information aggregation method and two consensus improvement models for group decision making (GDM). The first model is a four-stage optimization model, based on which the revised individual and collective opinions can be obtained. The results demonstrate that the proposed models can better deal with the issues in existing consensus models.
COMPUTERS & INDUSTRIAL ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Ying-Ming Wang, Xiang Jia, Hui-Hui Song, Luis Martinez
Summary: This paper proposes a multi-attribute group decision making (MAGDM) method based on regret theory, which considers both the psychological behaviors of decision makers and the consistency of assessments. By defining the regret preference relation and devising four algorithms, the consistency of assessments is improved. Two weighting determination models are built to calculate the weights of attributes and decision makers, and alternatives are ranked based on their overall synthetic values. The application of the proposed method in an emergency assistance area selection problem verifies its feasibility and effectiveness.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
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
Automation & Control Systems
Cong-Cong Li, Haiming Liang, Yucheng Dong, Francisco Chiclana, Enrique Herrera-Viedma
Summary: This article proposes a novel approach to improving consistency in linguistic group decision making, based on personalized individual semantics. By integrating personalized representation into the model, the proposed approach can be used to enhance the consistency of linguistic preference relations.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Management
Diego Garcia-Zamora, Bapi Dutta, Sebastia Massanet, Juan Vicente Riera, Luis Martinez
Summary: This article introduces the use of Minimum Cost Consensus (MCC) models to tackle agreement in Group Decision-Making problems. The Comprehensive Minimum Cost Consensus (CMCC) model, a recent MCC-based model, adds a constraint related to the parameter gamma in addition to the constraint related to the parameter epsilon to enforce modified expert preferences. The paper analyzes the relationship between these constraints from two perspectives, providing simple bounds using inequalities and more precise bounds using Convex Polytope Theory.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Article
Public, Environmental & Occupational Health
Xin Chen, Yucheng Dong, Meng Wu
Summary: During the COVID-19 crisis, policymakers' confidence and public trust have significant impacts on medical capacity investment. Public trust in policymakers affects the spread of the epidemic, while policymakers' behavioral biases impact overall performance.
Article
Engineering, Industrial
Yucheng Dong, Siqi Wu, Xiaoping Shi, Yao Li, Francisco Chiclana
Summary: This article investigates the clustering of failure modes based on their risks in FMEA practice. It proposes the additive N-clustering problem and explores the characteristics of exogenous clustering methods and endogenous clustering methods. It also introduces the Consensus-based ENdogenous Clustering Method (CENCM) as a solution for cases where accurate category thresholds are difficult to provide, and validates its effectiveness through comparisons and simulation experiments.
Article
Operations Research & Management Science
Jindong Qin, Yingying Liang, Luis Martinez, Alessio Ishizaka, Witold Pedrycz
Summary: This paper introduces a novel multiple criteria sorting method, ORESTE-SORT, and its main characteristics and properties. With the introduction of the assignment rule driven by attitudes, this method can effectively handle preference relationships and sort port group competitiveness.
ANNALS OF OPERATIONS RESEARCH
(2023)
Article
Automation & Control Systems
Tapan Senapati, Luis Martinez, Guiyun Chen
Summary: q-Rung orthopair fuzzy sets (q(r)OFSs) is a collection of differentiated ideas for expressing fuzzy data, which can adapt the information region by adjusting the parameter q>=1 and generate more possibilities. This article exhibits some q-rung orthopair fuzzy (q(r)OF) Aczel-Alsina aggregation operators for aggregating q(r)OFSs to solve multiple attribute decision-making (MADM) issues.
INTERNATIONAL JOURNAL OF FUZZY SYSTEMS
(2023)
Article
Automation & Control Systems
Cristina Zuheros, Eugenio Martinez-Camara, Enrique Herrera-Viedma, Francisco Herrera
Summary: The wisdom of the crowd theory states that a nonexpert crowd makes smarter decisions than a reduced set of experts. Evaluations from social networks can enhance the quality of decision-making models. We propose a crowd decision-making model guided by sentiment analysis, which incorporates all the evaluation shades and tackles the lack of information using sparse representation. The results show that integrating the wisdom of the crowd and the different shades of the evaluations enhances the quality of the decision.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Qianlei Jia, Enrique Herrera-Viedma
Summary: In this article, a new layout coordinate system is defined to map Z-numbers to q-ROFSs. The genetic algorithm is adopted to derive the potential probability distribution. An approach for calculating the weighted information entropy of Z-numbers is proposed and proven to be rational. A linguistic Z-PFS weighted aggregation operator is presented, and a score function is defined in the coordinate system. Finally, a decision-making model is constructed based on the new solution.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Jiang Deng, Jianming Zhan, Enrique Herrera-Viedma, Francisco Herrera
Summary: Behavioral decision theory modifies classic decision-making theories to make them more applicable in realistic scenarios. Regret theory, an important component of behavioral decision theories, has been widely used in theories and applications. In this study, a generalized three-way decision method is proposed based on regret theory for incomplete multiscale decision information systems. Experimental results show that the decision-making results of the proposed method maintain over 97% consistency in incomplete information systems.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Computer Science, Information Systems
Yuanyuan Liang, Yanbing Ju, Peiwu Dong, Xiao-Jun Zeng, Luis Martinez, Jinhua Dong, Aihua Wang
Summary: This paper proposes a novel LSGDM consensus model that explores and manages the meso-scale structure among experts using free texts in a social network setting. The model extracts preferences over alternatives from experts' opinions through sentiment analysis and converts them into distributed linguistic preference relation matrices. It also introduces a core-periphery detection method for the social network and derives expert weights based on an optimization model that maximizes expert reliability. Additionally, a two-stage consensus model based on prospect theory is developed to improve group consensus systematically and gradually.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
LeSheng Jin, Zhen-Song Chen, Jiang-Yuan Zhang, Ronald R. Yager, Radko Mesiar, Martin Kalina, Humberto Bustince, Luis Martinez
Summary: Determination of normalized weight vector under bi-polar preferences is crucial in multicriteria decision making. New methods such as the ordered weighted averaging (OWA) aggregation on lattice have been proposed to determine weights for the elements in a partially ordered set that can represent bi-polar preferences. However, when only fuzzy relations are available, new generalized methods need to be developed.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
LeSheng Jin, Ronald R. Yager, Zhen-Song Chen, Radko Mesiar, Luis Martinez, Rosa M. Rodriguez
Summary: This paper discusses rules-based decision-making methods in uncertain decision scenarios with or without fusion processes. It proposes special averaging aggregation methods and transformations for basic uncertain information vector. The paper also explores [0, 1]-valued, [-1, 1]-valued, and vector-valued bipolar preferences and their relations. The aggregation methods mainly revolve around Sugeno integral. Finally, it proposes multiple aggregation schemes with vector-valued preferences and provides a few related comparisons.
Article
Engineering, Electrical & Electronic
Shubo Wang
Summary: This article proposes a novel nonlinear uncertainty estimator-based time-varying sliding mode control (SMC) scheme for servo systems with prescribed performance. The scheme uses a nonlinear uncertainty estimator to handle unknown nonlinearities and a robust integral of the sign of the error (RISE) feedback to handle estimation errors and uncertainties. A modified prescribed performance function (PPF) is incorporated into the control design to restrict tracking errors within predefined boundaries, and a time-varying sliding mode (TVSM) controller is developed to improve control performance. The validity and feasibility of the proposed scheme are verified through simulations and experiments based on a motor driving system.
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION
(2023)
Article
Computer Science, Artificial Intelligence
Qi Sun, Francisco Chiclana, Jian Wu, Yujia Liu, Changyong Liang, Enrique Herrera-Viedma
Summary: This article proposes a novel framework for managing the noncooperative behavior of subgroups in large-scale group decision making using weight penalty. The framework defines a trust-consensus index (TCI) by combining trust score and consensus degree and uses an algorithm to detect subgroups in a large network. A weight penalty feedback model is established to manage subgroups that are detected as discordant and noncooperative. The article also provides a detailed analysis on computing the optimal penalty parameter to prevent excessive penalization and includes numerical and comparative analyses to verify the proposed method's validity.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Automation & Control Systems
Jose Ramon Trillo, Enrique Herrera-Viedma, Juan Antonio Morente-Molinera, Francisco Javier Cabrerizo
Summary: Debate is a process of arriving at a reasoned opinion, requiring individuals to defend their judgments. It has been used in group decision-making (GDM) to improve decisions, but aggressive language during debates can hinder consensus. To address this, a new method incorporating sentiment analysis techniques is proposed to identify aggressive comments. Two procedures are developed based on information extracted during debates to assign weights to experts and introduce new consensus measures for the final decision. This method utilizes extracted information throughout the decision process, aligning with real-world GDM processes.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Kuo Pang, Luis Martinez, Nan Li, Jun Liu, Li Zou, Mingyu Lu
Summary: This paper proposes a MAGDM approach based on linguistic concept lattices, which is effective and rational in aggregating the opinions of multiple individuals during the decision-making process. By constructing linguistic concept lattices and introducing the extent of fuzzy linguistic concepts and meet-irreducible elements, information loss can be reduced and the rationality of decision results can be enhanced.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
C. Lopez-Molina, S. Iglesias-Rey, B. De Baets
Summary: Quantitative image comparison is a critical topic in image processing literature, with diverse applications. Existing measures of comparison often overlook the context in which the comparison takes place. This paper presents a context-aware comparison method for binary images, tested on the BSDS500 benchmark.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Lorenz Linhardt, Klaus-Robert Mueller, Gregoire Montavon
Summary: This paper investigates the issue of mismatches between the decision strategy of the explainable model and the user's domain knowledge, and proposes a new method EGEM to mitigate hidden flaws in the model. Experimental results demonstrate that the approach can significantly reduce reliance on Clever Hans strategies and improve the accuracy of the model on new data.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Zhimin Shao, Weibei Dou, Yu Pan
Summary: This paper proposes a novel algorithm, Dual-level Deep Evidential Fusion (DDEF), to integrate multimodal information at both the BBA level and multimodal level, aiming to enhance accuracy, robustness, and reliability. The DDEF approach utilizes the Dirichlet framework and BBA methods for effective uncertainty estimation and employs the Dempster-Shafer Theory for dual-level fusion. The experimental results show that the proposed DDEF outperforms existing methods in synthetic digit classification and real-world medical prognosis after BCI treatment.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Abhishek K. Ghosh, Danilo S. Catelli, Samuel Wilson, Niamh C. Nowlan, Ravi Vaidyanathan
Summary: The inability of current FM monitoring methods to be used outside clinical environments has made it challenging to understand the nature and evolution of FM. This investigation introduces a novel wearable FM monitor with a heterogeneous sensor suite and a data fusion architecture to efficiently capture and separate FM from interfering artifacts. The performance of the device and architecture were validated through at-home use, demonstrating high accuracy in detecting and recognizing FM events. This research is a major milestone in the development of low-cost wearable FM monitors for pervasive monitoring of FM in unsupervised environments.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Jianlei Kong, Xiaomeng Fan, Min Zuo, Muhammet Deveci, Xuebo Jin, Kaiyang Zhong
Summary: In this study, we propose an intelligent traffic flow prediction framework based on the adaptive dual-graphic transformer with a cross-fusion strategy, aiming to uncover latent graphic feature representations that transcend temporal and spatial limitations. By establishing a traffic spatiotemporal prediction model using a cross-fusion attention mechanism, our proposed model achieves superior prediction performance on practical urban traffic flow datasets, particularly for long-term predictions.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Huilai Zhi, Jinhai Li
Summary: This article addresses the issue that conflict analysis based on single-valued information systems is no longer valid. It proposes a conflict analysis method based on component similarity, which uses three-way n-valued concept lattices to handle set-valued formal contexts and realizes fast conflict analysis from an information fusion viewpoint. Experimental results verify the effectiveness of this method in reducing time consumption.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Huchang Liao, Jiaxin Qi, Jiawei Zhang, Chonghui Zhang, Fan Liu, Weiping Ding
Summary: In this paper, a hospital selection approach based on a fuzzy multi-criterion decision-making method is proposed. This approach considers sentiment evaluation values of unstructured data from online reviews and structured data of public indexes simultaneously. The methodology involves collecting and processing online reviews, classifying topics and sentiments, quantifying sentiment analysis results using fuzzy numbers, and obtaining final preference scores of hospitals based on patients' preferences. A case study and robustness analysis are conducted to validate the effectiveness of the method.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Faramarz Farhangian, Rafael M. O. Cruz, George D. C. Cavalcanti
Summary: The proliferation of social networks has posed challenges in combating fake news, but automatic fake news detection using artificial intelligence has become more feasible. This paper revisits the definitions and perspectives of fake news and proposes an updated taxonomy, based on multiple criteria, for the field. The study finds that optimal feature extraction techniques vary depending on the dataset, and context-dependent models based on transformer models consistently exhibit superior performance.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Mariana A. Souza, Robert Sabourin, George D. C. Cavalcanti, Rafael M. O. Cruz
Summary: In this study, a dynamic selection technique is proposed to handle sparse and overlapped data. The technique leverages the relationships between instances and classifiers to learn a dynamic classifier combination rule. Experimental results show that the proposed method outperforms static selection and other dynamic selection techniques.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Bin Yu, Ruihui Xu, Mingjie Cai, Weiping Ding
Summary: This paper introduces a clustering method based on non-Euclidean metric and multi-granularity staged clustering to address the challenges posed by complex spatial structure data to traditional clustering methods. The method improves the similarity measure and employs an attenuation-diffusion pattern for local to global clustering, achieving good clustering results.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Jian Zhu, Pengbo Hu, Bingqian Li, Yi Zhou
Summary: The acquisition of multi-view hash representation for heterogeneous data is highly important for multimedia retrieval. Current approaches suffer from limited retrieval precision due to insufficient integration of multi-view features and failure to effectively utilize metric information from diverse samples. In this paper, we propose an innovative method called Fast Metric Multi-View Hashing (FMMVH), which demonstrates the superiority of gate-based fusion over traditional methods. We also introduce a novel deep metric loss function to leverage metric information from dissimilar samples. By optimizing and employing model compression techniques, our FMMVH method significantly outperforms existing state-of-the-art methods on benchmark datasets, with up to 7.47% improvement in mean Average Precision (mAP).
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Fayaz Ali Dharejo, Iyyakutti Iyappan Ganapathi, Muhammad Zawish, Basit Alawode, Moath Alathbah, Naoufel Werghi, Sajid Javed
Summary: The resource-limited nature of underwater vision equipment affects underwater robotics and ocean engineering tasks. Super-resolution methods, particularly using Vision Transformers (ViTs), have emerged to enhance low-resolution underwater images. However, ViTs face challenges in handling severe degradation in underwater imaging. In contrast, Multi-scale ViTs (MViTs) overcome these challenges by preserving long-range dependencies through evolving channel capacity. This study proposes a novel algorithm, SwinWave-SR, for efficient and accurate multi-scale super-resolution for underwater images.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Weiwei Jiang, Haoyu Han, Yang Zhang, Jianbin Mu
Summary: This study incorporates federated learning and split learning paradigms with satellite-terrestrial integrated networks and introduces a split-then-federated learning framework and federated split learning with long short-term memory to handle sequential data in STINs. The proposed solution is demonstrated to be effective through a case study of electricity theft detection based on a real-world dataset.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Najah Abuali, Mohammad Bilal Khan, Farman Ullah, Mohammad Hayajneh, Hikmat Ullah, Shahid Mumtaz
Summary: The demand for innovative solutions in biomedical systems for precise diagnosis and management of critical diseases is increasing. A promising technology, non-invasive and intelligent Internet of Medical Things (IoMT) system, emerges to assess patients with reduced health risks. This research introduces a comprehensive framework for early diagnosis of respiratory abnormalities through RF sensing and SDR technology. The results highlight the superior performance of deep learning frameworks in classifying respiratory anomalies.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Shichen Huang, Weina Fu, Zhaoyue Zhang, Shuai Liu
Summary: In the era of adversarial machine learning (AML), developing robust and generalized algorithms has become a key research topic. This study proposes a global similarity matching module and a global-local cognition fusion training mechanism based on relationship adversarial sample generation to improve image-text matching algorithm. Experimental results show significant improvements in accuracy and robustness, performing well in facing security challenges and promoting the fusion of visual and linguistic modalities.
INFORMATION FUSION
(2024)
