Complex & Intelligent Systems最新文献_第2页

A unified differential-equation and operations-research framework for benchmarking metaheuristic optimization of engineering systems 工程系统标杆化元启发式优化的统一微分方程与运筹学框架

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2026-10-12 DOI: 10.1007/s40747-026-02541-8

Abadal-Salam T. Hussain,Taha Almulaisi,Viean A. Zain Al-Aabdeen,Pritesh Shah,Mustafa Mohammed Al-Tuhafi,Hilal A. Fadhil,Ravi Sekhar,Ahmed Dheyaa Radhi

引用次数: 0

MAPLE: manifold-aligned purification with logit-edge calibration for robust black-box adversarial defense MAPLE：歧管对准净化与对数边缘校准稳健的黑箱对抗防御

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2026-10-12 DOI: 10.1007/s40747-026-02538-3

Ning Zhou,Peng Yi

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Replicability of agent-based simulations: a case study with a SEIRS epidemiological model 基于主体的模拟的可复制性：SEIRS流行病学模型的案例研究

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2026-10-09 DOI: 10.1007/s40747-026-02546-3

Benjamin Antunes, Hanae Havion, Étienne Delay, Clément Foucher, David R. C. Hill, Claude Mazel, Paul-Antoine Bisgambiglia, Arthur Scriban, Oleksandr Zaitsev, Raphaël Duboz

引用次数: 0

Uncertainty-aware evidential deep learning for reliable stroke detection and clinical decision support 不确定性感知证据深度学习用于可靠的脑卒中检测和临床决策支持

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2026-10-09 DOI: 10.1007/s40747-026-02540-9

Muhammad Asim Saleem, Ashir Javeed, Wasan Akarathanawat, Aurauma Chutinet, Nijasri Charnnarong Suwanwela, Surachai Chaitusaney, Watit Benjapolakul, Pasu Kaewplung

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FedMed-LoRA: a federated parameter-efficient fine-tuning framework with dual lora for medical multi-organ segmentation FedMed-LoRA：用于医学多器官分割的具有双lora的联邦参数高效微调框架

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2026-10-09 DOI: 10.1007/s40747-026-02548-1

Fengjun Zhou, Shuyan Jiao, Xiaoguang Lv, Nana Zhou, Hengji Hu, Guiyun Wang, Jian Song, Zhenqiang Zhang

引用次数: 0

XCardioPulmoNet: an explainable attention-guided residual framework for cardiopulmonary disease detection using sound classification XCardioPulmoNet：一个可解释的注意引导残差框架，用于使用声音分类进行心肺疾病检测

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2026-10-09 DOI: 10.1007/s40747-026-02539-2

Shimaa Nagro, Maha Helal

引用次数: 0

MedSegNet-AXU: advanced segmentation and graph-based classification of multiple sclerosis lesions from 3D magnetic resonance imaging via radiomics and clinical data fusion MedSegNet-AXU：通过放射组学和临床数据融合，从3D磁共振成像中对多发性硬化病变进行高级分割和基于图的分类

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2026-10-08 DOI: 10.1007/s40747-026-02535-6

Wasimul Karim, Sayeem Been Zaman, Debopom Sutradhar, Ripon Kumar Debnath, Sami Azam, Kheng Cher Yeo, Yan Zhang, Mirjam Jonkman

引用次数: 0

Triple collaborative asymmetric deep hashing for multi-label image retrieval 基于三重协同非对称深度哈希的多标签图像检索

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2026-10-08 DOI: 10.1007/s40747-026-02533-8

Weigang Wang, Ziyuan Cui

{"title":"Triple collaborative asymmetric deep hashing for multi-label image retrieval","authors":"Weigang Wang, Ziyuan Cui","doi":"10.1007/s40747-026-02533-8","DOIUrl":"https://doi.org/10.1007/s40747-026-02533-8","url":null,"abstract":"Due to its efficient storage and fast retrieval capabilities, hashing methods have garnered significant attention in image retrieval applications. However, most existing methods rely on semantic similarity matrices to indicate whether two images are similar or not, which neglects the guiding role of semantic signals in the multimodal information flow. Moreover, conventional symmetric hashing architectures encode both query and database images simultaneously, which increases the time overhead of the model. To address aforementioned limitations, we propose a Triple Collaborative Asymmetric Deep Hashing (TCADH) framework for multi-label image retrieval, which maps two asymmetric image streams and one semantic label stream into a common discrete Hamming space, achieving collaborative alignment of multi-source information. Specifically, we first build a Siamese network based on ResNet34 to extract features from both original and augmented images, while incorporating a sign-consistency-guided feature enhancement block into the backbone to mitigate information loss caused by feature dimensionality reduction. Furthermore, we introduce a semantic label stream to capture fine-grained semantic supervision and employ a weighted pairwise loss to maximize semantic consistency among different data streams from the same image source, thereby achieving semantic alignment among original images, semantic labels, and augmented images in the Hamming space. Finally, we adopt an asymmetric learning strategy to capture similarities between hash codes and real-valued image features, improving retrieval precision and accelerating model convergence. Extensive experiments are conducted on four multi-label datasets: NUS-WIDE, MIRFLICKR-25K, MS-COCO, and VOC2012, the results demonstrate that the proposed TCADH outperforms state-of-the-art deep supervised hashing methods in multi-label image retrieval tasks.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"32 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149130597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 operator selection via deep reinforcement learning for decomposition-based multi-objective multi-task optimization 基于深度强化学习的多目标多任务优化算子自适应选择

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2026-10-07 DOI: 10.1007/s40747-026-02387-0

Xuanwei Zhang,Zhiyuan Pan,Yushu Du,Junze Zhu,Long Chen

{"title":"Adaptive operator selection via deep reinforcement learning for decomposition-based multi-objective multi-task optimization","authors":"Xuanwei Zhang,Zhiyuan Pan,Yushu Du,Junze Zhu,Long Chen","doi":"10.1007/s40747-026-02387-0","DOIUrl":"https://doi.org/10.1007/s40747-026-02387-0","url":null,"abstract":"Abstract Multi-objective multi-task optimization (MO-MTO), which aims to simultaneously solve multiple related multi-objective problems by leveraging knowledge transfer across tasks to enhance optimization performance on each task, has emerged as a new research focus in the field of evolutionary computation. Although a variety of MO-MTO algorithms have achieved certain success on different problems, most of them rely on a single genetic operator to generate offspring, and their knowledge transfer process is often confined to the objective space, failing to fully exploit valuable information in the decision space. This limitation results in insufficient search efficiency and difficulty in producing high-quality offspring. To address this issue, this paper proposes a multi-task evolutionary algorithm with reinforcement learning-based knowledge selection (MTEA-RLKS), which integrates a collaborative knowledge transfer mechanism combining both objective and decision spaces, as well as static and dynamic knowledge. Specifically, the algorithm extracts static knowledge from global population distributions and local neighborhoods in the objective space, while capturing dynamic knowledge of population evolution in the decision space by employing Gaussian processes. Through synergistic knowledge transfer, it guides the generation of high-quality offspring and accelerates the optimization process. Additionally, a Deep Q-Network is introduced to adaptively select genetic operators, which is trained online during iterations to accommodate the evolutionary needs of different solutions. Experimental results on two MO-MTO benchmark test sets, CEC2017 and CEC2019, demonstrate that the proposed MTEA-RLKS significantly outperforms six other state-of-the-art algorithms.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"54 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2026-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149090975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

A heart disease diagnosis method based on structure-adaptive Belief Rule Base and multi-strategy-improved whale optimization algorithm 基于结构自适应信念规则库和多策略改进鲸鱼优化算法的心脏病诊断方法

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2026-10-06 DOI: 10.1007/s40747-026-02549-0

Xueqiu Sun,Wei Fu,Ning Ma,Xiping Duan,Manlin Chen

引用次数: 0