Article
Computer Science, Artificial Intelligence
Saleem Abdullah, Mohammed M. Al-Shomrani, Peide Liu, Sheraz Ahmad
Summary: The study developed a new technique based on decision-theoretical rough sets (DTRSs) for three-way decision-making problems, introducing fractional fuzzy sets (FFS) and their operations. Innovations were made in the loss function and a new form of decision technique was proposed based on DTRSs. Results showed that the proposed three-way decision-making models are more accurate compared to particular fuzzy sets.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Kainan Guan, Guang Yang, Liang Du, Zhengguang Li, Xinhua Yang
Summary: This paper proposes a synergistic approach of fusion model and interpretation analysis for optimizing knowledge construction and decision-making rules. The effectiveness of the method is validated and analyzed based on real engineering data, showing a high degree of consistency with actual engineering knowledge.
JOURNAL OF INTELLIGENT MANUFACTURING
(2023)
Article
Mathematics, Applied
Sisi Xia, Lin Chen, Haoran Yang
Summary: This paper introduces the concept of neighborhood soft set and its corresponding decision system to address decision-making problems with heterogeneous information. By defining neighborhood soft set and related operations, as well as decision system and core attribute, decision rules are derived for optimal decision-making. Finally, an algorithm based on the neighborhood soft set is presented and applied in medical diagnosis, with a comparison analysis conducted against other decision-making methods.
Article
Computer Science, Artificial Intelligence
Wei Li, Bin Yang
Summary: As a generalization of equivalent class, fuzzy covering makes descriptions of the objective world more realistic, practical and accurate in some cases. In this paper, we establish four kinds of fuzzy probabilistic covering-based rough set, which combine the fuzzy covering-based neighborhood operator and the fuzzy probabilistic rough set, and then obtain the result of their three-way decision.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Information Systems
Gongao Qi, Bin Yang, Wei Li
Summary: In this paper, four types of fuzzy neighborhood operators based on fuzzy covering and their implicators are proposed. The equalities among overlap function-based fuzzy neighborhood operators on a finite fuzzy covering are investigated. The operators are divided into seventeen groups according to equivalence relations, and the partial order relations among them are discussed. Two types of neighborhood-related fuzzy covering-based rough set models are proposed, and the groups and partial order relations are also discussed. A novel fuzzy TOPSIS methodology is applied to solve a biosynthetic nanomaterials select issue, and its rationality and enforceability are verified by comparing with nine different methods.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Kai Zhang, Jianming Zhan, Wei-Zhi Wu
Summary: This article introduces a novel fuzzy alpha-neighborhood operator and a fuzzy rough set model based on this operator for decision-making in information systems. By utilizing data normalization and the fuzzy alpha-neighborhood-based fuzzy rough set model, real-valued information systems are effectively transformed into intuitionistic fuzzy-valued information systems, with three different sorting decision-making schemes developed on the latter. The method is validated through numerical experiments and comparative studies, demonstrating its stability and effectiveness.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Bin Yu, Zeshui Xu, Jianhua Dai, Tian Yang
Summary: A new multi-attribute decision-making method, named Disadvantage and Advantage Neighborhood Environments Comparison, is proposed to reduce the influence of attribute weight on the decision-making results. The method determines the ranking of objects by analyzing the ratio of advantage neighborhood sets to disadvantage neighborhood sets. The advantages and disadvantages of the objects are considered to overcome the influence of attribute weight on the decision-making results.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Mathematics
Fernando Chacon-Gomez, M. Eugenia Cornejo, Jesus Medina
Summary: This paper investigates different methods for classifying new objects using decision rules in decision-making processes. These methods determine the best possible decision based on various indicators associated with the decision rules.
Article
Computer Science, Artificial Intelligence
Jin Ye, Bingzhen Sun, Xiaoli Chu, Jianming Zhan, Jianxiong Cai
Summary: In this paper, we propose a new method to effectively integrate multiple decision preferences of decision makers in heterogeneous multi-attribute group decision-making (MAGDM) problems. Utilizing the framework of granular computing and the valued outranking relations in the ELECTRE III method, we define a special valued outranking relation on a heterogeneous attribute multi-decision information system and introduce the notion of valued outranking classes. We also construct multi-decision multigranulation probabilistic rough sets (MD-MGPRSs) from different perspectives, establish a novel MAGDM method, and provide evidence of its effectiveness, robustness, and superiority through experiments on seven datasets.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Juncheng Bai, Bingzhen Sun, Xiaoli Chu, Ting Wang, Hongtao Li, Qingchun Huang
Summary: This paper introduces a new multi-attribute prediction approach based on neighborhood rough set and multivariate variational mode decomposition to improve the accuracy of disease prediction. Experimental results show that the proposed method has high accuracy and stability, and can provide a new quantitative theory and method for chronic disease management decision-making in medical decision-making.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Information Systems
Kai Zhang, Jianhua Dai
Summary: In this paper, the concept of fuzzy fl-covering group approximation spaces and a three-way multi-criteria group decision-making method are proposed to solve ranking and classification problems in a group decision-making environment. The method employs fuzzy fitting neighborhoods and an overall loss function to meet the preferences of decision makers. Numerical and experimental analysis demonstrate the feasibility and superiority of the proposed method.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Akin Osman Atagun, Huseyin Kamaci
Summary: In this paper, two new uncertainty modeling concepts, strait soft set and strait rough set, are introduced, which bridge the gap between rough sets and soft sets and provide new perspectives for both theoretical and practical aspects. The reduction method of alternatives using strait soft sets is presented, and the fusion of parameters in soft set operations is defined and demonstrated. Strait rough sets retain the characteristics of rough sets and allow the characterization of parameters. The effectiveness of strait soft sets and strait rough sets is supported by numerous examples and comparisons. Additionally, a new decision-making approach based on these concepts is proposed and applied to real-life scenarios to illustrate the computational processes.
Article
Computer Science, Artificial Intelligence
Jin Ye, Bingzhen Sun, Xiaoli Chu, Jianming Zhan, Qiang Bao, Jianxiong Cai
Summary: This article discusses the problem of multisource heterogeneous fuzzy decision making and proposes a novel group decision making method based on multigranulation rough sets. A weighted multigranulation generalized fuzzy rough set model is constructed to model the multisource heterogeneous fuzzy decision systems, and extended entropy weight methods are used to calculate the weights of attributes and experts. Finally, the method is applied to the management decision-making problem of gout diagnosis, and comparative and sensitivity analyses demonstrate the feasibility, effectiveness, stability, and superiority of the method.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Jilin Yang, Xianyong Zhang, Keyun Qin
Summary: The paper explores the combination of fuzzy rough sets and three-way decisions, constructing robust fuzzy rough set models from a three-way decision perspective. Experimental results demonstrate the better performance in terms of robustness of the improved model based on three-way approximations.
COGNITIVE COMPUTATION
(2022)
Article
Automation & Control Systems
Akin Osman Atagun, Huseyin Kamaci
Summary: This paper introduces a new uncertainty modelling concept called strait fuzzy set, which brings new perspectives to fuzzy mathematics. Strait fuzzy sets allow objects/points to be graded with fuzzy membership intervals instead of exact values. The paper also studies the basic operations and properties of strait fuzzy sets, introduces the concept of strait fuzzy rough set, and proposes similarity approaches for measuring similarity rates of vaccines against influenza viruses.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
(2023)
Article
Engineering, Multidisciplinary
Yan Zhang, Yongqiang Liu, Yang Liu
MATHEMATICAL PROBLEMS IN ENGINEERING
(2018)
Article
Engineering, Multidisciplinary
Zhang Yan, Liu Yongqiang, Liu Yang
MATHEMATICAL PROBLEMS IN ENGINEERING
(2018)
Article
Engineering, Multidisciplinary
Yan Zhang, Xiaoli Chu, Yang Liu, Yongqiang Liu
MATHEMATICAL PROBLEMS IN ENGINEERING
(2019)
Article
Engineering, Multidisciplinary
Lili Mo, Yongqiang Liu, Yan Zhang
MATHEMATICAL PROBLEMS IN ENGINEERING
(2019)
Article
Computer Science, Information Systems
Yan Zhang, Jingxin Zhang, Jielong Chen, Yufeng Tang, Qianhuo Tian, Yang Liu
Summary: In this paper, the optimal management of university laboratories using MLD system is discussed, showing its ideal applicability through cost function establishment and random student number for energy-saving management. Simulation analysis demonstrates the hybrid dynamic characteristics and optimal management effect of the MLD system in laboratory optimization.
Article
Automation & Control Systems
Yan Zhang, Xiaoli Chu, Yongqiang Liu
JOURNAL OF CONTROL SCIENCE AND ENGINEERING
(2018)
Article
Computer Science, Information Systems
Xia Liang, Jie Guo, Peide Liu
Summary: This paper investigates a novel consensus model based on social networks to manage manipulative and overconfident behaviors in large-scale group decision-making. By proposing a novel clustering model and improved methods, the consensus reaching is effectively facilitated. The feedback mechanism and management approach are employed to handle decision makers' behaviors. Simulation experiments and comparative analysis demonstrate the effectiveness of the model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiang Li, Haiwang Guo, Xinyang Deng, Wen Jiang
Summary: This paper proposes a method based on class gradient networks for generating high-quality adversarial samples. By introducing a high-level class gradient matrix and combining classification loss and perturbation loss, the method demonstrates superiority in the transferability of adversarial samples on targeted attacks.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Lingyun Lu, Bang Wang, Zizhuo Zhang, Shenghao Liu
Summary: Many recommendation algorithms only rely on implicit feedbacks due to privacy concerns. However, the encoding of interaction types is often ignored. This paper proposes a relation-aware neural model that classifies implicit feedbacks by encoding edges, thereby enhancing recommendation performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jaehong Yu, Hyungrok Do
Summary: This study discusses unsupervised anomaly detection using one-class classification, which determines whether a new instance belongs to the target class by constructing a decision boundary. The proposed method uses a proximity-based density description and a regularized reconstruction algorithm to overcome the limitations of existing one-class classification methods. Experimental results demonstrate the superior performance of the proposed algorithm.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Hui Tu, Shifei Ding, Xiao Xu, Haiwei Hou, Chao Li, Ling Ding
Summary: Border-Peeling algorithm is a density-based clustering algorithm, but its complexity and issues on unbalanced datasets restrict its application. This paper proposes a non-iterative border-peeling clustering algorithm, which improves the clustering performance by distinguishing and associating core points and border points.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Long Tang, Pan Zhao, Zhigeng Pan, Xingxing Duan, Panos M. Pardalos
Summary: In this work, a two-stage denoising framework (TSDF) is proposed for zero-shot learning (ZSL) to address the issue of noisy labels. The framework includes a tailored loss function to remove suspected noisy-label instances and a ramp-style loss function to reduce the negative impact of remaining noisy labels. In addition, a dynamic screening strategy (DSS) is developed to efficiently handle the nonconvexity of the ramp-style loss.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Raghunathan Krishankumar, Sundararajan Dhruva, Kattur S. Ravichandran, Samarjit Kar
Summary: Health 4.0 is gaining global attention for better healthcare through digital technologies. This study proposes a new decision-making framework for selecting viable blockchain service providers in the Internet of Medical Things (IoMT). The framework addresses the limitations in previous studies and demonstrates its applicability in the Indian healthcare sector. The results show the top ranking BSPs, the importance of various criteria, and the effectiveness of the developed model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Tao Tan, Hong Xie, Liang Feng
Summary: This paper proposes a heterogeneous update idea and designs HetUp Q-learning algorithm to enlarge the normalized gap by overestimating the Q-value corresponding to the optimal action and underestimating the Q-value corresponding to the other actions. To address the limitation, a softmax strategy is applied to estimate the optimal action, resulting in HetUpSoft Q-learning and HetUpSoft DQN. Extensive experimental results show significant improvements over SOTA baselines.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Chao Yang, Xianzhi Wang, Lina Yao, Guodong Long, Guandong Xu
Summary: This paper proposes a dynamic transformer-based architecture called Dyformer for multivariate time series classification. Dyformer captures multi-scale features through hierarchical pooling and adaptive learning strategies, and improves model performance by introducing feature-map-wise attention mechanisms and a joint loss function.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiguang Li, Baolu Feng, Yunhe Sun, Ammar Hawbani, Saeed Hammod Alsamhi, Liang Zhao
Summary: This paper proposes an enhanced scatter search strategy, using opposition-based learning, to solve the problem of automated test case generation based on path coverage (ATCG-PC). The proposed ESSENT algorithm selects the path with the lowest path entropy among the uncovered paths as the target path and generates new test cases to cover the target path by modifying the dimensions of existing test cases. Experimental results show that the ESSENT algorithm outperforms other state-of-the-art algorithms, achieving maximum path coverage with fewer test cases.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Shirin Dabbaghi Varnosfaderani, Piotr Kasprzak, Aytaj Badirova, Ralph Krimmel, Christof Pohl, Ramin Yahyapour
Summary: Linking digital accounts belonging to the same user is crucial for security, user satisfaction, and next-generation service development. However, research on account linkage is mainly focused on social networks, and there is a lack of studies in other domains. To address this, we propose SmartSSO, a framework that automates the account linkage process by analyzing user routines and behavior during login processes. Our experiments on a large dataset show that SmartSSO achieves over 98% accuracy in hit-precision.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Renchao Wu, Jianjun He, Xin Li, Zuguo Chen
Summary: This paper proposes a memetic algorithm with fuzzy-based population control (MA-FPC) to solve the joint order batching and picker routing problem (JOBPRP). The algorithm incorporates batch exchange crossover and a two-level local improvement procedure. Experimental results show that MA-FPC outperforms existing algorithms in terms of solution quality.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Guoxiang Zhong, Fagui Liu, Jun Jiang, Bin Wang, C. L. Philip Chen
Summary: In this study, we propose the AMFormer framework to address the problem of mixed normal and anomaly samples in deep unsupervised time-series anomaly detection. By refining the one-class representation and introducing the masked operation mechanism and cost sensitive learning theory, our approach significantly improves anomaly detection performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jin Zhou, Kang Zhou, Gexiang Zhang, Ferrante Neri, Wangyang Shen, Weiping Jin
Summary: In this paper, the authors focus on the issue of multi-objective optimisation problems with redundant variables and indefinite objective functions (MOPRVIF) in practical problem-solving. They propose a dual data-driven method for solving this problem, which consists of eliminating redundant variables, constructing objective functions, selecting evolution operators, and using a multi-objective evolutionary algorithm. The experiments conducted on two different problem domains demonstrate the effectiveness, practicality, and scalability of the proposed method.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Georgios Charizanos, Haydar Demirhan, Duygu Icen
Summary: This article proposes a new fuzzy logistic regression framework that addresses the problems of separation and imbalance while maintaining the interpretability of classical logistic regression. By fuzzifying binary variables and classifying subjects based on a fuzzy threshold, the framework demonstrates superior performance on imbalanced datasets.
INFORMATION SCIENCES
(2024)
