Applied Intelligence最新文献

{"title":"Knee-cartilage segmentation from MR images using Multi-view Hypergraph Convolutional Neural Networks","authors":"Christos Chadoulos,&nbsp;John Theocharis,&nbsp;Andreas Symeonidis,&nbsp;Serafeim Moustakidis","doi":"10.1007/s10489-025-06808-4","DOIUrl":"10.1007/s10489-025-06808-4","url":null,"abstract":"<div><p>Leveraging the increased capacities of hypergraphs to model complex data structures, we propose in this article the Multi-view Hyper-Graph Convolutional Network <i>(MVHGCN)</i> to yield automated knee-joint cartilage segmentations from MRIs. The main properties of our approach are presented as follows: 1) Node features are obtained from multi-view <i>(MV)</i> acquisitions, corresponding to different feature extractors or image modalities. 2) Node embeddings are generated using a distributive <i>MV</i> convolution scheme which combines the various view-specific convolutions. These results are aggregated via an attention-based fusion module to automatically learn the weights of the different views. 3) Our model integrates both local and global level learning, simultaneously. Local hypergraph convolutions explore the relationships across the spatially aligned node libraries, while global hypergraph convolutions search for global affinities between nodes located at different positions within the image. 4) We propose two different blending schemes to combine local and global convolutions, namely, the cross-talk <i>(CT)</i> and the collaborative <i>(COL)</i> blending units, respectively. Using these units as building blocks, we construct the <i>MVHGCN</i> model, a deep network with enhanced feature representation and learning capabilities. The suggested segmentation method is evaluated on the publicly available Osteoarthritis Initiative <i>(OAI)</i> cohort. Specifically, we have designed a thorough experimental setup, including parameter sensitivity analysis and comparative results against a series of existing traditional methods, deep <i>CNN</i> models, and graph convolutional networks. The results show that <i>MVHGCN</i> outperforms the competing methods, achieving an overall cartilage segmentation score of <span>(mathcal {DSC} = 95.81%)</span> and <span>(mathcal {DSC} = 96.33%)</span>, for the <i>CT</i> and the <i>COL</i> blending, respectively.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 13","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10489-025-06808-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144909752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction to: Dynamic preventive maintenance strategy for a heterogeneous multi-unit redundant system: A deep reinforcement learning approach with weighted network estimator","authors":"Deming Xu,&nbsp;Yan Wang,&nbsp;Xiang Liu,&nbsp;Hao Ma,&nbsp;Zhicheng Ji","doi":"10.1007/s10489-025-06804-8","DOIUrl":"10.1007/s10489-025-06804-8","url":null,"abstract":"","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 13","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144909894","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}

{"title":"Deep reinforcement learning with graph attention mechanism for vehicle routing problem with time windows","authors":"Fan Zhang,&nbsp;Huiling Hu,&nbsp;Yuqian Zhao","doi":"10.1007/s10489-025-06829-z","DOIUrl":"10.1007/s10489-025-06829-z","url":null,"abstract":"<div><p>As the logistics industry expands, the complexity of vehicle routing problems, particularly those with time window constraints, increases with the growing demand for services. The challenge of vehicle routing problems with time windows (VRPTW) lies in efficiently scheduling a fleet of vehicles to service a set of customers within specified time frames. This study introduces a deep reinforcement learning approach based on attention mechanisms to optimize vehicle routing and scheduling, aiming to meet specific time window requirements of customers while effectively reducing travel distances and costs, thereby enhancing the efficiency of logistics delivery. This method models the problem as a Markov decision process, defines actions, states, and rewards, and uses reinforcement learning for training to extract node information features and generate preliminary solutions. The model can focus on key information and optimize strategy selection by introducing an encoding-decoding structure and attention map neural network. Then, the large neighborhood search algorithm is used to iterative optimize the initial solution to obtain the optimal solution. The model is trained and tested on the Solomon data set. The experimental results show that the model is significantly better than other methods.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 13","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144905155","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}

{"title":"RDM2: a two-stage model based on residual learning diffusion model and multi-scale convolution for Low Dose CT denoising","authors":"Zhencun Jiang,&nbsp;Kangrui Ren,&nbsp;Kefan Wang,&nbsp;Zhongjie Wang","doi":"10.1007/s10489-025-06604-0","DOIUrl":"10.1007/s10489-025-06604-0","url":null,"abstract":"<div><p>Computed Tomography (CT) is widely used in clinical diagnosis, but large amount of radiation accompanied is not expected. Low Dose CT (LDCT) can reduce the radiation effect, however, noise and artifacts will be unavoidably produced. Low dose accompanies large noise intensity, which is difficult to effectively denoise while retaining the details. Aiming at this problem, a two-stage LDCT denoising model, named RDM2, is proposed. In the first stage, a residual learning diffusion model is constructed to eliminate the noise of LDCT. The residuals between LDCT and Normal Dose CT (NDCT) is a kind of complex mixed noise with unknown intensity. In order to fully utilize the residual information, the whole residual is equally divided into small pieces and added iteratively in the diffusion process. Considering even the best trained residual diffusion model may bring unavoidable error when it is used for prediction, a multi-scale convolution encoder decoder convolution neural network (MEDCNN) is proposed in the second stage to further reduce this part of error. The proposed model RDM2 is validated on both the Mayo2020 25% dose LDCT dataset and Mayo2020 10% dose LDCT dataset, the values of PSNR, SSIM, and RMSE on these two datasets are respectively 44.7651, 0.9939, 0.0068 and 35.5302, 0.9601, 0.0172. It is proved that RDM2 outperforms the traditional method, the supervised learning-based method and the GAN-based method, and has the potential to meet clinical needs. Code is available at: https://github.com/zhencunjiang/RDM2.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 13","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144904937","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}

{"title":"Survey on Tabular Data Privacy and Synthetic Data Generation in Industry 4.0","authors":"Hadi Koubeissy,&nbsp;Amir Amine,&nbsp;Marc Kamradt,&nbsp;Abdallah Makhoul","doi":"10.1007/s10489-025-06823-5","DOIUrl":"10.1007/s10489-025-06823-5","url":null,"abstract":"<div><p>Synthetic data is an emerging field that solves the raised need for privacy-preserving data sharing and the lack of real data. One of the most common data types used is tabular data, which is widely used to train machine learning models, especially in the industrial domain for better decision-making and edge case handling, two key points in Industry 4.0. In this paper, we present and evaluate state-of-the-art models for tabular data generation under a proposed taxonomy consisting of statistical models, generative adversarial networks (GANs)-based models, denoising diffusion probabilistic models (DDPMs), and large language models (LLMs). Additionally, we propose a revised evaluation taxonomy consisting of three dimensions, including realism, representativeness, and privacy. The results proved that analyzing models based on multiple metrics from each category could ensure a better understanding of the dataset when used for downstream tasks. Finally, we found that models based on GANs are still a solid option in multiple cases, such as a constrained computational environment. In contrast, models based on LLMs and DDPMs are more promising in terms of realism and representativeness. More research should be invested in overcoming limitations such as numerical data representation and long training times for LLMs. Our survey serves as a study for existing models and newer directions in the field, with guidelines for evaluation that can be applied to industrial and other domains.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 13","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144904974","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}

{"title":"Federated trajectory clustering based on multi-feature similarity calculation","authors":"Kun Guo,&nbsp;Xinglong Hu,&nbsp;Zhiyu Zhang,&nbsp;Chuyu Liu,&nbsp;Qishan Zhang","doi":"10.1007/s10489-025-06813-7","DOIUrl":"10.1007/s10489-025-06813-7","url":null,"abstract":"<div><p>Trajectory clustering plays an important role in numerous real-world applications, such as urban transportation planning and tourist route recommendation. Existing trajectory clustering approaches primarily focus on the spatial and temporal features of trajectories but neglect the velocity feature. Therefore, it is difficult for them to distinguish trajectories sharing spatial and temporal features but diverging velocities. Furthermore, in the context of distributed trajectory clustering among multiple participants, individuals’ privacy, such as the travel routes or habits of a person, should never be violated, which necessitates the equipment of trajectory clustering with privacy-preserving techniques. In this paper, we propose a Federated and Multi-Feature-based Trajectory Clustering (FMFTC) algorithm to address the above issues. First, we develop a Multi-Feature-based Trajectory Clustering (MFTC) algorithm with a new multi-feature to vector encoder (MF2Vec) to capture spatial, temporal and velocity features during trajectory embedding generation. Second, we adapt MFTC to the federated learning paradigm to construct FMFTC for privacy-preserving distributed trajectory clustering. The experiments on real-world datasets demonstrate that FMFTC achieves up to <span>(varvec{24.4%})</span> higher accuracy than existing trajectory clustering algorithms and performs identically as MFTC with no accuracy loss.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 13","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144904976","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}

{"title":"Multi-agent neighborhood coordinated and holistic optimized actor-critic framework for adaptive traffic signal control","authors":"Qi Deng,&nbsp;Lijun Wu,&nbsp;Zhiyuan Li,&nbsp;Kaile Su,&nbsp;Wei Wu,&nbsp;Weiwei Duan","doi":"10.1007/s10489-025-06758-x","DOIUrl":"10.1007/s10489-025-06758-x","url":null,"abstract":"<div><p>Adaptive Traffic Signal Control (ATSC) is a pivotal research area within intelligent transportation systems, aiming to enhance transportation efficiency and alleviate traffic congestion at signalized intersections. While multi-agent deep reinforcement learning has been extensively applied to ATSC, existing approaches commonly frame it as a fully cooperative problem, presupposing that all agents are committed to pursuing a collective optimal solution. However, achieving such altruistic cooperation is often impractical. Furthermore, as the number of agents escalates, challenges such as the curse of dimensionality and non-stationarity arise, complicating the learning process. To address these issues, we propose a novel perspective by framing ATSC as a competitive-cooperative game trade-off scenario and design a multi-agent framework, termed Neighborhood Coordinated and Holistic Optimized Actor-Critic (NcHo-AC). Specifically, we introduce a novel traffic state representation, design a sophisticated feature extraction network, develop a robust training algorithm, and leverage mean field approximation to model population-level agent interactions. These designs foster neighborhood-level cooperation and communication, facilitate the learning of the desired Nash equilibrium, and mitigate the noise caused by agents’ exploratory behaviors, thereby alleviating non-stationarity and the curse of dimensionality, while enhancing scalability to large-scale traffic networks. Comprehensive experiments conducted on both synthetic and real-world datasets demonstrate that NcHo-AC significantly outperforms state-of-the-art baselines across four key metrics: average travel time, average queue length, delay, and throughput, along with improved convergence, robustness, and interpretability.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 13","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144904977","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}

{"title":"Uncovering connections: a reference network approach to statute law retrieval","authors":"Thi-Hai-Yen Vuong,&nbsp;Hai-Long Nguyen,&nbsp;Tan-Minh Nguyen,&nbsp;Ha-Thanh Nguyen,&nbsp;Le-Minh Nguyen,&nbsp;Xuan-Hieu Phan","doi":"10.1007/s10489-025-06818-2","DOIUrl":"10.1007/s10489-025-06818-2","url":null,"abstract":"<div><p>The increasing volume and complexity of statute law data have led to a growing demand for efficient and effective retrieval methods. This paper presents a novel approach to statute law retrieval that utilizes reference networks to uncover connections between laws. By representing law articles as a network of references, our method allows users to quickly identify relevant direct and indirect articles. The key point is that the reference network can encode both internal and external legal relations, helping to integrate both the local and the long-range dependencies into the final retrieval model. The proposed approach is evaluated on several statute law corpora and shows that it performs better existing methods on the same tasks. In addition, our finding is that internal references help enhance the accuracy significantly while external links are also important. Our empirical study also suggests the optimal range of local window size to achieve a balance between retrieval accuracy and noise. Our approach can also contribute to the development of AI-assisted legal research tools, making it easier for legal practitioners to find relevant laws and precedents. Furthermore, by uncovering hidden connections between laws, our method can help identify inconsistencies and gaps in the legal system, ultimately improving its effectiveness and reliability.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 13","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144904975","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}

{"title":"Simultaneous fault prediction in evolving industrial environments with ensembles of Hoeffding adaptive trees","authors":"A. Esteban,&nbsp;A. Cano,&nbsp;S. Ventura,&nbsp;A. Zafra","doi":"10.1007/s10489-025-06786-7","DOIUrl":"10.1007/s10489-025-06786-7","url":null,"abstract":"<div><p>Predictive Maintenance (PdM) emerges as a critical task of Industry 4.0, driving operational efficiency, minimizing downtime, and reducing maintenance costs. However, real-world industrial environments present unsolved challenges, especially in predicting simultaneous and correlated faults under evolving conditions. Traditional batch-based and deep learning approaches for simultaneous fault prediction often fall short due to their assumptions of static data distributions and high computational demands, making them unsuitable for dynamic, resource-constrained systems. In response, we propose OEMLHAT (Online Ensemble of Multi-Label Hoeffding Adaptive Trees), a novel model tailored for real-time, multi-label fault prediction in non-stationary industrial settings. OEMLHAT introduces a scalable online ensemble architecture that integrates online bagging, dynamic feature subspacing, and adaptive output weighting. This design allows it to efficiently handle concept drift, high-dimensional input spaces, and label sparsity, key bottlenecks in existing PdM solutions. Experimental results on three public multi-label PdM case studies demonstrate substantial improvements in predictive performance of OEMLHAT over previous batch-based and online proposals for multi-label classification, particularly with an average improvement in micro-averaged F1-score of 18.49% over the second most-accurate batch-based proposal and of 8.56% in the case of the second best online model. By addressing a critical gap in online multi-label learning for PdM, this work provides a robust and interpretable solution for next-generation industrial monitoring systems for fault detection, particularly for rare and concurrent failures.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 13","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10489-025-06786-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144892411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Remote sensing image change detection method based on dual-branch multi-level feature difference interactive learning","authors":"Songtao Ding,&nbsp;Xinyu Li,&nbsp;Hongyu Wang,&nbsp;Shiwen Gao","doi":"10.1007/s10489-025-06728-3","DOIUrl":"10.1007/s10489-025-06728-3","url":null,"abstract":"<div><p>Remote sensing (RS) image change detection (CD) is a key technology in environmental monitoring and geographic information systems (GIS). It can reveal the dynamic changes of surface features and is of great significance in fields such as urban planning, disaster assessment, and ecological research. However, the pseudo-change problem, that is, the image differences caused by non-actual surface changes, often affects the accuracy of detection, leading to false alarms and omissions, which limits the effectiveness of the CD technology. Traditional dual-branch CD methods often focus on basic feature extraction. This method independently processes the feature extraction of the bi-temporal phases and lacks a comparative interactive learning process for the features of the bi-temporal phases, thereby weakening its ability to identify pseudo-changes in complex environments. To solve the above problems, we propose a RS image CD method based on dual-branch multi-level feature difference interactive learning (DMFDIL). The model is built based on the siamese convolutional neural network (CNN) of deep learning and includes three parts: the dual-branch cooperative coding module (DCM), the dual-branch difference decoding module (DDDM), and the change output module (COM). Among them, the DCM innovatively introduces the tri-attention mechanism. Through this mechanism, the model can effectively interact on multi-level features, enhancing the ability to capture subtle changes in RS images, especially in distinguishing real changes from pseudo-changes. The DDDM, on the other hand, focuses on further optimizing the detection capability of the model by identifying real changes from pseudo-changes and integrating feature information at different scales. Finally, the validation was carried out on three public datasets, and the results were better than other popular methods. The experimental results on the LEVIR-CD dataset show that the proposed DMFDIL model achieved 95.80% in precision (Pre), 94.54% in recall (Rec), 95.16% in F1-score (F1), 91.10% in Intersection over Union (IoU), and 99.07% in overall accuracy (OA), which are significantly better than those of the state-of-the-art (SOTA) approaches. This method provides a new technical approach in the field of RS image CD, especially in improving detection accuracy and dealing with pseudo-change problems, and has important application value and broad application prospects.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 13","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144892410","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}