Information Sciences最新文献_第9页

Polytomous knowledge structures constructed by L-fuzzy approximation operators 由l -模糊逼近算子构造的多同构知识结构

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-03-27 DOI: 10.1016/j.ins.2025.122137

Bochi Xu , Jinjin Li , Fugui Shi

{"title":"Polytomous knowledge structures constructed by L-fuzzy approximation operators","authors":"Bochi Xu , Jinjin Li , Fugui Shi","doi":"10.1016/j.ins.2025.122137","DOIUrl":"10.1016/j.ins.2025.122137","url":null,"abstract":"<div><div>Rough set theory is more concerned with the character of the upper and lower approximations of a particular set than with the overall structure. Knowledge space theory can provide another new perspective on rough sets. In this paper, we establish a theoretical linkage between polytomous knowledge structures and <em>L</em>-fuzzy approximation operators. We generate polytomous knowledge structures constructed by <em>L</em>-fuzzy approximation operators and give the corresponding properties, and find that a polytomous knowledge space (polytomous closure space, respectively) and can be completely characterized by an upper (lower, respectively) <em>L</em>-fuzzy approximation. In particular, we discuss the dichotomous knowledge structure by the means of fuzzy approximation operators, which corresponds to the method of fuzzy skill maps. Finally, by <em>L</em>-fuzzy relation constructing two particular dichotomous knowledge structures, which are called backward-graded and forward-graded, is also discussed. This study proposes a framework to analyze <em>L</em>-fuzzy rough sets through knowledge space theory, bridging these mathematical disciplines.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"712 ","pages":"Article 122137"},"PeriodicalIF":8.1,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739392","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

Regional express delivery network planning: A location-routing model and two-tier adaptive GA 区域快递网络规划：一种位置-路径模型和两层自适应遗传算法

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-03-26 DOI: 10.1016/j.ins.2025.122133

Wenfei Li , Yue Xin , Guoqing Yang

{"title":"Regional express delivery network planning: A location-routing model and two-tier adaptive GA","authors":"Wenfei Li , Yue Xin , Guoqing Yang","doi":"10.1016/j.ins.2025.122133","DOIUrl":"10.1016/j.ins.2025.122133","url":null,"abstract":"<div><div>Modern express delivery system has the ability to deliver mail thousands of kilometers away to the rural doorstep within a few days. This ability depends largely on the robustness and efficiency of regional express delivery networks for distributing and collecting packages. This study aims to minimize the total cost of a regional express delivery network by developing a location-routing model with novel structures. Routing depots are considered non-hub nodes within a hub location network, while external interfaces are introduced as gateways for inter-regional mail exchange. To reduce the model scale, a decomposition approach is proposed, which divides the model into a master problem and a cluster of sub-problems. Furthermore, a two-tier adaptive GA is designed for the sub-problems. Numerical experiments simulate the SF Express operation in the main urban area of Beijing. Computational results show that: 1) The decomposition approach effectively addresses a real-scale problem by transforming it into some small-scale ones. 2) The two-tier adaptive GA achieves high effectiveness in tactic and temporal efficiency compared with the branch and bound method. 3) The proposed model is robust in terms of rents, fuel costs, discount factors and the quantity passing through external interfaces.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"712 ","pages":"Article 122133"},"PeriodicalIF":8.1,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748330","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

Knowledge-aware differential equation discovery with automated background knowledge extraction 基于背景知识自动提取的知识感知微分方程发现

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-03-26 DOI: 10.1016/j.ins.2025.122131

Elizaveta Ivanchik, Alexander Hvatov

引用次数: 0

A stable framework-based modeling of the complex dynamical system using a double context layered with self-weighted output feedback loop Elman recurrent neural network 基于稳定框架的复杂动力系统的双上下文分层自加权输出反馈回路Elman递归神经网络建模

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-03-26 DOI: 10.1016/j.ins.2025.122132

Rajesh Kumar

{"title":"A stable framework-based modeling of the complex dynamical system using a double context layered with self-weighted output feedback loop Elman recurrent neural network","authors":"Rajesh Kumar","doi":"10.1016/j.ins.2025.122132","DOIUrl":"10.1016/j.ins.2025.122132","url":null,"abstract":"<div><div>In this paper, a modified structure of the classical Elman recurrent neural network (ERNN) named Double context layered with output self-weighted feedback loop Elman recurrent neural network (DCLOSWFLERNN) is proposed. It consists of two additional components (as compared to the ERNN model): one extra context layer and an adjustable weighted feedback loop in the output layer. This has resulted in the model's ability to approximate the underlying unknown mathematical relationship relating to the input-output data (obtained from any complex dynamical plant). The second emphasis of this paper pertains to the stability component, wherein the Lyapunov stability is utilized to develop a stable Back-propagation (BP) based weight update rule. Lastly, an adjustable learning rate is also suggested, which contributes to improving the learning algorithm's overall performance. The simulation results reveal that the proposed model has given better modeling accuracy as compared to the other considered neural models. This can be observed from the values obtained of the error-based indicators such as Root Mean Square Error (RMSE) and Mean Average Error (MSE). The values of RMSE and MAE obtained from the proposed model during the modeling procedure are 0.0028 and 0.0035 which are the least among the obtained with other neural models.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"712 ","pages":"Article 122132"},"PeriodicalIF":8.1,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143705465","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

Adaptive fuzzy funnel control of nonlinear multi-agent systems via dual-channel event-triggered strategy 基于双通道事件触发策略的非线性多智能体系统自适应模糊漏斗控制

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-03-26 DOI: 10.1016/j.ins.2025.122129

Xiaoyang Liu , Minghao Hui , Lu Fan , Wenwu Yu , Jinde Cao

引用次数: 0

Semantic enhanced bi-syntactic graph convolutional network for aspect-based sentiment analysis 基于方面的情感分析的语义增强双句法图卷积网络

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-03-25 DOI: 10.1016/j.ins.2025.122130

Junyang Xiao , Yun Xue , Fenghuan Li

{"title":"Semantic enhanced bi-syntactic graph convolutional network for aspect-based sentiment analysis","authors":"Junyang Xiao , Yun Xue , Fenghuan Li","doi":"10.1016/j.ins.2025.122130","DOIUrl":"10.1016/j.ins.2025.122130","url":null,"abstract":"<div><div>Previous work on fine-grained sentiment analysis focuses on establishing the semantic correlations between words by means of attention mechanisms. More recently, effects of syntax-based models, applying graph convolution operation over dependency trees, are highlighted due to their superiority. However, these methods still have deficiencies. For one thing, little aspect-specific information is considered during semantic modeling of contextual words, which introduces irrelevant noise toward the aspect. For another, current syntax-based approaches either ignore the syntactic constituent knowledge, or fail to maintain the syntactic information from the reconstructed constituent tree. As such, no relation among words, phrases and clauses is built. In this work, a Semantic Enhanced Bi-Syntax Graph Convolutional Network (SEBS-GCN) is proposed to enhance semantics of context to the aspect, and capture the sentiment relevance among words, phrases and clauses. Specifically, we devise an aspect-aware gated mechanism to obtain the aspect-aware feature, based on the semantic correlations between the specific aspect and its contexts. Furthermore, the syntax information of the constituent tree is sufficiently exploited to analyze the hierarchical structure and the logical relation among words, phrases and clauses, based on which to capture the sentiment clues of the aspect.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"712 ","pages":"Article 122130"},"PeriodicalIF":8.1,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143705463","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

DoS-resilient event-triggering control of connected vehicles: An attack-parameter-dependent functional method 网联车辆dos弹性事件触发控制：一种攻击参数相关的泛函方法

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-03-25 DOI: 10.1016/j.ins.2025.122118

Yihui Xu , Huiyan Zhang , Yongchao Liu , Ning Zhao , Imre J. Rudas

{"title":"DoS-resilient event-triggering control of connected vehicles: An attack-parameter-dependent functional method","authors":"Yihui Xu , Huiyan Zhang , Yongchao Liu , Ning Zhao , Imre J. Rudas","doi":"10.1016/j.ins.2025.122118","DOIUrl":"10.1016/j.ins.2025.122118","url":null,"abstract":"<div><div>This article studies the problem of event-triggering formation tracking control for networked autonomous vehicles in the face of denial-of-service (DoS) attacks. Because of the disruptive nature of DoS attacks on vehicle-to-vehicle communication, the traditional control strategy is difficult to accomplish the task of vehicle formation. To overcome this problem, a resilient distributed dynamic event-triggered control strategy is created to mitigate the impact of attacks on intermittent communication. Meanwhile, to optimize network resource utilization, a novel dynamic event-triggered scheme is designed to significantly reduce the frequency of data updates and transmits in the controller. Subsequently, a novel Lyapunov function is established to rigorously analyze the exponential stability of the vehicle formation system. The function incorporates attack-related time-varying parameters and deals with the artificial delay introduced in the model through an interval correlation function, which significantly reduces conservatism and simplifies the stability analysis process. Finally, the effectiveness of the control strategy is confirmed through a simulation study conducted on the vehicle platoon system.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"712 ","pages":"Article 122118"},"PeriodicalIF":8.1,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143760443","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

DPSN-STHA: A dynamic perception model of similar nodes with spatial-temporal heterogeneity attention for traffic flow forecasting 基于时空异质性的交通流预测相似节点动态感知模型

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-03-25 DOI: 10.1016/j.ins.2025.122126

Jinnan Yang , Wentian Cui , Qing Shen , Jungang Lou

{"title":"DPSN-STHA: A dynamic perception model of similar nodes with spatial-temporal heterogeneity attention for traffic flow forecasting","authors":"Jinnan Yang , Wentian Cui , Qing Shen , Jungang Lou","doi":"10.1016/j.ins.2025.122126","DOIUrl":"10.1016/j.ins.2025.122126","url":null,"abstract":"<div><div>Precisely capturing spatial-temporal feature correlations represents an effective approach for improving the traffic flow prediction performance. However, accurate capturing of spatial-temporal features in traffic systems faces certain challenges, such as long-range correlations and node heterogeneity. To overcome these issues, this paper introduces a novel traffic flow prediction model that incorporates spatial-temporal heterogeneous attention, allowing for dynamic perception of similar nodes. In the proposed model, a filter network dynamic parameter memory generator is used for real-time parameter adjustment, which assigns greater weights to nodes with higher similarity to mitigate spatial-temporal heterogeneity. In addition, a similarity-based node computation method, which uses the Wasserstein distance, is introduced to construct a spatial-temporal association matrix, allowing for the dynamic capturing of long-range correlations between nodes. The model's efficacy is validated through experiments on four publicly available traffic datasets. Results show that the proposed model consistently outperforms the best baselines in predictive accuracy. Furthermore, this study examines factors such as training data size, dimensionality, the number of attention heads, and the threshold of the spatial-temporal association matrix, and includes an ablation study to evaluate the model's overall performance.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"712 ","pages":"Article 122126"},"PeriodicalIF":8.1,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143724334","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

Corrigendum to “Advanced predictive control for GRU and LSTM networks” [Inf. Sci. 616 (2022) 229–254] “GRU和LSTM网络的先进预测控制”的勘误表[j] .科学通报，616(2022)229-254。

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-03-25 DOI: 10.1016/j.ins.2025.122079

Krzysztof Zarzycki, Maciej Ławryńczuk

引用次数: 0

Herbal ingredient-target interaction prediction via multi-modal learning 基于多模态学习的草药成分-靶标相互作用预测

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-03-24 DOI: 10.1016/j.ins.2025.122115

Xudong Liang , Guichuan Lai , Jintong Yu , Tao Lin , Chaochao Wang , Wei Wang

{"title":"Herbal ingredient-target interaction prediction via multi-modal learning","authors":"Xudong Liang , Guichuan Lai , Jintong Yu , Tao Lin , Chaochao Wang , Wei Wang","doi":"10.1016/j.ins.2025.122115","DOIUrl":"10.1016/j.ins.2025.122115","url":null,"abstract":"<div><div>The computational prediction of herbal ingredient-target interactions (ITIs) is essential for understanding the mechanisms of action (MoA) of herbal medicine. However, many existing computational methods have yet to fully utilize the multi-modal knowledge of herbs, and the potential noise in literature-mined ITI data has been overlooked. To address these challenges, we propose Multi-ITI, a multi-modal learning framework to learn molecular biological and network topological features for ingredients and targets from multi-modal herbal data, including ingredient SMILES sequences, target protein sequences, ingredient SMILES sequence similarity, target protein sequence similarity, and ingredient-target interactions. Multi-ITI consists of a biological feature learning module and a heterogeneous graph learning module. The biological feature learning module integrates pre-trained models to build deep feature representations for ingredients and targets, while the heterogeneous graph learning module leverages a heterogeneous graph neural network with dynamic attention mechanisms to capture ingredient-target network interactions and mitigate the impact of noisy connections. Experimental results on three public datasets demonstrate that Multi-ITI outperforms six state-of-the-art methods. Additionally, we validate the effectiveness of Multi-ITI through molecular docking simulations and comparisons with recent studies, further highlighting its superior predictive performance and practical applicability.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"711 ","pages":"Article 122115"},"PeriodicalIF":8.1,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704445","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