Information Sciences最新文献_第2页

Robust sampled-data fuzzy consensus control for nonlinear multi-agent systems with parametric uncertainties 具有参数不确定性的非线性多智能体系统的鲁棒采样数据模糊一致控制

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-07-22 DOI: 10.1016/j.ins.2025.122519

Yong Hoon Jang , Seunghoon Lee , Han Sol Kim

引用次数: 0

ClusterDDPM: An EM clustering framework with Denoising Diffusion Probabilistic Models 基于扩散概率模型去噪的EM聚类框架

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-07-22 DOI: 10.1016/j.ins.2025.122518

Jie Yan, Jing Liu, Zhong-Yuan Zhang

{"title":"ClusterDDPM: An EM clustering framework with Denoising Diffusion Probabilistic Models","authors":"Jie Yan, Jing Liu, Zhong-Yuan Zhang","doi":"10.1016/j.ins.2025.122518","DOIUrl":"10.1016/j.ins.2025.122518","url":null,"abstract":"<div><div>Variational autoencoder (VAE) and generative adversarial networks (GAN) have found widespread applications in clustering and have achieved significant success. However, the potential of these approaches may be limited due to VAE's mediocre generation capability or GAN's well-known instability during adversarial training. In contrast, denoising diffusion probabilistic models (DDPMs) represent a new and promising class of generative models that may unlock fresh dimensions in clustering. In this study, we introduce an innovative expectation-maximization (EM) framework for clustering using DDPMs. In the E-step, we aim to derive a mixture of Gaussian priors for the subsequent M-step. In the M-step, our focus lies in learning clustering-friendly latent representations for the data by employing the conditional DDPM and matching the distribution of latent representations to the mixture of Gaussian priors. We present a rigorous theoretical analysis of the optimization process in the M-step, proving that the optimizations are equivalent to maximizing the lower bound of the Q function within the vanilla EM framework under certain constraints. Comprehensive experiments validate the advantages of the proposed framework, showcasing superior performance in clustering, unsupervised conditional generation and latent representation learning. The code is available at <span><span>https://github.com/Jarvisyan/ClusterDDPM-pytorch</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"720 ","pages":"Article 122518"},"PeriodicalIF":8.1,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144694626","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cross-view alignment and completion for incomplete information multi-view clustering 不完全信息多视图聚类的跨视图对齐和补全

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-07-22 DOI: 10.1016/j.ins.2025.122515

Xinyue Liu, Sai Ma, Guosheng Chen, Jiayu Liu, Linlin Zong

{"title":"Cross-view alignment and completion for incomplete information multi-view clustering","authors":"Xinyue Liu, Sai Ma, Guosheng Chen, Jiayu Liu, Linlin Zong","doi":"10.1016/j.ins.2025.122515","DOIUrl":"10.1016/j.ins.2025.122515","url":null,"abstract":"<div><div>Multi-view clustering seeks to group data instances by exploiting the complementary and comprehensive information from multiple views. Due to the complexities involved in data acquisition and alignment, practical challenges such as the Partially View-unaligned Problem (PVP) and the Partially Sample-missing Problem (PSP) may emerge, and the multi-view problem with incomplete information when both coexist. Current research primarily focuses on solving PVP or PSP individually. Although there is a method that can handle PVP and PSP simultaneously, it relies on traditional Euclidean distance to compute cross-view distance matrices. This separation of matrix computation and network training hinders achieving globally optimal optimization matrices and fails to capture the consistency and complementarity between views adequately. To overcome these challenges, the Cross-View Alignment and Completion for Incomplete Information Multi-View Clustering (CAC-MVC) is proposed. In brief, CAC-MVC integrates a contrastive optimization module to improve the consistency between feature representation and alignment tasks. In addition, a module based on category-level alignment and completion strategy is designed to establish optimal alignment and completion relationships between views. Our experiments on four datasets, alongside comparisons with over 10 state-of-the-art approaches, demonstrate the effectiveness and robustness of CAC-MVC in multi-view clustering with incomplete information.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"720 ","pages":"Article 122515"},"PeriodicalIF":8.1,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144703723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The fusion of hyperparameter candidates for one-class classification problems 一类分类问题的超参数候选融合

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-07-19 DOI: 10.1016/j.ins.2025.122526

Toshitaka Hayashi , Dalibor Cimr , Hamido Fujita , Richard Cimler , Hanan Aljuaid

{"title":"The fusion of hyperparameter candidates for one-class classification problems","authors":"Toshitaka Hayashi , Dalibor Cimr , Hamido Fujita , Richard Cimler , Hanan Aljuaid","doi":"10.1016/j.ins.2025.122526","DOIUrl":"10.1016/j.ins.2025.122526","url":null,"abstract":"<div><div>One-class classification (OCC) is a supervised classification problem where the training data is solely one class. OCC cannot execute hyperparameter tuning because its evaluation requires access to other classes; the algorithm will no longer be OCC if the model is updated after accessing other classes. To address this issue, this paper proposes hyperparameter fusion, which is applicable without the evaluation. The fusion process applies ensemble learning techniques, voting, and stacking into OCC models trained on different hyperparameters. The experiments involve 54 OCC problems from 27 imbalanced learn datasets and 115 hyperparameter candidates. The experiment results show that hyperparameter fusion outperformed the average base learners in the area under the receiver operating characteristic (AUC) score. Moreover, removing the worst base learner can improve the AUC score for the ensemble. The discussion section predicts the worst base learner from correlations of normality rankings created by model outputs. The worst base learner has relatively small ranking correlations to the ensemble model compared to other base learners.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"720 ","pages":"Article 122526"},"PeriodicalIF":8.1,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144679943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fault diagnosis of proton exchange membrane fuel cell using multiple convolutional neural networks with multi-scale attention mechanism 基于多尺度关注机制的多重卷积神经网络质子交换膜燃料电池故障诊断

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-07-19 DOI: 10.1016/j.ins.2025.122524

Zhe Jiang , Bo Yang , Ruyi Zheng , Yitong Hou , Hongbiao Li , Dengke Gao , Zhengxun Guo , Lin Jiang

{"title":"Fault diagnosis of proton exchange membrane fuel cell using multiple convolutional neural networks with multi-scale attention mechanism","authors":"Zhe Jiang , Bo Yang , Ruyi Zheng , Yitong Hou , Hongbiao Li , Dengke Gao , Zhengxun Guo , Lin Jiang","doi":"10.1016/j.ins.2025.122524","DOIUrl":"10.1016/j.ins.2025.122524","url":null,"abstract":"<div><div>To enhance the accuracy and robustness of fault diagnosis in proton exchange membrane fuel cells (PEMFCs), this study proposes a hybrid fault diagnosis model based on stacking ensemble learning. This model integrates multiple convolutional neural networks with a multi-scale attention mechanism (Stacking-MCNN-MSA). In the proposed model, MCNN is utilized to extract data features. A multi-head self-attention (MSA) mechanism is then applied to assign appropriate weights to these features. This process emphasizes critical information while suppressing noise. Subsequently, the MCNN-MSA component acts as the base learner. The predictions from the base learner are fed into the meta-learner to obtain the final fault diagnosis results. The research commences with data preprocessing, which involves crucial steps such as data noise reduction and data expansion. After that, the Stacking-MCNN-MSA model is constructed and evaluated through simulation experiments. Its performance is compared with that of alternative algorithms. The results demonstrate that the proposed model achieves high diagnostic accuracy under both original and noisy data conditions. Notably, after data expansion, the model attains a diagnostic accuracy of 98.67 %. These findings validate the effectiveness of the Stacking-MCNN-MSA model and provide a solid foundation for its practical application in PEMFC fault diagnosis.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"720 ","pages":"Article 122524"},"PeriodicalIF":8.1,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144665499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Retraction notice to “Edge-enabled personalized fitness recommendations and training guidance for athletes with privacy preservation” [Inform. Sci. 707 (2025) 122032] 关于“为保护隐私的运动员提供个性化健身建议和训练指导”的撤回通知[通知]。科学通报。707 (2025)122032]

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-07-19 DOI: 10.1016/j.ins.2025.122465

Yuncheng Li , Cong Li , Fan Wang

引用次数: 0

Nearest neighbor regression for evolutionary dynamic multiobjective optimization 进化动态多目标优化的最近邻回归

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-07-18 DOI: 10.1016/j.ins.2025.122513

Youpeng Deng , Haobo Gao , Yan Zheng , Zhaopeng Meng , Yueyang Hua , Qiangguo Jin , Leilei Cao

{"title":"Nearest neighbor regression for evolutionary dynamic multiobjective optimization","authors":"Youpeng Deng , Haobo Gao , Yan Zheng , Zhaopeng Meng , Yueyang Hua , Qiangguo Jin , Leilei Cao","doi":"10.1016/j.ins.2025.122513","DOIUrl":"10.1016/j.ins.2025.122513","url":null,"abstract":"<div><div>Dynamic multi-objective optimization problems (DMOPs), characterized by time-varying objectives and constraints, demand algorithms capable of rapid adaptation while balancing convergence and diversity. Existing evolutionary algorithms are frequently subjected to significant online training costs due to their utilization of data-driven prediction models. A novel hybrid framework, MOEA/D-KNN, is proposed, integrating K-Nearest Neighbor (KNN) regression with the MOEA/D methodology. Within this framework, historical data is leveraged by KNN to dynamically predict Pareto-optimal solutions, enabling rapid adaptation to environmental changes. Simultaneously, the problem is decomposed by MOEA/D to facilitate an effective search strategy. Comprehensive empirical evaluation was conducted on standard DMOP benchmarks across diverse dynamic scenarios. MOEA/D-KNN is demonstrated to outperform state-of-the-art algorithms, particularly in managing abrupt and frequent environmental changes. Machine learning prediction is successfully bridged with evolutionary optimization through this approach, offering a robust and efficient solution for dynamic multi-objective challenges.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"720 ","pages":"Article 122513"},"PeriodicalIF":8.1,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144679892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advancing mathematical frontiers: A comprehensive study of the foundations of fermatean fuzzy soft linear spaces and its applications in supply chain management 推进数学前沿：fermatean模糊软线性空间基础及其在供应链管理中的应用的综合研究

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-07-16 DOI: 10.1016/j.ins.2025.122506

Muhammad Kamran , Shahzaib Ashraf , Manal E.M. Abdalla , Saara Fatima

{"title":"Advancing mathematical frontiers: A comprehensive study of the foundations of fermatean fuzzy soft linear spaces and its applications in supply chain management","authors":"Muhammad Kamran , Shahzaib Ashraf , Manal E.M. Abdalla , Saara Fatima","doi":"10.1016/j.ins.2025.122506","DOIUrl":"10.1016/j.ins.2025.122506","url":null,"abstract":"<div><div>This research introduces the concept of Fermatean Fuzzy Soft Linear Space (FFSLS) and conducts an in-depth investigation into its various properties and structural characteristics, along with illustration examples. These findings are significant because of their potential for real-life applications across diverse fields like decision-making, supply chain management, etc. Specifically, we define and elucidate the Cartesian product of Fermatean Fuzzy Soft Linear Spaces, Fermatean Fuzzy Soft Subspaces, Fermatean Fuzzy Soft Vectors, and Fermatean Fuzzy Soft Scalars through practical examples. Furthermore, we establish fundamental theorems relating to FFSLS, which contribute to the theoretical framework and practical utility of this novel mathematical construct. Understanding FFSLS and its associated components has improved theoretical comprehension and created more opportunities for real-world applications in a variety of practical situations, ranging from decision-making to engineering. We also built a machine learning based algorithms that used for better indicator performance. Therefore, this research serves as a fundamental contribution to the field of FFSLS, offering practical solutions to real-world problems. We also provide a real-life example at the end of the study that simplifies the decision process in a very easy and quick way.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"720 ","pages":"Article 122506"},"PeriodicalIF":8.1,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144653829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Inferring normality from noised samples: Enhanced deep autoencoder with image denoising for anomaly detection 从噪声样本中推断正态性：用于异常检测的增强深度自编码器与图像去噪

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-07-16 DOI: 10.1016/j.ins.2025.122503

Dong Liang , Xinbo Gao , Wen Lu , Jie Li

{"title":"Inferring normality from noised samples: Enhanced deep autoencoder with image denoising for anomaly detection","authors":"Dong Liang , Xinbo Gao , Wen Lu , Jie Li","doi":"10.1016/j.ins.2025.122503","DOIUrl":"10.1016/j.ins.2025.122503","url":null,"abstract":"<div><div>The goal of image anomaly detection is to detect and localize the patterns that do not meet the normal expectations in an image. The widely used reconstruction-based method typically trains an autoencoder just with normal images and detects abnormal images based on the reconstruction error. It is expected that abnormal images cannot be reconstructed well because they are not included in the training process. However, due to its strong generalization ability, the autoencoder often reconstruct abnormal images quite well, thus reducing the performance of anomaly detection. To address this problem, this paper proposes a novel anomaly detection method based on image reconstruction and image denoising. Specifically, this method enhances the autoencoder to learn robust normality by adding noise to the input images. Besides, we proposed a metric to guide the model training based on the spatial scale of abnormal regions. The experimental results demonstrate that our method achieves competitive performance comparing to the state-of-the-art methods. Moreover, based on its concise architecture, our method achieves high efficiency.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"720 ","pages":"Article 122503"},"PeriodicalIF":8.1,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144653831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An enhanced sparse multiobjective evolutionary algorithm in large-scale multiobjective optimization 大规模多目标优化中的增强稀疏多目标进化算法

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-07-16 DOI: 10.1016/j.ins.2025.122476

Xiaodong Huang , Jian Wang , Kai Zhang , Bin Yuan , Caili Dai , Sergey V. Ablameyko

{"title":"An enhanced sparse multiobjective evolutionary algorithm in large-scale multiobjective optimization","authors":"Xiaodong Huang , Jian Wang , Kai Zhang , Bin Yuan , Caili Dai , Sergey V. Ablameyko","doi":"10.1016/j.ins.2025.122476","DOIUrl":"10.1016/j.ins.2025.122476","url":null,"abstract":"<div><div>In recent years, sparse large-scale multiobjective optimization problems (LSMOPs) have found widespread application in real-world scenarios and have become a focus of evolutionary computing research. Due to the high dimensionality of decision variables in LSMOPs, evolutionary algorithms (EAs) often struggle to efficiently find optimal solutions. In an effort to settle this difficulty, we raise an enhanced sparse multiobjective evolutionary algorithm (ESMOEA) that uses the strongly convex sparse (SCSparse) operator to optimize the decision variables, which can further enhance the sparsity of solutions. Additionally, to consider the sparsity property of solutions during variable grouping, the parameter in the sparse operator that represents whether the solution becomes sparse is ingeniously incorporated into the proposed sparse grouping technique. To evaluate the performance of the proposed ESMOEA, a set of experiments is carried out on both benchmark and real-world problems. The experimental results indicate that the proposed ESMOEA achieves superior performance compared to existing large-scale multiobjective evolutionary algorithms (MOEAs).</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"720 ","pages":"Article 122476"},"PeriodicalIF":8.1,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144670486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0