Information Sciences最新文献_第5页

MO-DMLHM: Multi-objective dynamic hypergraph modeling for cross-layer community detection in organizational networks 组织网络跨层社区检测的多目标动态超图建模

IF 6.8 1区计算机科学

Information Sciences Pub Date : 2026-05-25 Epub Date: 2026-01-29 DOI: 10.1016/j.ins.2026.123170

You-hong Li , Jian-Qiang Wang , Le Gao , Tian-Yu Wang , Hao-Ming Mo

{"title":"MO-DMLHM: Multi-objective dynamic hypergraph modeling for cross-layer community detection in organizational networks","authors":"You-hong Li , Jian-Qiang Wang , Le Gao , Tian-Yu Wang , Hao-Ming Mo","doi":"10.1016/j.ins.2026.123170","DOIUrl":"10.1016/j.ins.2026.123170","url":null,"abstract":"<div><div>Community detection in organizational networks is vital for optimizing team structures, yet existing methods face critical challenges: Static models ignore temporal dynamics, dynamic single-layer approaches overlook cross-layer interactions, and multi-objective frameworks often optimize goals in isolation, leading to suboptimal real-world performance. We propose the Multi-Objective Dynamic Multi-Layer Hypergraph Modeling Framework (MO-DMLHM), integrating three innovations: (1) Adaptive Dynamic Hypergraph Modeling with dual-scale decay and adaptive time windowing to capture spatiotemporal dynamics; (2) Four-Dimensional Multi-Objective Optimization balancing modularity, cross-layer consistency, stability, and efficiency via Pareto-optimal NSGA-III; (3) Hybrid Encoding Evolutionary Algorithm jointly optimizing hyperedge activation and node membership through spectral clustering-guided mutation and betweenness centrality-driven crossover. Experiments on diverse organizational networks show MO-DMLHM outperforms state-of-the-art methods in detection accuracy, cross-layer alignment, and stability, reducing coordination costs by nearly 40%. Ablation studies confirm the necessity of dynamic modeling, multi-objective optimization, and hybrid encoding. MO-DMLHM resolves structural-community decoupling in dynamic multi-layer systems, advancing complex network analysis and enabling adaptive governance in organizations, with extensions to smart cities, biological networks, and financial risk management.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"739 ","pages":"Article 123170"},"PeriodicalIF":6.8,"publicationDate":"2026-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146190944","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

ESP-based prescribed performance formation control for vehicle platoon systems with input saturation: A fully actuated system approach 基于esp的输入饱和车辆排系统的规定性能编队控制：一种全驱动系统方法

IF 6.8 1区计算机科学

Information Sciences Pub Date : 2026-05-25 Epub Date: 2026-01-28 DOI: 10.1016/j.ins.2026.123161

Meilin Lei , Zhechen Zhu , Yingnan Pan , Yan Lei

{"title":"ESP-based prescribed performance formation control for vehicle platoon systems with input saturation: A fully actuated system approach","authors":"Meilin Lei , Zhechen Zhu , Yingnan Pan , Yan Lei","doi":"10.1016/j.ins.2026.123161","DOIUrl":"10.1016/j.ins.2026.123161","url":null,"abstract":"<div><div>Prescribed performance control (PPC) plays a vital role in vehicle platoon systems by ensuring their safe and stable operation, and its effectiveness is commonly limited by spacing policies, initial conditions, and input saturation. This paper investigates an improved PPC strategy under these influences within the framework of the fully actuated system approach. Firstly, an improved exponential spacing policy (IESP) incorporating the leader’s velocity information is proposed to mitigate the effects of velocity fluctuations on inter-vehicle spacing. Subsequently, a novel shifting function is designed such that the spacing error converges inside the prescribed region within the settling time, thus eliminating the dependence on the initial spacing error. The time-varying convergence boundary of the proposed performance function improves the adaptability of the system to sudden changes in the road environment. In addition, the input saturation problem is addressed using the hyperbolic tangent function. Finally, all the signals of the system are proven to be semi-globally ultimately uniformly bounded, ensuring the internal stability, string stability, and traffic flow stability. The effectiveness of the proposed strategy is verified via simulation results.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"739 ","pages":"Article 123161"},"PeriodicalIF":6.8,"publicationDate":"2026-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191056","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

Global context modeling for image super-resolution transformer 图像超分辨率转换器的全局上下文建模

IF 6.8 1区计算机科学

Information Sciences Pub Date : 2026-05-25 Epub Date: 2026-01-27 DOI: 10.1016/j.ins.2026.123135

Dongsheng Ruan , Yuan Zheng , Lide Mu , Ao Ran , Lei Pan , Mingfeng Jiang , Chengjin Yu , Nenggan Zheng , Huafeng Liu

{"title":"Global context modeling for image super-resolution transformer","authors":"Dongsheng Ruan , Yuan Zheng , Lide Mu , Ao Ran , Lei Pan , Mingfeng Jiang , Chengjin Yu , Nenggan Zheng , Huafeng Liu","doi":"10.1016/j.ins.2026.123135","DOIUrl":"10.1016/j.ins.2026.123135","url":null,"abstract":"<div><div>Window-based Transformers have achieved remarkable results in image super-resolution (SR). State-of-the-art SR models generally employ a window self-attention mechanism combined with a multi-layer perceptron (MLP) to effectively capture long-range dependencies. However, the window design and the MLP’s deficiency in capturing spatial dependencies restrict their capacity to utilize global contextual information in images. This paper aims to address this limitation by introducing global context modeling. Specifically, we propose a general global context-injected framework for window self-attention. Within this framework, we develop a new instantiation with a novel global context-injected (GCI) module, which allows each window to take advantage of the contextual information from other windows. The GCI module is lightweight and can be easily integrated into existing window-based Transformers, improving performance with negligible increases in parameters and computational costs. Furthermore, we introduce a window self-attention (WSA) to vision state space (VSS) flow to further enhance the ability for global context modeling. We incorporate our advancements into popular SR models, such as SwinIR and SRFormer, creating enhanced versions. Extensive experiments on three representative SR tasks demonstrate the effectiveness of our methods, showing substantial performance improvements over their vanilla counterparts. Notably, our GCI-MSRformer outperforms current state-of-the-art models like MambaIR.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"739 ","pages":"Article 123135"},"PeriodicalIF":6.8,"publicationDate":"2026-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081918","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

Multi-attribute group consensus decision-making with two-stage trust risk adjustment 基于两阶段信任风险调整的多属性群体共识决策

IF 6.8 1区计算机科学

Information Sciences Pub Date : 2026-05-25 Epub Date: 2026-01-27 DOI: 10.1016/j.ins.2026.123162

Peide Liu , Yurong Qian , Ran Dang , Fei Teng , Peng Wang

{"title":"Multi-attribute group consensus decision-making with two-stage trust risk adjustment","authors":"Peide Liu , Yurong Qian , Ran Dang , Fei Teng , Peng Wang","doi":"10.1016/j.ins.2026.123162","DOIUrl":"10.1016/j.ins.2026.123162","url":null,"abstract":"<div><div>As decision-making problems continue to expand, group decision-making (GDM) has seen growing interest in the application of social trust networks (STN). The trust capacity stems of a decision maker (DM) from both self-trust and external social support. Misalignment between the two may lead to trust crises or overconfidence, affecting decision outcomes. Under a two-dimensional linguistic (2DL) environment, a consensus method for multi-attribute group decision-making (MAGDM) that combines internal and external trust mechanisms is presented in this paper. First, DMs’ self-trust is assessed through subjective judgment in the 2DL setting, and then compared with social trust support from the STN in a two-stage trust risk evaluation to align individual competence with external expectations. Next, individual opinions are optimized during the consensus process while managing trust risk. In attribute assignment, individual weights are determined through the interaction between DMs and STN, and attribute weights are calculated using information entropy. To better capture DMs’ psychological behavior, such as regret and hesitation during comparisons, a new MAGDM ranking method integrating regret theory and the SIR method is proposed to improve decision reliability. Lastly, through an illustrative application in an actual data element market context and comparative analysis, the effectiveness of the proposed method is demonstrated, offering actionable insights to support decisions pertaining to data factor market development.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"739 ","pages":"Article 123162"},"PeriodicalIF":6.8,"publicationDate":"2026-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191053","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

SSDSNet: Dual-stage self-supervised network for low-light image enhancement 用于弱光图像增强的双级自监督网络

IF 6.8 1区计算机科学

Information Sciences Pub Date : 2026-05-25 Epub Date: 2026-02-05 DOI: 10.1016/j.ins.2026.123191

Zhuo-Ming Du, Qian Yu, Fei-long Han

引用次数: 0

Space tree-based graph continuous cellular automaton for unit commitment and economic dispatch optimization 基于空间树型图连续元胞自动机的机组投入与经济调度优化

IF 6.8 1区计算机科学

Information Sciences Pub Date : 2026-05-25 Epub Date: 2026-02-03 DOI: 10.1016/j.ins.2026.123199

Siyi Zhou , Li’ao Chen , Xingyu Liang , Min Xia , Shi Liang , Jun Liu , Jiayue Hu

{"title":"Space tree-based graph continuous cellular automaton for unit commitment and economic dispatch optimization","authors":"Siyi Zhou , Li’ao Chen , Xingyu Liang , Min Xia , Shi Liang , Jun Liu , Jiayue Hu","doi":"10.1016/j.ins.2026.123199","DOIUrl":"10.1016/j.ins.2026.123199","url":null,"abstract":"<div><div>Unit Commitment (UC) and Economic Dispatch (ED) are core issues in the power system. In this paper we try to solve both problems jointly: UC-ED problem is a large-scale mixed-integer linear programming (MILP) problem. Algorithms based on mathematical optimization cannot solve large-scale problems, models based on heuristic algorithms tend to fall into local optima, and methods based on deep learning generally cannot directly handle constraint violations. To address UC-ED problem, a new framework: Space tree-based graph continuous cellular automaton (ST-GCCA) has been proposed. It extracts fused features through an autoencoder and decision tree, then uses deep boosted regression trees to generate initial solution of UC-ED problem, and finally employs graph continuous cellular automaton (GCCA) to optimize the solution, achieving economic and secure power system dispatch. Compared with traditional algorithms, it achieves 1400<span><math><mo>×</mo></math></span> harmonic mean speedup improvement, making it possible to solve large-scale problems; compared to the most up-to-date AI approaches, it can explicitly handle safety constraints. While achieving speed improvements, it reached economic optimality and, more importantly, achieved zero constraint violations. The experimental results on the IEEE 30-bus and IEEE 118-bus test systems demonstrate our achievements, indicating that ST-GCCA can find the optimal solution to the UC-ED problem.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"739 ","pages":"Article 123199"},"PeriodicalIF":6.8,"publicationDate":"2026-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191149","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

Machine learning and deep learning techniques for detecting brown spot and narrow brown spot diseases in paddy (Oryza sativa): Algorithms, challenges, and future prospects 水稻褐斑病和窄褐斑病检测的机器学习和深度学习技术：算法、挑战和未来展望

IF 6.8 1区计算机科学

Information Sciences Pub Date : 2026-05-25 Epub Date: 2026-02-02 DOI: 10.1016/j.ins.2026.123195

Fredy William Amon, Bhabesh Nath, Dhruba Kumar Bhattacharyya

{"title":"Machine learning and deep learning techniques for detecting brown spot and narrow brown spot diseases in paddy (Oryza sativa): Algorithms, challenges, and future prospects","authors":"Fredy William Amon, Bhabesh Nath, Dhruba Kumar Bhattacharyya","doi":"10.1016/j.ins.2026.123195","DOIUrl":"10.1016/j.ins.2026.123195","url":null,"abstract":"<div><div>The reviewed studies demonstrate that host plant resistance is a viable strategy for managing brown spot (BS) disease in paddy. However, challenges such as limited resistant varieties and environmental influences persist. Resistant genotypes like PARC-7 and IRRI-43 show promise, but breeding efforts must prioritise stability, yield, and genotype-environment interactions through multilocation testing. Accurate disease diagnosis, particularly distinguishing BS from narrow brown spot (NBS) based on lesion morphology, is critical for effective management. Meanwhile, AI-based disease monitoring presents opportunities but faces challenges in model selection, balancing accuracy with deployability. While advanced deep learning architectures show potential, issues such as lesion heterogeneity, data scarcity, and real-world variability hinder practical implementation. Future research must focus on robust data collection, improved image processing, lightweight AI models, and enhanced feature extraction to bridge the gap between controlled experiments and field applications. Addressing these challenges will be essential for developing reliable and scalable solutions to support sustainable rice production.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"739 ","pages":"Article 123195"},"PeriodicalIF":6.8,"publicationDate":"2026-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191142","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

Campus anomaly detection systems from the perspective of unmanned aerial vehicles 基于无人机视角的校园异常检测系统

IF 6.8 1区计算机科学

Information Sciences Pub Date : 2026-05-25 Epub Date: 2026-01-30 DOI: 10.1016/j.ins.2026.123166

Shujuan Feng , Jinming Wang , Yangkai Wu , Fei Liu , Ezzeddine Touti

{"title":"Campus anomaly detection systems from the perspective of unmanned aerial vehicles","authors":"Shujuan Feng , Jinming Wang , Yangkai Wu , Fei Liu , Ezzeddine Touti","doi":"10.1016/j.ins.2026.123166","DOIUrl":"10.1016/j.ins.2026.123166","url":null,"abstract":"<div><div>Campus anomalies due to large crowds are monitored and thwarted using unmanned aerial vehicle (UAV) images/ videos. A pattern anomaly is detected by correlating the physical dimensions of the input image with the usual and unusual activities used for training. The proposed Boundary Position-induced Object Anomaly Detection (BPOAD) method uses deep ensemble learning with parallel, modular functions to identify unusual crowd behaviour patterns. The BPOAD method uniquely coordinates bagging and boosting processes within its method. The bagging component creates diverse training subsets to enhance boundary detection precision and orientation, while the boosting component adaptively weights misclassified instances to improve feature correlation across training sets. This dual approach allows the system to maintain high accuracy even after precision saturation, as the model can selectively apply either technique based on extracted features. This method establishes robust decision boundaries that maximise anomaly detection by correlating the physical dimensions of input images with normal and abnormal activity patterns. In real-world campus security applications, this significantly reduces false alarm rates and faster response times to potential threats. Experimental results demonstrate BPOAD’s effectiveness with 12.79% improved anomaly detection precision, 11.81% higher sensitivity, and 11.54% increased recall compared to existing methods. These improvements enable campus security personnel to more accurately identify and respond to unusual situations, ultimately enhancing overall campus safety management.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"739 ","pages":"Article 123166"},"PeriodicalIF":6.8,"publicationDate":"2026-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146190942","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

Generalized mining of mixed drove co-occurrence patterns 混合驱动共现模式的广义挖掘

IF 6.8 1区计算机科学

Information Sciences Pub Date : 2026-05-25 Epub Date: 2026-01-27 DOI: 10.1016/j.ins.2026.123148

Witold Andrzejewski, Pawel Boinski

{"title":"Generalized mining of mixed drove co-occurrence patterns","authors":"Witold Andrzejewski, Pawel Boinski","doi":"10.1016/j.ins.2026.123148","DOIUrl":"10.1016/j.ins.2026.123148","url":null,"abstract":"<div><div>In this paper, we investigate mining Mixed-Drove spatio-temporal Co-Occurrence Patterns (MDCOPs). MDCOPs represent sets of object types frequently located together for a given minimum fraction of time. Current solutions fail to address several important factors in practical applications. Specifically, state-of-the-art methods rely on a series of snapshots, i.e., discrete object positions recorded at predefined timestamps rather than their trajectories. However, spatio-temporal data gathering often depends on unsynchronized distributed sensors that independently register positions for each object.</div><div>To tackle this issue using traditional methods, one can interpolate object positions at snapshot timestamps. However, this raises another challenge: determining the optimal number of snapshots while balancing accuracy, processing time, and memory requirements. To overcome these limitations, we formulate a generalized MDCOP mining problem and introduce GMDCOP-Miner, an algorithm that employs a new, generalized time-prevalence measure. The proposed algorithm provides the most accurate results, equal to those obtained via state-of-the-art methods with the number of snapshots tending to infinity. Moreover, our experiments demonstrate that GMDCOP-Miner surpasses existing approaches in both processing time and memory efficiency.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"739 ","pages":"Article 123148"},"PeriodicalIF":6.8,"publicationDate":"2026-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146190947","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

Multi-objective two-archive evolutionary algorithm to optimize the discovery of gene networks involved in cancer survival 多目标双档案进化算法优化发现与癌症生存相关的基因网络

IF 6.8 1区计算机科学

Information Sciences Pub Date : 2026-05-25 Epub Date: 2026-01-30 DOI: 10.1016/j.ins.2026.123182

Fernando M. Rodríguez-Bejarano , Sergio Santander-Jiménez , Miguel A. Vega-Rodríguez

{"title":"Multi-objective two-archive evolutionary algorithm to optimize the discovery of gene networks involved in cancer survival","authors":"Fernando M. Rodríguez-Bejarano , Sergio Santander-Jiménez , Miguel A. Vega-Rodríguez","doi":"10.1016/j.ins.2026.123182","DOIUrl":"10.1016/j.ins.2026.123182","url":null,"abstract":"<div><div>Gene networks have gained considerable relevance in cancer research, enabling the representation of complex biological relationships that provide insights into the mechanisms driving tumor development and progression. The increasing availability of biological data facilitates the construction of clinically relevant gene networks by integrating multiple information sources. Specifically, we consider mutation data, patient survival data, and protein-protein interaction data to identify networks whose genes are recurrently mutated, significantly involved in patient survival, and functionally associated. To this end, we apply multi-objective optimization to simultaneously maximize survival impact, functional association, and mutation coverage. Herein, we introduce MOTEA-GENSU (Multi-Objective Two-archive Evolutionary Algorithm to discover GEne Networks involved in SUrvival), a novel method that employs two collaborative archives and intelligent evolutionary operators to guide the generation of high-quality gene networks. Evaluation across 27 real biological scenarios covering diverse cancer types shows that MOTEA-GENSU outperforms existing methods, achieving superior results in 92.6% of comparisons, with improvements of up to 315.8% over the best-performing competing approach, and consistently surpassing all state-of-the-art methods on average within each evaluated dataset. Biological analysis of the identified networks validates their functional coherence and significant impact on cancer patient survival, revealing clinically relevant networks composed of genes with demonstrated prognostic value.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"739 ","pages":"Article 123182"},"PeriodicalIF":6.8,"publicationDate":"2026-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191067","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