Article
Computer Science, Information Systems
Xiaoqing Ye, Dun Liu
Summary: This paper proposes a novel sequential recommendation strategy from the temporal-spatial perspective, which constructs multilevel recommendation information using recurrent neural network and achieves multi-step recommendation through a temporal-spatial three-way recommendation strategy. A temporal-spatial three-way recommendation based on recurrent neural network is further proposed to realize recommendation with lower decision cost.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Wenbin Qian, Yangyang Zhou, Jin Qian, Yinglong Wang
Summary: This paper proposes a cost-sensitive sequential three-way decision model for information systems with fuzzy decision, which achieves better classification performance and lower test costs by optimizing information granularity.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2022)
Article
Chemistry, Multidisciplinary
Xinhui Zhang, Tinghui Ouyang
Summary: This paper proposes an advanced method for forming three-way decision classification rules, which uses information granules and information entropy to describe uncertainty and form fuzzy rules to solve classification problems. Experimental results show that classification rules considering uncertain data perform better in decision-making processes and have an improvement compared to traditional methods.
APPLIED SCIENCES-BASEL
(2022)
Article
Engineering, Mechanical
Xin-Jie Wu, Ming-Da Xu, Chang-Di Li, Chong Ju, Qian Zhao, Shi-Xing Liu
Summary: The paper introduces an electromagnetic tomography (EMT) image reconstruction algorithm based on a Restricted Boltzmann Machine (RBM) autoencoder neural network, achieving higher accuracy in image reconstruction through deep learning.
FLOW MEASUREMENT AND INSTRUMENTATION
(2021)
Article
Computer Science, Information Systems
Faiza Samreen, Gordon S. Blair, Yehia Elkhatib
Summary: This article presents a transfer learning based decision support system that reduces time and cost in building new models for performance of new applications and cloud infrastructures.
IEEE TRANSACTIONS ON CLOUD COMPUTING
(2022)
Article
Computer Science, Information Systems
Musa Adeku Ibrahim, Tamunokuro Opubo William-West
Summary: The decision-theoretic three-way approximation of a fuzzy set F utilizes a three-element set {0, 0.5, 1} to approximate F, using optimum pair of thresholds (alpha, beta). The paper introduces a novel way of determining appropriate values of n, m, and p without the restriction of n = 0 and p = 1, showing the suitability of the {n, m, p} system in minimizing approximation error.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Xianyong Zhang, Yanhong Zhou, Xiao Tang, Yunrui Fan
Summary: This study aims to improve the conditional neighborhood entropy by establishing three-level granular structures and three-way neighborhood entropies. The improved measurement method provides more accurate, hierarchical, systematic, and monotonic measurements. The effectiveness of the method is verified through decision table examples and data set experiments, facilitating uncertainty measurement, information processing, and knowledge discovery.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2022)
Article
Quantum Science & Technology
Chang Yu Hsieh, Qiming Sun, Shengyu Zhang, Chee Kong Lee
Summary: This study presents a novel method to generate complex-valued RBM-NQS, achieving modeling complex-valued wave functions, using minimal ancilla qubits for simulating hidden spins in RBM architecture, and avoiding post-selections to enhance scalability.
NPJ QUANTUM INFORMATION
(2021)
Editorial Material
Multidisciplinary Sciences
Hechen Wang
Summary: With the unsustainable growth of resources needed for artificial intelligence, analog design offers an energy-efficient alternative to digital computer chips that is particularly well-suited for neural network computations.
Article
Computer Science, Artificial Intelligence
Lidong Wang, Xueqin Liu, Yanjun Wang
Summary: This study focuses on achieving consensus in group decision-making problems in complex and uncertain environments through minimum adjustment and minimum cost. A two-stage consensus optimization model is designed, and Pythagorean fuzzy linguistic preference information is used to achieve a balance between expert opinions.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Jindong Qin, Luis Martinez, Witold Pedrycz, Xiaoyu Ma, Yingying Liang
Summary: The management of uncertainty in decision-making problems remains a challenging and timely research issue. Granular Computing, a paradigm for handling higher types of uncertainty in decision analysis, is considered a new asset in decision-making studies. This paper provides a comprehensive overview of Granular Computing for decision-making through literature analysis, highlighting its extensions, applications, and challenges.
INFORMATION FUSION
(2023)
Article
Computer Science, Artificial Intelligence
Wenbin Chen, Qinghua Zhang, Yongyang Dai
Summary: This study proposes a new sequential multi-class three-way decision model by considering the granular structure of the sequential process. The model defines decision cost, calculates attribute sequence, and the experimental results demonstrate its advantage in decision cost.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2022)
Article
Computer Science, Artificial Intelligence
Francesca Elisa Leonelli, Elena Agliari, Linda Albanese, Adriano Barra
Summary: This study leverages the equivalence between RBMs and HNN to propose an effective weight initialization method and applies it in a simple auto-encoder model. Additionally, obtaining larger retrieval regions by applying Gram-Schmidt orthogonalisation to the patterns is demonstrated.
Article
Physics, Multidisciplinary
Fuwen Zhang, Yonggang Tan, Qing-yu Cai
Summary: In this paper, quantum machine learning based on quantum algorithms is used to recognize handwritten number datasets by training the quantum Boltzmann machine (QBM) and comparing the results with classical models. It is found that, when the QBM is semi-restricted, better training results are achieved with fewer computing resources. This highlights the importance of designing targeted algorithms for faster computation and resource conservation.
COMMUNICATIONS IN THEORETICAL PHYSICS
(2022)
Article
Computer Science, Artificial Intelligence
Rami Al-Hmouz, Witold Pedrycz, Abdullah Balamash, Ali Morfeq
Summary: The article presents a logic-oriented approach to dimensionality reduction and introduces the concept of information granularity to quantify the quality of the reduced data. The study focuses on the construction and analysis of logic-oriented autoencoders, proposing a two-level architecture composed of logic processing units. Experimental results are also reported.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Xianhe Wang, Bo Wang, Tiantian Li, Huaxiong Li, Junzo Watada
Summary: In this study, a novel three-way decisions model is proposed based on cumulative prospect theory and outranking relations, which introduces a boundary region and reduces decision risk. Three strategies are proposed to design the model by constructing an outranked set for each alternative and a hybrid multi-criteria decision-making matrix.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Tianxing Wang, Libo Zhang, Bing Huang, Xianzhong Zhou
Summary: Conflict analysis is important for conflict resolution and has received widespread interest. This study focuses on three-way conflict analysis using interval-valued Pythagorean fuzzy information systems and prospect theory. The results show the effectiveness and superiority of this approach compared to other models and methods.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Engineering, Electrical & Electronic
Wenyuan Zhong, Huaxiong Li, Qinghua Hu, Yang Gao, Chunlin Chen
Summary: Deep learning methods have attracted much attention for image classification recently. However, for small-scale data, these methods may not yield optimal results due to the lack of training samples. Sparse representation is efficient and interpretable, but its precision is not competitive. To address this issue, we propose a Multi-Level Cascade Sparse Representation (ML-CSR) learning method that combines the advantages of both deep learning and sparse representation. ML-CSR utilizes a pyramid structure and two core modules, Error-To-Feature (ETF) and Generate-Adaptive-Weight (GAW), to improve precision. Experiments on face databases demonstrate the effectiveness of ML-CSR, and ablation experiments further confirm the benefits of the proposed pyramid structure, ETF, and GAW modules. The code is available at https://github.com/Zhongwenyuan98/ML-CSR.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2023)
Article
Computer Science, Information Systems
Tianxing Wang, Bing Huang, Huaxiong Li, Dun Liu, Hong Yu
Summary: This paper combines prospect theory with regret theory to study the compound risk preference modeling of three-way decision and applies it to conflict analysis. The results show that the proposed three-way decision model based on compounded risk preference can effectively solve software development conflict analysis problem.
INFORMATION SCIENCES
(2023)
Article
Automation & Control Systems
Jiubing Liu, Bing Huang, Huaxiong Li, Xiangzhi Bu, Xianzhong Zhou
Summary: Due to the effectiveness and advantages of interval-valued intuitionistic fuzzy sets (IVIFSs) in evaluating uncertainty and risk, we introduce IVIFSs into loss functions of decision-theoretic rough sets (DTRSs) and propose an optimization-based approach to interval-valued intuitionistic fuzzy three-way decisions. First, based on the classical DTRSs and two previous optimization models, we construct a new concise linear programming model for simultaneously determining the threshold pair. Second, we extend the constructed model via the IVIFSs of loss functions and discuss the relations between these loss functions based on ranking methods. Third, we develop our extended models via two ranking methods and prove the existence and uniqueness of the optimal solution of the model. The advantages of our approach compared to existing methods are demonstrated through an illustrative example.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Computer Science, Information Systems
Wei Lv, Chao Zhang, Huaxiong Li, Bo Wang, Chunlin Chen
Summary: Linear representation based methods are extensively used in image recognition to handle noise, illumination changes, and occlusions. However, existing methods have limitations in handling complex noise variations and often make biased approximations. To address these issues, we propose a nonconvex regularized robust mixed error coding (NRRM) method that models image noise without convex relaxation. Our method accurately captures and alleviates noise's negative impact on recognition. Experimental results on benchmark face image databases demonstrate the superiority of NRRM over state-of-the-art linear representation based methods.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Jiubing Liu, Xinying Guo, Peijia Ren, Libo Zhang, Zhifeng Hao
Summary: This paper proposes a weight-updating-based three-way group decision method in linguistic intuitionistic fuzzy opinions, which evaluates loss functions and achieves consensus through weight updating. The effectiveness of the method is verified through an illustrative example and comparative experiments.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Jiubing Liu, Shilin Hu, Huaxiong Li, Yongjun Liu, Bing Huang, Yuxiang Sun
Summary: This paper proposes an expert-weight-updating-based approach to improve and achieve a group consistency under three-way group decision. It uses fuzzy numbers to evaluate loss functions among experts and establishes a fundamental model. It also utilizes a mapping technique to generate numerical thresholds and employs the normalized Hamming distance to define similarity measure and group consensus index. Through experiments and analysis, the convergence of the algorithm and the effectiveness of the approach are verified.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2023)
Article
Computer Science, Artificial Intelligence
Yunan Lu, Weiwei Li, Huaxiong Li, Xiuyi Jia
Summary: Label distribution learning (LDL) addresses label ambiguity by transforming logical labels into label distributions. We propose a generative label enhancement model that utilizes variational Bayes inference to infer label distributions while preserving label ranking and correlation. Extensive experiments validate the effectiveness of our method.
Article
Computer Science, Artificial Intelligence
Denghao Dong, Minyu Feng, Juergen Kurths, Libo Zhang
Summary: This paper proposes a Fuzzy Large Margin Distribution Machine (FLDM) that combines fuzzy set theory with LDM to handle the credibility of different samples and improve the robustness and performance. The FLDM utilizes a fuzzy membership function based on the distance to the class center to characterize the confidence of each sample. Experiments show the superiority of FLDM in various aspects.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2023)
Article
Computer Science, Information Systems
Yuke Li, Pin Wang, Ching-Yao Chan
Summary: Multi-person action forecasting is a crucial step in video understanding and this paper proposes a novel RElational Spatio-TEmPoral learning (RESTEP) approach to address this challenge. RESTEP combines spatial and temporal information in a single pass through relational reasoning, enabling the simultaneous prediction of actions for all actors in a scene. Experimental results demonstrate that RESTEP outperforms existing methods on multiple datasets.
IEEE TRANSACTIONS ON MULTIMEDIA
(2023)
Article
Computer Science, Artificial Intelligence
Chao Zhang, Huaxiong Li, Yang Gao, Chunlin Chen
Summary: This paper proposes a weakly supervised enhanced semantic-aware hashing (WASH) method that simultaneously estimates label noises and performs enhanced semantic-aware hash learning. WASH employs low-rank and sparse decomposition to alleviate label noises and obtains high-level semantic factors and a semantic correlation matrix. The low-rank semantic factors and multi-modal features are jointly factorized into a common subspace to reduce heterogeneity gaps and enhance the semantic awareness of shared representation.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Huaxiong Li, Chao Zhang, Xiuyi Jia, Yang Gao, Chunlin Chen
Summary: Hashing methods have been widely studied for cross-modal retrieval. However, most existing approaches only focus on preserving semantic similarity and ignore the label information and multi-label correlations. In this article, a new method called Adaptive Label correlation based asymmEtric Cross-modal Hashing (ALECH) is proposed, which decomposes the hash learning into two steps: hash codes learning and hash functions learning. ALECH leverages adaptive label correlations and employs an asymmetric strategy to connect latent feature space and Hamming space to preserve semantic similarity. Experimental results on benchmark datasets demonstrate that ALECH outperforms state-of-the-art cross-hashing methods.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Wentao Fan, Chao Zhang, Huaxiong Li, Xiuyi Jia, Guoyin Wang
Summary: This article proposes a novel semisupervised hashing method called three-stage semisupervised hashing (TS3H), which handles both labeled and unlabeled data. The method decomposes the process into three stages to optimize cost-effectiveness and precision. It achieves efficient and superior performance compared with state-of-the-art methods, as verified by experiments on benchmark databases.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Mingyang Zhong, Weiming Xiong, Dong Li, Kehan Chen, Libo Zhang
Summary: Masked Face Recognition (MFR) is a challenging task, and existing methods fail to accurately represent the uncertainty in masked face images. To address this issue, we propose a novel masked face data uncertainty learning method (MaskDUF), which adaptively adjusts optimization weights and measures sample recognizability, to learn an ideal sample distribution with compact intra-class, discrepant inter-class, and distant noise.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
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)
