Article
Automation & Control Systems
Jiali He, Liangdong Qu, Lijun Chen
Summary: This paper investigates the fuzzy granular structure of a fuzzy relation vector and its uncertainty measurement. It defines the fuzzy granular structure using fuzzy set matrices, explores the dependence between fuzzy granular structures, and obtains algebraic and lattice features. Furthermore, it discusses the application of fuzzy granular structures in uncertainty measurement for fuzzy relation vectors and conducts an analysis of the proposed measures' effectiveness.
INTERNATIONAL JOURNAL OF FUZZY SYSTEMS
(2022)
Article
Mathematics
Olga Grigorenko, Alexander Sostak
Summary: We propose an alternative approach to the fuzzy metric concept by extending a crisp metric using a fuzzy equivalence relation. We call this extended metric an E-d metric and explore its properties and relationships with classical fuzzy metrics. Specifically, we focus on the topologies and fuzzy topologies induced by E-d metrics.
Article
Computer Science, Information Systems
Adam Marszalek, Tadeusz Burczynski
Summary: This paper explores the concept of ordered fuzzy numbers and fuzzy random variables to develop a method for constructing fuzzy stochastic time series models, which can estimate parameters using classical equations.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Theory & Methods
Marzieh Najariyan, Naser Pariz, Ho Vu
Summary: This paper investigates fuzzy linear singular differential equations under granular differentiability with coefficients and initial conditions as fuzzy numbers. New notions like fuzzy nilpotent matrix, fuzzy linearly independent vectors, fuzzy eigenvectors, rank, index, and fuzzy Jordan canonical form of a fuzzy matrix are introduced. The proposed method is compared with others based on different derivatives using examples.
FUZZY SETS AND SYSTEMS
(2022)
Article
Computer Science, Interdisciplinary Applications
Fangfang Shi, Guoju Ye, Wei Liu, Dafang Zhao
Summary: The aim of this paper is to study nonconvex optimization problems with fuzzy objective functions under the concept of granular differentiability. The paper provides the definition of granular preinvex fuzzy functions and discusses their characteristics. It proves two necessary and sufficient conditions for a granular differentiable fuzzy function to be granular preinvex. The developed theory is applied to solve a class of nonconvex fuzzy optimization problems with constraints, and the existence of the optimal solution is obtained by solving the fuzzy variational inequalities. The theory is illustrated by numerical examples.
MATHEMATICS AND COMPUTERS IN SIMULATION
(2023)
Article
Automation & Control Systems
Razieh Abdollahipour, Khosro Khandani, Aliakbar Jalali
Summary: This article investigates the consensus problem of linear multiagent systems (MASs) with uncertain dynamics and uncertain switching topology. Uncertainties are represented as granular fuzzy numbers using horizontal membership functions and relative distance measurement arithmetic. The existence of a crisp solution to a fuzzy linear matrix inequality problem is first proved, and then an algorithm is proposed for achieving consensus in the MASs with uncertainty. The efficiency of the proposed algorithm is demonstrated in a simulation example.
IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS
(2023)
Article
Automation & Control Systems
Yanhui Zhai, Tao Wang, Deyu Li
Summary: Formal concept analysis is an effective tool for data analysis and visualization using concept lattice. This paper introduces the tolerance threshold to the variable threshold concept lattice, forming the Robust variable threshold fuzzy Concept Lattice (RobCL). RobCL has incremental characteristics and can model the incremental cognitive process, distinguishing it from other concept lattice models. A comparative study shows that variable threshold concept lattice is a special case of RobCL.
INTERNATIONAL JOURNAL OF FUZZY SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Kalle Kaarli, Sandor Radeleczki
Summary: In this paper, the relationships between ordered relations, tolerances, and formal contexts on complete lattices are studied. It is shown that the factor lattice of a complete lattice under a complete tolerance is isomorphic to the concept lattice of a context, and any ordered relation on a complete sublattice can be extended to the whole lattice. Moreover, the self-bonds of a formal context and the ordered relations on its concept lattice are proven to form isomorphic lattices.
JOURNAL OF MULTIPLE-VALUED LOGIC AND SOFT COMPUTING
(2021)
Article
Automation & Control Systems
Dan Wang, Xiubin Zhu, Witold Pedycz, Zhenhua Yu, Zhiwu Li
Summary: This study aims to develop a framework for granular fuzzy relation equations, with the goal of improving the performance of solutions by selecting an optimal subset of fuzzy rules.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2021)
Article
Computer Science, Information Systems
M. Eugenia Cornejo, Jesus Medina, Eloisa Ramirez-Poussa, Clemente Rubio-Manzano
Summary: This paper studies an inference method related to the qualitative preference levels considered in the applications by non-expert users of the FCA framework.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Theory & Methods
David Lobo, Victor Lopez-Marchante, Jesus Medina
Summary: This paper explores the relationship between Fuzzy Relation Equations (FRE) and Concept Lattices, proposing a method to reduce FRE without losing information. It considers attribute reduction theory in property-oriented and object-oriented concept lattices to detect redundant equations. As a result, the computation of solvable FRE's solution set is reduced, and a novel method for computing approximate solutions of unsolvable FRE is introduced.
FUZZY SETS AND SYSTEMS
(2023)
Article
Mathematics, Applied
M. Eugenia Cornejo, Juan Carlos Diaz-Moreno, Jesus Medina
Summary: Datasets often contain imprecise data or noise, which can lead to unexpected results in mappings. One way to minimize the impact of noise on the final results is to use generalized quantifiers. This paper presents four types of generalized quantifiers based on adjoint triples, which offer a more flexible framework compared to current approaches. The properties and characteristics of these quantifiers are studied, and their application in formal concept analysis is demonstrated.
JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS
(2022)
Article
Computer Science, Artificial Intelligence
Prem Kumar Singh
Summary: The study of bipolar fuzzy concept lattice is important for analyzing uncertainty in soft data sets and can help in making more informed decisions. By measuring randomness in bipolar fuzzy concepts using Shannon entropy and determining importance through computed weights, the study aims to provide valuable insights for decision-making processes.
Article
Automation & Control Systems
Anil Kumar, P. S. V. S. Sai Prasad
Summary: This paper proposes a method to increase the scalability of fuzzy rough set (FRS) reduct computation. By utilizing fuzzy min-max neural network (FMNN) preprocessing, combined with extended overlapping criteria and the construction of crisp discernibility matrix (DM), reduct can be effectively computed on large datasets, reducing computational time.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
D. Preethi, J. Vimala
Summary: This paper introduces the concept of homomorphism on fuzzy hyperlattice ordered group (FHLOG) and studies the transformation of binary and fuzzy hyperoperations among different FHLOGs. It also defines the notion of fuzzy hypercongruence relation on FHLOG. By establishing redox reactions of copper, gold, and americium, the paper develops homomorphism and composition functions of FHLOGs. Therefore, a relation among three different metal's redox reactions, preserving binary and fuzzy hyperoperations, is established.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Wei-Zhi Wu, Ming-Wen Shao, Xia Wang
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2019)
Article
Computer Science, Artificial Intelligence
Anhui Tan, Wei-Zhi Wu, Yuhua Qian, Jiye Liang, Jinkun Chen, Jinjin Li
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2019)
Article
Computer Science, Information Systems
Bing Huang, Wei-Zhi Wu, Jinjiang Yan, Huaxiong Li, Xianzhong Zhou
INFORMATION SCIENCES
(2020)
Article
Computer Science, Interdisciplinary Applications
Tianjun Wu, Wen Dong, Jiancheng Luo, Yingwei Sun, Qiting Huang, Weizhi Wu, Xiaodong Hu
EARTH SCIENCE INFORMATICS
(2019)
Article
Computer Science, Artificial Intelligence
Anhui Tan, Wei-Zhi Wu, Suwei Shia, Shimei Zhao
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2019)
Article
Computer Science, Interdisciplinary Applications
Kai Zhang, Jianming Zhan, Weizhi Wu, Jose Carlos R. Alcantud
COMPUTERS & INDUSTRIAL ENGINEERING
(2019)
Article
Computer Science, Artificial Intelligence
Ningxin Xie, Zhaowen Li, Wei-Zhi Wu, Gangqiang Zhang
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2019)
Article
Computer Science, Artificial Intelligence
Ming-Wen Shao, Wei-Zhi Wu, Chang-Zhong Wang
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2019)
Article
Computer Science, Artificial Intelligence
Han Bao, Wei-Zhi Wu, Jia-Wen Zheng, Tong-Jun Li
Summary: This paper explores the concept of entropy and its application in selecting optimal scale combinations in hierarchical data sets. It examines the relationship between entropy optimal scale combinations and classical optimal scale combinations, showing their equivalence in certain scenarios. The study ultimately verifies the effectiveness of entropy in maintaining uncertain measures of knowledge in multi-scale information tables.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2021)
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
Automation & Control Systems
Anhui Tan, Suwei Shi, Wei-Zhi Wu, Jinjin Li, Witold Pedrycz
Summary: This article examines the application of granular structures of intuitionistic fuzzy information in data mining and information processing. It defines partial-order relations at different hierarchical levels to reveal the granularity of the structures, characterizes the granularity invariance between different structures using relational mappings, and generalizes Shannon's entropies to IF entropies. The significance of intuitionistic attributes using the information measures is introduced, and an information-preserving algorithm for data reduction of IF information systems is constructed. Numerical experiments confirm the performance of the proposed technique by inducing substantial IF relations from public datasets considering the similarity/diversity between samples from the same/different classes.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Computer Science, Artificial Intelligence
Jia-Wen Zheng, Wei-Zhi Wu, Han Bao, An-Hui Tan
Summary: This paper investigates the optimal scale selection for multi-scale ordered decision systems based on evidence theory and clarifies relationships among different types of optimal scales.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2022)
Article
Computer Science, Artificial Intelligence
Jinbo Wang, Wei-Zhi Wu, Anhui Tan
Summary: This study investigates knowledge discovery in incomplete generalized multi-scale decision systems based on multi-granulation rough sets. It discusses the multi-granulation structures, defines pessimistic and optimistic optimal scale combinations, and explores reducts of scale combinations. Numerical algorithms are designed for finding optimal scale combinations.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2022)
Article
Computer Science, Artificial Intelligence
Yu Sun, Wei-Zhi Wu, Xia Wang
Summary: This paper investigates the problem of selecting the optimal scale in numerical incomplete multi-scale information systems (NIMIS) and numerical incomplete multi-scale decision systems (NIMDS). By employing the maximal consistent block technique, the authors define the scale in NIMIS and NIMDS and propose the maximal consistent block based optimal scale. The results show that the maximal consistent block based optimal scale and the optimal scale are equivalent in consistent NIMDS, while there is no static relationship between the maximal consistent block based lower-approximation optimal scale and the upper-approximation optimal scale in inconsistent NIMDS.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2023)
Article
Computer Science, Artificial Intelligence
Wei-Zhi Wu, Dongran Niu, Jinhai Li, Tong -Jun Li
Summary: This paper investigates knowledge acquisition in multi-scale information systems by deriving IF-THEN rules for multi-scale decision attributes. It introduces the concept of a generalized multi-scale decision information table (GMDIT) and defines scale selections for individual decision tables in GMDITs. The paper also describes information granules and their properties with different scale selections, formulates optimal scale selections for inconsistent GMDITs, and presents local optimal scale selections for obtaining concise decision rules. Finally, attribute reducts based on optimal scale selections are derived, and hidden decision rules in inconsistent GMDITs are unraveled.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2023)
Article
Computer Science, Artificial Intelligence
Hao Yang, Min Wang, Zhengfei Yu, Hang Zhang, Jinshen Jiang, Yun Zhou
Summary: In this paper, a novel method called CSTTA is proposed for test time adaptation (TTA), which utilizes confidence-based optimization and sample reweighting to better utilize sample information. Extensive experiments demonstrate the effectiveness of the proposed method.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Jin Liu, Ju-Sheng Mi, Dong-Yun Niu
Summary: This article focuses on a novel method for generating a canonical basis for decision implications based on object-induced operators (OE operators). The logic of decision implication based on OE operators is described, and a method for obtaining the canonical basis for decision implications is given. The completeness, nonredundancy, and optimality of the canonical basis are proven. Additionally, a method for generating true premises based on OE operators is proposed.
KNOWLEDGE-BASED SYSTEMS
(2024)
Review
Computer Science, Artificial Intelligence
Kun Bu, Yuanchao Liu, Xiaolong Ju
Summary: This paper discusses the importance of sentiment analysis and pre-trained models in natural language processing, and explores the application of prompt learning. The research shows that prompt learning is more suitable for sentiment analysis tasks and can achieve good performance.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Xiangjun Cai, Dagang Li
Summary: This paper presents a new decomposition mechanism based on learned decomposition mapping. By using a neural network to learn the relationship between original time series and decomposed results, the repetitive computation overhead during rolling decomposition is relieved. Additionally, extended mapping and partial decomposition methods are proposed to alleviate boundary effects on prediction performance. Comparative studies demonstrate that the proposed method outperforms existing RDEMs in terms of operation speed and prediction accuracy.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Xu Wu, Yang Liu, Jie Tian, Yuanpeng Li
Summary: This paper proposes a blockchain-based privacy-preserving trust management architecture, which adopts federated learning to train task-specific trust models and utilizes differential privacy to protect device privacy. In addition, a game theory-based incentive mechanism and a parallel consensus protocol are proposed to improve the accuracy of trust computing and the efficiency of consensus.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Zaiyang Yu, Prayag Tiwari, Luyang Hou, Lusi Li, Weijun Li, Limin Jiang, Xin Ning
Summary: This study introduces a 3D view-based approach that effectively handles occlusions and leverages the geometric information of 3D objects. The proposed method achieves state-of-the-art results on occluded ReID tasks and exhibits competitive performance on holistic ReID tasks.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Yongliang Shi, Runyi Yang, Zirui Wu, Pengfei Li, Caiyun Liu, Hao Zhao, Guyue Zhou
Summary: Neural implicit representations have gained attention due to their expressive, continuous, and compact properties. However, there is still a lack of research on city-scale continual implicit dense mapping based on sparse LiDAR input. In this study, a city-scale continual neural mapping system with a panoptic representation is developed, incorporating environment-level and instance-level modeling. A tailored three-layer sampling strategy and category-specific prior are proposed to address the challenges of representing geometric information in city-scale space and achieving high fidelity mapping of instances under incomplete observation.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Ruihan Hu, Zhi-Ri Tang, Rui Yang, Zhongjie Wang
Summary: Mesh data is crucial for 3D computer vision applications worldwide, but traditional deep learning frameworks have struggled with handling meshes. This paper proposes MDSSN, a simple mesh computation framework that models triangle meshes and represents their shape using face-based and edge-based Riemannian graphs. The framework incorporates end-to-end operators inspired by traditional deep learning frameworks, and includes dedicated modules for addressing challenges in mesh classification and segmentation tasks. Experimental results demonstrate that MDSSN outperforms other state-of-the-art approaches.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Buliao Huang, Yunhui Zhu, Muhammad Usman, Huanhuan Chen
Summary: This paper proposes a novel semi-supervised conditional normalizing flow (SSCFlow) algorithm that combines unsupervised imputation and supervised classification. By estimating the conditional distribution of incomplete instances, SSCFlow facilitates imputation and classification simultaneously, addressing the issue of separated tasks ignoring data distribution and label information in traditional methods.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Deeksha Varshney, Asif Ekbal, Erik Cambria
Summary: This paper focuses on the neural-based interactive dialogue system that aims to engage and retain humans in long-lasting conversations. It proposes a new neural generative model that combines step-wise co-attention, self-attention-based transformer network, and an emotion classifier to control emotion and knowledge transfer during response generation. The results from quantitative, qualitative, and human evaluation show that the proposed models can generate natural and coherent sentences, capturing essential facts with significant improvement over emotional content.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Junchen Ye, Weimiao Li, Zhixin Zhang, Tongyu Zhu, Leilei Sun, Bowen Du
Summary: Modeling multivariate time series has long been a topic of interest for scholars in various fields. This paper introduces MvTS, an open library based on Pytorch, which provides a unified framework for implementing and evaluating these models. Extensive experiments on public datasets demonstrate the effectiveness and universality of the models reproduced by MvTS.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Reham R. Mostafa, Ahmed M. Khedr, Zaher Al Aghbari, Imad Afyouni, Ibrahim Kamel, Naveed Ahmed
Summary: Feature selection is crucial in classification procedures, but it faces challenges in high-dimensional datasets. To overcome these challenges, this study proposes an Adaptive Hybrid-Mutated Differential Evolution method that incorporates the mechanics of the Spider Wasp Optimization algorithm and the concept of Enhanced Solution Quality. Experimental results demonstrate the effectiveness of the method in terms of accuracy and convergence speed, and it outperforms contemporary cutting-edge algorithms.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Ti Xiang, Pin Lv, Liguo Sun, Yipu Yang, Jiuwu Hao
Summary: This paper introduces a Track Classification Model (TCM) based on marine radar, which can effectively recognize and classify shipping tracks. By using a feature extraction network with multi-feature fusion and a dataset production method to address missing labels, the classification accuracy is improved, resulting in successful engineering application in real scenarios.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Zhihao Zhang, Yuan Zuo, Chenghua Lin, Junjie Wu
Summary: This paper proposes a novel unsupervised context-aware quality phrase mining framework called LMPhrase, which is built upon large pre-trained language models. The framework mines quality phrases as silver labels using a parameter-free probing technique on the pre-trained language model BERT, and formalizes the phrase tagging task as a sequence generation problem by fine-tuning on the Sequence to-Sequence pre-trained language model BART. The results of extensive experiments show that LMPhrase consistently outperforms existing competitors in two different granularity phrase mining tasks.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Kemal Buyukkaya, M. Ozan Karsavuran, Cevdet Aykanat
Summary: The study aims to investigate the hybrid parallelization of the Stochastic Gradient Descent (SGD) algorithm for solving the matrix completion problem on a high-performance computing platform. A hybrid parallel decentralized SGD framework with asynchronous inter-process communication and a novel flexible partitioning scheme is proposed to achieve scalability up to hundreds of processors. Experimental results on real-world benchmark datasets show that the proposed algorithm achieves 6x higher throughput on sparse datasets compared to the state-of-the-art, while achieving comparable throughput on relatively dense datasets.
KNOWLEDGE-BASED SYSTEMS
(2024)
