Article
Computer Science, Artificial Intelligence
Noor Rehman, Abbas Ali
Summary: The paper points out the inconsistency in the transfer function for computing fuzzy preference degree by Pan et al., and presents modified versions. New concepts like fuzzy upward beta-coverings are introduced and their properties are studied. Additionally, a new approach to multiple attribute decision making based on fuzzy upward rough set is proposed.
Article
Computer Science, Interdisciplinary Applications
Quanyu Ding, Ying-Ming Wang, Mark Goh
Summary: This paper introduces a new solution for group emergency decision-making (GEDM) based on the extended TODIM method, addressing the complexity and fuzziness of the emergency decision-making environment, discussing the effectiveness and applicability of the proposed method.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Zhehuang Huang, Jinjin Li, Yuhua Qian
Summary: This paper proposes a new robust rough set model by combining various rough set methods to address the limitations of the traditional fuzzy beta covering method in real data. By reconstructing the upper and lower approximations of the target concept, introducing a fuzzy dependence function to evaluate the classification ability, and utilizing a feature selection algorithm for dimensionality reduction, the proposed model demonstrates good robustness on datasets contaminated with noise and outperforms some state-of-the-art feature learning algorithms in terms of classification accuracy and the size of the selected feature subset.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Likui An, Sinan Ji, Changzhong Wang, Xiaodong Fan
Summary: A novel model, weighted multigranulation fuzzy decision rough sets, is proposed in this paper, which uses Gaussian kernel to compute similarity and obtain multiple fuzzy granulations from multisource fuzzy information system. The proposed method is compared with Sun's multigranulation rough set model, demonstrating its effectiveness in multisource data analysis.
Article
Computer Science, Artificial Intelligence
Mohammed Atef, Abd El Fattah El Atik
Summary: This article introduces covering-based multigranulation fuzzy rough sets models and four different types of variants, discussing the characteristics of these models and comparing them with previous models. Finally, the proposed models are applied with an algorithm on certain drug forms to assist experts in medical decision making.
Article
Computer Science, Artificial Intelligence
Mehdi Divsalar, Marzieh Ahmadi, Elnaz Ebrahimi, Alessio Ishizaka
Summary: A probabilistic hesitant fuzzy set is introduced as a generalization of the hesitant fuzzy set for handling uncertain information when there is no complete consensus among decision-makers. This paper presents a novel Choquet integral-based TODIM method and validates its effectiveness through a supplier selection problem in the dairy industry.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Xianyong Zhang, Jiefang Jiang
Summary: This study improves the Variable Precision Multigranulation Fuzzy Rough Sets (VP-MFRSs) by proposing Decision-Theoretic Multigranulation Fuzzy Rough Sets (DT-MFRSs) which systematically fuse the multigranulation maximum and minimum. DT-MFRSs provide tri-level analysis of measurement, modeling, and reduction via three-way decisions. The study extends and improves VP-MFRSs by introducing optimistic, pessimistic, and compromised models, and enhances uncertainty optimization through a new reduction criteria.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Chenxia Jin, Jusheng Mi, Fachao Li, Meishe Liang
Summary: This study explores and applies the fusion of probabilistic hesitant fuzzy sets (PHFSs) and rough sets in uncertain multi-criteria decision-making (MCDM). It introduces an advanced method to obtain normalized PHFSs (NPHFSs), establishes a novel probabilistic hesitant fuzzy rough set (PHFRS) model, and proposes a fuzziness-based objective weight determination method. The effectiveness of the proposed method is demonstrated through experimental comparisons.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Wen Liu, Ju-Sheng Mi, Yan Sun
Summary: This paper introduces hesitant fuzzy set and its related concepts and operations, solves the single axiomatization problem of hesitant fuzzy rough approximation operator, and studies operators derived from different hesitant fuzzy relations. Finally, the advantages and disadvantages of three types of fuzzy sets are compared through cases.
Article
Computer Science, Artificial Intelligence
Zhanao Xue, Bingxin Sun, Haodong Hou, Wenli Pang, Yanna Zhang
Summary: This article proposes intuitionistic hesitant fuzzy sets and multi-granulation rough intuitionistic hesitant fuzzy set models, and establishes three-way decision models. The research results show that these models can effectively evaluate objects with different attitudes and provide decision-making solutions.
COGNITIVE COMPUTATION
(2022)
Article
Computer Science, Artificial Intelligence
Archana Dikshit-Ratnaparkhi, Dattatraya Bormane, Rajesh Ghongade
Summary: The paper discusses a novel method that incorporates entropy-based attribute weighting and evaluates approximate sets within a fuzzy rough framework. By revolutionizing the decision-making process with powerful tools for handling vagueness and uncertainty in data sets, a new approach for multicriteria decision-making is proposed.
COMPLEX & INTELLIGENT SYSTEMS
(2021)
Article
Engineering, Multidisciplinary
Guangfen Yang, Min Ren, Xiumei Hao
Summary: In this paper, a novel fuzzy entropy named as the exponential probabilistic hesitant fuzzy entropy is proposed to measure the uncertainty of probabilistic hesitant fuzzy information. Based on this, a multi-criteria decision-making (MCDM) problem is presented, and an application case in green building demonstrates the efficiency of the proposed method.
ALEXANDRIA ENGINEERING JOURNAL
(2023)
Article
Computer Science, Artificial Intelligence
Lin Sun, Lanying Wang, Weiping Ding, Yuhua Qian, Jiucheng Xu
Summary: The article introduces a feature selection method based on FNMRS for preprocessing data and improving its classification performance in heterogeneous data sets. The approach constructs uncertainty measures using fuzzy neighborhood rough sets and neighborhood multigranulation rough sets, and provides optimistic and pessimistic FNMRS models along with fuzzy neighborhood entropy-based uncertainty measures. Additionally, the Fisher score model is utilized to reduce the complexity of high-dimensional data sets by deleting irrelevant features and a forward feature selection algorithm is presented to enhance the performance of heterogeneous data classification.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Chao Fu, Keyun Qin, Lei Yang, Qian Hu
Summary: Covering rough sets are successfully applied to decision analysis due to their strong representing capability for uncertain information. Hesitant fuzzy multi-attribute decision-making (HFMADM) has received increasing attention as a research hotspot in decision analysis. However, existing covering rough sets cannot handle hesitant fuzzy information, limiting their application. To tackle this problem, we propose hesitant fuzzy beta-covering rough set models and discuss their application to HFMADM.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Quanyu Ding, Mark Goh, Ying-Ming Wang
Summary: This paper introduces a method to address dynamic emergency decision-making problems by using the interval evidential reasoning and interval-valued hesitant fuzzy TODIM method, combined with geometric area method and probability method to determine the ranking of decision alternatives. The effectiveness of the method is validated through a practical case study and comparative analysis.
Article
Computer Science, Artificial Intelligence
Chao Zhang, Wenhui Bai, Deyu Li, Jianming Zhan
Summary: This paper investigates a new method for multiple attribute group decision making (MAGDM) using multigranulation probabilistic models, MULTIMOORA, and the technique of precise order preference (TPOP) in incomplete q-rung orthopair fuzzy information systems. Experimental studies demonstrate the applicability and validity of the proposed methodology.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2022)
Article
Computer Science, Artificial Intelligence
Erliang Yao, Deyu Li, Yanhui Zhai, Chao Zhang
Summary: In this article, two novel multilabel feature selection methods are proposed from the perspective of discerning sample pairs. These methods address the issues of ineffective feature distinction and high time complexity in existing approaches for multilabel data. Experimental results demonstrate that the proposed algorithms outperform other methods in terms of performances and the running time.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Yanhui Zhai, Jianjun Qi, Deyu Li, Chao Zhang, Weihua Xu
Summary: Three-way decision (3WD) is a widely studied mathematical theory that has applications in various fields. This paper proves the connection between three-way concept lattice (3WCL) and classical concept lattices, introduces a structure theorem to highlight its importance, and discusses additional problems.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2022)
Article
Computer Science, Artificial Intelligence
Wenjie Wang, Jianming Zhan, Chao Zhang, Enrique Herrera-Viedma, Gang Kou
Summary: This research proposes a new decision-making method that combines regret theory with three-way decision in fuzzy incomplete information systems. The method considers the influence of decision-makers' psychological states on decision outcomes by obtaining integrated utility perception values.
INFORMATION FUSION
(2023)
Article
Computer Science, Artificial Intelligence
Wenhui Bai, Chao Zhang, Yanhui Zhai, Arun Kumar Sangaiah
Summary: Water quality inspection is essential for the safe utilization of water resources, and complex data modeling and analysis are crucial in finding the best water quality resources. However, challenges such as missing data, differences in decision results, and bounded rationality of decision-makers still exist in water quality inspection. Therefore, this paper proposes a comprehensive multi-attribute group decision-making approach for water quality inspection based on stable and behavioral decision-making in multi-granularity incomplete intuitionistic fuzzy information systems.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2023)
Article
Automation & Control Systems
Wentao Li, Deyu Li, Huiyan Zhang, Chao Zhang, Peng Shi
Summary: For the multi-source event-based decision system (MSEDS), the fusion of information from multiple sources to make comprehensive decisions is a key research area. This paper introduces support characteristic levels and discusses their models and decision rules for single sources in MSEDS. It also addresses the construction of rough approximations and analysis of their properties and decision-making rules in MSEDS. An illustrative example in the medical field is investigated to demonstrate the practical application of these developments in event-based decision-making.
TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL
(2023)
Article
Computer Science, Artificial Intelligence
Juanjuan Ding, Chao Zhang, Deyu Li, Arun Kumar Sangaiah
Summary: This article investigates the application of hyperautomation in air quality evaluation (AQE) through a three-way large-scale group decision-making (LSGDM) method in an intuitionistic fuzzy (IF) setting. The proposed method improves information fusion performance, avoids subjective factors in decision results, and provides more explainable decision results.
COGNITIVE COMPUTATION
(2023)
Article
Computer Science, Artificial Intelligence
Chao Zhang, Jingjing Zhang, Wentao Li, Witold Pedrycz, Deyu Li
Summary: Forest fires are a sudden and devastating meteorological disaster that can occur worldwide, causing significant ecological, economic, and social losses. The causes of these disasters are complex and involve uncertain factors such as temperature, relative humidity, wind speed, and rainfall. To efficiently explore forest fire management, a novel model based on regret theory (RT) and multi-granularity (MG) three-way decisions (TWD) in incomplete T-spherical fuzzy (T-SF) environments has been constructed.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Information Systems
Wantong Li, Chao Zhang, Yifan Cui, Jiale Shi
Summary: Air pollution is a significant environmental issue that poses a potential threat to human health. Real-time and continuous measurement of air pollutants in urban environments has become possible with the proliferation of IoT devices and sensors. However, the accuracy and uncertainty of data from multiple sources of IoT sensors present challenges in utilizing and fusing this data effectively. This paper presents a novel MAGDM approach based on hesitant trapezoidal fuzzy information and discusses its application to air quality evaluation.
Article
Computer Science, Artificial Intelligence
Juanjuan Ding, Deyu Li, Chao Zhang, Mingwei Lin
Summary: Liver diseases have become a growing concern in modern times. This paper investigates a three-way group decision scheme with evidential reasoning for liver disease diagnosis. It proposes a multi-granulation incomplete hesitant fuzzy information system and develops an adjustable hesitant fuzzy probability rough set concept. The methodology is demonstrated through a real-life example and experimental analyses.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Information Systems
Yu Wang, Jianming Zhan, Chao Zhang
Summary: This study introduces a three-way decision (TWD) method that incorporates prospect theory (PT) and probabilistic linguistic term sets (PLTSs) to address multi-attribute decision-making (MADM) problems. A novel distance formula is introduced to address the shortcomings of existing distance formulas for PLTSs. The weight calculation is explored from two dimensions, and a novel satisfaction function is formulated to address the limitation of reference point selections in PT.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Chenglong Zhu, Xueling Ma, Chao Zhang, Weiping Ding, Jianming Zhan
Summary: The BPNN model based on GTS performs better in long-term time series forecasting.
INFORMATION SCIENCES
(2023)
Article
Environmental Sciences
Feng Cao, Bing Xing, Jiancheng Luo, Deyu Li, Yuhua Qian, Chao Zhang, Hexiang Bai, Hu Zhang
Summary: The field of remote sensing information processing focuses on object detection in high-spatial-resolution remote sensing images (HSRIs). OD in HSRIs faces challenges like object scale variations, complex backgrounds, dense arrangement, and uncertain orientations. This paper presents an innovative OD algorithm that enhances the YOLOv5 framework with modules like RepConv, Transformer Encoder, BiFPN, C3GAM, SIoU loss function, and circular smooth label method. The algorithm achieves higher detection accuracies on HRSC2016 and UCAS-AOD datasets compared to other OD algorithms for HSRIs.
Article
Computer Science, Information Systems
Jeyabharathy Sadaiyandi, Padmapriya Arumugam, Arun Kumar Sangaiah, Chao Zhang
Summary: Due to imbalanced datasets, classifying and accurately predicting unbalanced data is challenging. This study uses sampling-based machine learning and deep learning approaches to automate the recognition of rotting trees in a forest dataset. The proposed approach successfully predicts the state of decay of trees, achieving a classification accuracy of 91%.
Article
Computer Science, Cybernetics
Chao Zhang, Juanjuan Ding, Jianming Zhan, Arun Kumar Sangaiah, Deyu Li
Summary: The objective of this article is to explore a fuzzy intelligence learning approach based on bounded rationality in IoMT systems for biomedical data analysis. The approach utilizes adjustable multigranulation rough sets and interactive multicriteria decision-making to detect freezing of gait in Parkinson's disease. Experimental analyses on a UCI dataset demonstrate the effectiveness of this approach in diagnosing freezing of gait.
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
(2023)
