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Predicting lymph node metastasis in colorectal cancer using caselevel multiple instance learning 认领 引用 被引量：1
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作者 Ling-Feng Zou Xuan-Bing Wang +4 位作者 Jing-Wen Li Xin Ouyang Yi-Ying Luo Yan Luo Cheng-Long Wang 《World Journal of Gastroenterology》 SCIE CAS 2026年第1期110-125,共16页
BACKGROUND The accurate prediction of lymph node metastasis(LNM)is crucial for managing locally advanced(T3/T4)colorectal cancer(CRC).However,both traditional histopathology and standard slide-level deep learning ofte... BACKGROUND The accurate prediction of lymph node metastasis(LNM)is crucial for managing locally advanced(T3/T4)colorectal cancer(CRC).However,both traditional histopathology and standard slide-level deep learning often fail to capture the sparse and diagnostically critical features of metastatic potential.AIM To develop and validate a case-level multiple-instance learning(MIL)framework mimicking a pathologist's comprehensive review and improve T3/T4 CRC LNM prediction.METHODS The whole-slide images of 130 patients with T3/T4 CRC were retrospectively collected.A case-level MIL framework utilising the CONCH v1.5 and UNI2-h deep learning models was trained on features from all haematoxylin and eosinstained primary tumour slides for each patient.These pathological features were subsequently integrated with clinical data,and model performance was evaluated using the area under the curve(AUC).RESULTS The case-level framework demonstrated superior LNM prediction over slide-level training,with the CONCH v1.5 model achieving a mean AUC(±SD)of 0.899±0.033 vs 0.814±0.083,respectively.Integrating pathology features with clinical data further enhanced performance,yielding a top model with a mean AUC of 0.904±0.047,in sharp contrast to a clinical-only model(mean AUC 0.584±0.084).Crucially,a pathologist’s review confirmed that the model-identified high-attention regions correspond to known high-risk histopathological features.CONCLUSION A case-level MIL framework provides a superior approach for predicting LNM in advanced CRC.This method shows promise for risk stratification and therapy decisions,requiring further validation. 展开更多
关键词 Colorectal cancer Lymph node metastasis Deep learning Multiple instance learning Histopathology
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PE-MILCon:Multiple-Instance Learning with Contrastive Multi-View Representation for Static Windows PE Malware Detection 认领 引用
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作者 Tuan Nguyen Kim Son Doan Trung Nguyen Minh Nhut Pham 《Computers, Materials & Continua》 SCIE EI 2026年第10期1160-1180,共21页
Static Windows Portable Executable(PE)malware detection remains a significant challenge due to the growing use of packing,obfuscation,and code reuse techniques,which gradually reduce the effectiveness of signature-bas... Static Windows Portable Executable(PE)malware detection remains a significant challenge due to the growing use of packing,obfuscation,and code reuse techniques,which gradually reduce the effectiveness of signature-based and manually engineered feature approaches.Recent deep learning models that operate directly on binary code or static features have achieved encouraging results;however,most still rely on global file-level representations.Such approaches are susceptible to noise introduced by padding or obfuscation and may overlook localized malicious regions.Moreover,many multi-view methods process different feature sources independently,lacking mechanisms to enforce semantic consistency across views.This paper proposes PE-MILCon,a malware detection framework that integrates Multiple-Instance Learning(MIL)with contrastive multi-view representation learning across raw byte segments and structural PE features.In PE-MILCon,each executable file is modeled as a“bag”of byte segments.The attention mechanism within MIL enables the model to focus selectively on suspicious regions rather than treating the entire file uniformly.In parallel,structural and semantic PE features are encoded as a complementary view.A contrastive loss function aligns the two representations within a shared semantic space,enhancing robustness against obfuscation and packing techniques.The proposed framework operates entirely on static analysis and is trained end-to-end.Experiments on large-scale PE datasets under a strict time-based evaluation protocol show that PE-MILCon achieves an ROC-AUC above 0.98 and an F1-score of approximately 0.96,demonstrating competitive performance compared with existing models.In addition,instance-level attention weights provide intuitive indications of important code regions,supporting malware inspection and forensic analysis.These results suggest that PE-MILCon offers an effective,robust,and interpretable approach for static malware detection. 展开更多
关键词 Malware detection multiple-instance learning contrastive learning multi-view learning static analysis portable executable deep learning cybersecurity
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Transformer-based data-driven reinforcement learning for collision-free integrated planning and control of multiple UAVs 认领 引用 被引量：1
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作者 Wei DONG Yue LIU +2 位作者 Xiaoyu GUO Chunyan WANG Zhengtao DING 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第7期110-124,共15页
This paper studies the challenging collision-free planning and control problem for multiple Unmanned Aerial Vehicles(UAVs)with complex dynamics.A data-driven reinforcement learning framework is constructed to address ... This paper studies the challenging collision-free planning and control problem for multiple Unmanned Aerial Vehicles(UAVs)with complex dynamics.A data-driven reinforcement learning framework is constructed to address this challenge by combining the transformer-based learned dynamics and iterative Linear Quadratic Regulator(iLQR)optimization.First,each UAV employs an independent transformer network,Multi-Head Self-Attention(MHSA),and residual connections to model local dynamics from online collected data.This approach enables efficient Jacobian computations via parallelization by exploiting the inherent block-diagonal structure in the decoupled dynamics.Then,to avoid inter-UAV and UAV-obstacle collision in the cooperative flight,logarithmic barrier functions are incorporated into the cost function of iLQR.The block-diagonal approximation of the Hessian is employed to overcome the coupling induced by the barrier terms and preserve the computational tractability during the backward pass.Specifically,the proposed framework possesses robust collision avoidance capabilities in solving the multi-UAV planning and control problem.Finally,simulation results demonstrate the effectiveness and superiority in convergence speed and accuracy of the proposed framework. 展开更多
关键词 Multiple UAVs Planning and control Reinforcement learning Transformer Collision avoidance
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Consensus learning based coordinated formation control of multiple UAVs 认领 引用
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作者 Yong TANG Yingxin SHOU +1 位作者 Bin XU Zhenbao LIU 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第2期402-413,共12页
This paper presents a hierarchical formation control strategy to address the challenges of multiple Unmanned Aerial Vehicles(UAVs)formation control within a cooperative consensus framework.The proposed strategy incorp... This paper presents a hierarchical formation control strategy to address the challenges of multiple Unmanned Aerial Vehicles(UAVs)formation control within a cooperative consensus framework.The proposed strategy incorporates a reference command generation layer,which derives UAV attitude commands based on formation requirements,and a tracking control layer to ensure accurate execution.Collaborative variables,including trajectory position and flight speed,are defined using a three-dimensional track particle and autopilot model,enabling the development of a consensus-based formation control law.Desired attitude angles are computed through altitudehold and coordinated-turn strategies.A sliding surface is designed based on reference models derived from flight quality metrics,while an adaptive controller compensates for aerodynamic model uncertainties.To enhance learning capabilities,a prediction error mechanism based on a series-parallel estimation model is introduced,enabling collaborative learning and the sharing of network weight estimation parameters within the multi-agent system.This facilitates the design of a distributed composite learning law.Lyapunov stability analysis confirms the local exponential stability of the tracking error.The simulations of a twelve-UAV formation,along with comparative analysis of two algorithms,demonstrate the system’s capability for formation maintenance and high-precision tracking control. 展开更多
关键词 Collaborative consistency Distributed composite learning Multiple unmanned aerial vehicles system Serial-parallel estimation model Sliding mode adaptive controller
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Impact of machine learning-driven analysis of blood transcriptomes in multiple sclerosis 认领 引用
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作者 Alessandro Digilio Cinthia Farina 《Neural Regeneration Research》 SCIE CAS CSCD 2026年第8期3557-3558,共2页
Multiple sclerosis(MS)is a chronic disorder of the central nervous system characterized by multifocal lesions where inflammation,demyelination,and neurodegeneration occur(Jakimovski et al.,2024).MS diagnosis primarily... Multiple sclerosis(MS)is a chronic disorder of the central nervous system characterized by multifocal lesions where inflammation,demyelination,and neurodegeneration occur(Jakimovski et al.,2024).MS diagnosis primarily relies on the demonstration of dissemination in time and space of the lesions based on clinical,magnetic resonance imaging(MRI),and cerebrospinal fluid assessments(Jakimovski et al.,2024). 展开更多
关键词 multifocal lesions inflammation cerebrospinal fluid assessments jakimovski blood transcriptomes diagnosis multiple sclerosis ms multiple sclerosis resonance imaging mri
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Flood predictions from metrics to classes by multiple machine learning algorithms coupling with clustering-deduced membership degree 认领 引用
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作者 ZHAI Xiaoyan ZHANG Yongyong +5 位作者 XIA Jun ZHANG Yongqiang TANG Qiuhong SHAO Quanxi CHEN Junxu ZHANG Fan 《Journal of Geographical Sciences》 SCIE CSCD 2026年第1期149-176,共28页
Accurate prediction of flood events is important for flood control and risk management.Machine learning techniques contributed greatly to advances in flood predictions,and existing studies mainly focused on predicting... Accurate prediction of flood events is important for flood control and risk management.Machine learning techniques contributed greatly to advances in flood predictions,and existing studies mainly focused on predicting flood resource variables using single or hybrid machine learning techniques.However,class-based flood predictions have rarely been investigated,which can aid in quickly diagnosing comprehensive flood characteristics and proposing targeted management strategies.This study proposed a prediction approach of flood regime metrics and event classes coupling machine learning algorithms with clustering-deduced membership degrees.Five algorithms were adopted for this exploration.Results showed that the class membership degrees accurately determined event classes with class hit rates up to 100%,compared with the four classes clustered from nine regime metrics.The nonlinear algorithms(Multiple Linear Regression,Random Forest,and least squares-Support Vector Machine)outperformed the linear techniques(Multiple Linear Regression and Stepwise Regression)in predicting flood regime metrics.The proposed approach well predicted flood event classes with average class hit rates of 66.0%-85.4%and 47.2%-76.0%in calibration and validation periods,respectively,particularly for the slow and late flood events.The predictive capability of the proposed prediction approach for flood regime metrics and classes was considerably stronger than that of hydrological modeling approach. 展开更多
关键词 flood regime metrics class prediction machine learning algorithms hydrological model
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Research on error performance of underwater OAM multiplexing optical communication assisted by deep learning 认领 引用
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作者 Aiping HUANG Yi CAO +1 位作者 Honglin WANG Linwei TAO 《Optoelectronics Letters》 EI 2026年第5期261-267,共7页
In this paper,a novel convolutional neural network(CNN)assisted decoding method is proposed to recover information directly for underwater orbital angular momentum(OAM)multiplexing optical communication.The effects of... In this paper,a novel convolutional neural network(CNN)assisted decoding method is proposed to recover information directly for underwater orbital angular momentum(OAM)multiplexing optical communication.The effects of various attenuations and ocean water types,such as absorption,scattering,turbulence fading,noise and diffraction,are considered comprehensively in our analysis.A regularly spaced continuous phase screen is used to represent ocean turbulence.And the angular diffraction function is exploited for simulating the propagation of the OAM beams.In order to minimize the bit error rate(BER)and simplify the receiver design,a CNN assisted decoding method is used to compensate the distorted OAM light and decode the transmission data directly without channel estimation and equalization.The CNN is trained to learn the multiplexed OAM light intensity map generated under various water environments.The bit error performance of CNN OAM system is also compared with that of traditional Gerchberg-Saxton(GS)algorithm.Our numerical simulation results indicate that the CNN assisted method combats the impairing effects of fading and improves the underwater OAM system performance obviously.Furthermore,it outperforms GS algorithm in almost all the turbulence environments at the same water environment.And the BER of the CNN assisted system still decreases effectively by increasing signal-to-noise ratio(SNR)even in moderate and strong turbulence situations while at the same time requiring less computation complexity. 展开更多
关键词 angular diffraction function underwater orbital angular recover information directly regularly spaced continuous phase screen Convolutional Neural Network CNN Deep Learning convolutional neural network cnn assisted Underwater Optical Communication
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Williamson Nanofluid Flow and Transport in an Asymmetric Porous Tapered Channel under Multiple Slip Conditions Using Perturbation and Supervised Machine Learning Models 认领 引用
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作者 H.Kamlesh E.P.Siva +2 位作者 P.Bathmanaban O.D.Makinde Dharmendra Tripathi 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第5期759-792,共34页
The current study comprehensively investigates Williamson nanofluid flow and transport in an asymmetric porous tapered channel under varying slip conditions,using both analytical and supervised machine learning approa... The current study comprehensively investigates Williamson nanofluid flow and transport in an asymmetric porous tapered channel under varying slip conditions,using both analytical and supervised machine learning approaches.This mathematical model integrates thermophoresis,Brownian motion,the Soret and Dufour effects,thermal radiation,and a transverse magnetic field to accurately describe thermosoluble transport phenomena relevant to biomedical contexts.The non-Newtonian Williamson formulation is used to explain how fluids,such as blood,dilute when sheared.Darcy resistance is used to describe porous structures in tissue scaffolds,capillary networks,and dialysis membranes.A perturbation method is used to find analytical solutions that show how key dimensionless parameters affect the profiles of velocity,temperature,concentration,Nusselt number,Sherwood number,skin friction,and pressure gradient.Supervised machine learning models,including artificial neural networks,are also used to predict heat and mass transfer properties and confirm analytical trends with a high degree of accuracy.The results show that increasing the Hartmann number reduces fluid motion due to Lorentz force resistance by approximately 14%,while the Williamson parameter increases shear-thinning and increases velocity by approximately 9%.Thermal radiation significantly broadens the temperature distribution,increasing heat transfer by 12%.The combination of perturbation analysis and supervised machine learning models demonstrates strong predictive power and makes the results more reliable.The integrated analytical-machine learning framework provides essential insights for enhancing nanoparticlemediated drug delivery and advancing hyperthermia cancer treatment through regulated thermosolute transport in porous biological tissues. 展开更多
关键词 Williamson nanofluid magnetohydrodynamics(MHD) thermal radiation hyperthermia cancer therapy drug delivery multiple slip boundary
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Multi-Robot Collaborative Complex Indoor Scene Segmentation via Multiplex Interactive Learning 认领 引用
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作者 Jinfu Liu Zhongzien Jiang +3 位作者 Xinhua Xu Wenhao Li Mengyuan Liu Hong Liu 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2025年第6期1646-1660,共15页
Indoor scene semantic segmentation is essential for enabling robots to understand and interact with their environments effectively.However,numerous challenges remain unresolved,particularly in single-robot systems,whi... Indoor scene semantic segmentation is essential for enabling robots to understand and interact with their environments effectively.However,numerous challenges remain unresolved,particularly in single-robot systems,which often struggle with the complexity and variability of indoor scenes.To address these limitations,we introduce a novel multi-robot collaborative framework based on multiplex interactive learning(MPIL)in which each robot specialises in a distinct visual task within a unified multitask architecture.During training,the framework employs task-specific decoders and cross-task feature sharing to enhance collaborative optimisation.At inference time,robots operate independently with optimised models,enabling scalable,asynchronous and efficient deployment in real-world scenarios.Specifically,MPIL employs specially designed modules that integrate RGB and depth data,refine feature representations and facilitate the simultaneous execution of multiple tasks,such as instance segmentation,scene classification and semantic segmentation.By leveraging these modules,distinct agents within multi-robot systems can effectively handle specialised tasks,thereby enhancing the overall system's flexibility and adaptability.This collaborative effort maximises the strengths of each robot,resulting in a more comprehensive understanding of environments.Extensive experiments on two public benchmark datasets demonstrate MPIL's competitive performance compared to state-of-the-art approaches,highlighting the effectiveness and robustness of our multi-robot system in complex indoor environments. 展开更多
关键词 cross-task interactive learning(artificial intelligence) multi-modal multiplex interactive learning multitask object segmentation semantic segmentation
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Spatial heterogeneity of groundwater depths in coastal cities and their responses to multiple factors interactions by interpretable machine learning models 认领 引用 被引量：2
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作者 Yuming Mo Jing Xu +5 位作者 Senlin Zhu Beibei Xu Jinran Wu Guangqiu Jin You-Gan Wang Ling Li 《Geoscience Frontiers》 SCIE CAS CSCD 2025年第3期223-241,共19页
Understanding spatial heterogeneity in groundwater responses to multiple factors is critical for water resource management in coastal cities.Daily groundwater depth(GWD)data from 43 wells(2018-2022)were collected in t... Understanding spatial heterogeneity in groundwater responses to multiple factors is critical for water resource management in coastal cities.Daily groundwater depth(GWD)data from 43 wells(2018-2022)were collected in three coastal cities in Jiangsu Province,China.Seasonal and Trend decomposition using Loess(STL)together with wavelet analysis and empirical mode decomposition were applied to identify tide-influenced wells while remaining wells were grouped by hierarchical clustering analysis(HCA).Machine learning models were developed to predict GWD,then their response to natural conditions and human activities was assessed by the Shapley Additive exPlanations(SHAP)method.Results showed that eXtreme Gradient Boosting(XGB)was superior to other models in terms of prediction performance and computational efficiency(R2>0.95).GWD in Yancheng and southern Lianyungang were greater than those in Nantong,exhibiting larger fluctuations.Groundwater within 5 km of the coastline was affected by tides,with more pronounced effects in agricultural areas compared to urban areas.Shallow groundwater(3-7 m depth)responded immediately(0-1 day)to rainfall,primarily influenced by farmland and topography(slope and distance from rivers).Rainfall recharge to groundwater peaked at 50%farmland coverage,but this effect was suppressed by high temperatures(>30℃)which intensified as distance from rivers increased,especially in forest and grassland.Deep groundwater(>10 m)showed delayed responses to rainfall(1-4 days)and temperature(10-15 days),with GDP as the primary influence,followed by agricultural irrigation and population density.Farmland helped to maintain stable GWD in low population density regions,while excessive farmland coverage(>90%)led to overexploitation.In the early stages of GDP development,increased industrial and agricultural water demand led to GWD decline,but as GDP levels significantly improved,groundwater consumption pressure gradually eased.This methodological framework is applicable not only to coastal cities in China but also could be extended to coastal regions worldwide. 展开更多
关键词 Groundwater depth Spatial heterogeneity Multiple influence factorsCoastal cities Machine Learning models SHAP values
Intelligent Suppression of Marine Seismic Multiples Using Deep Learning Methods 认领 引用 被引量：1
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作者 HU Guang LI Yan +4 位作者 YANG Shengxiong ZHANG Heng LIU Xin LI Yuanheng TIAN Dongmei 《Journal of Ocean University of China》 SCIE CAS CSCD 2025年第4期967-978,共12页
Multiple suppression is an important element of marine seismic data processing.Intelligent suppression of multiples us-ing artificial intelligence reduces labor costs,minimizes dependence on unknown prior information,... Multiple suppression is an important element of marine seismic data processing.Intelligent suppression of multiples us-ing artificial intelligence reduces labor costs,minimizes dependence on unknown prior information,and improves data processing ef-ficiency.In this study,we propose an intelligent method for suppressing marine seismic multiples using deep learning approaches.The proposed method enables the intelligent suppression of free-surface-related multiples from seismic records.Initially,we construct a multi-category marine seismic multiple dataset through finite difference forward modeling under different boundary conditions.We use various models and data augmentation methods,including sample rotation,noise addition,and random channel omission.Then,we apply depthwise separable convolution to develop our deep learning Mobilenet-Unet model.The Mobilenet-Unet framework sig-nificantly reduces the number of operations required for multiple elimination without sacrificing model performance,ultimately reali-zing the optimal multiple suppression model.The trained Mobilenet-Unet is applied to the test set for verification.Moreover,to deter-mine its generalization ability,it is implemented to seismic records containing multiples generated by two marine geophysical models that were not included in the training process.The performance of Mobilenet-Unet is also compared with that of different network structures.The results indicate that,despite its small size,our proposed Mobilenet-Unet deep learning model can rapidly and effective-ly separate multiples in marine seismic data,possessing reasonable generalization ability. 展开更多
关键词 multiple suppression marine seismic surveys artificial intelligence deep learning depthwise separable convolution
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Dual-frequency angular-multiplexed fringe projection profilometry with deep learning:breaking hardware limits for ultra-high-speed 3D imaging 认领 引用 被引量：2
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作者 Wenwu Chen Yifan Liu +8 位作者 Shijie Feng Wei Yin Jiaming Qian Yixuan Li Hang Zhang Maciej Trusiak Malgorzata Kujawinska Qian Chen Chao Zuo 《Opto-Electronic Advances》 SCIE EI CAS CSCD 2025年第9期15-24,共10页
Recent advancements in artificial intelligence have transformed three-dimensional(3D)optical imaging and metrology,enabling high-resolution and high-precision 3D surface geometry measurements from one single fringe pa... Recent advancements in artificial intelligence have transformed three-dimensional(3D)optical imaging and metrology,enabling high-resolution and high-precision 3D surface geometry measurements from one single fringe pattern projection.However,the imaging speed of conventional fringe projection profilometry(FPP)remains limited by the native sensor refresh rates due to the inherent"one-to-one"synchronization mechanism between pattern projection and image acquisition in standard structured light techniques.Here,we present dual-frequency angular-multiplexed fringe projection profilometry(DFAMFPP),a deep learning-enabled 3D imaging technique that achieves high-speed,high-precision,and large-depth-range absolute 3D surface measurements at speeds 16 times faster than the sensor's native frame rate.By encoding multi-timeframe 3D information into a single multiplexed image using multiple pairs of dual-frequency fringes,high-accuracy absolute phase maps are reconstructed using specially trained two-stage number-theoretical-based deep neural networks.We validate the effectiveness of DFAMFPP through dynamic scene measurements,achieving 10,000 Hz 3D imaging of a running turbofan engine prototype with only a 625 Hz camera.By overcoming the sensor hardware bottleneck,DFAMFPP significantly advances high-speed and ultra-high-speed 3D imaging,opening new avenues for exploring dynamic processes across diverse scientific disciplines. 展开更多
关键词 3D imaging fringe projection profilometry deep learning multiplex ultra-high-speed
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URLLC Service in UAV Rate-Splitting Multiple Access: Adapting Deep Learning Techniques for Wireless Network 认领 引用
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作者 Reem Alkanhel Abuzar B.M.Adam +3 位作者 Samia Allaoua Chelloug Dina S.M.Hassan Mohammed Saleh Ali Muthanna Ammar Muthanna 《Computers, Materials & Continua》 SCIE EI 2025年第7期607-624,共18页
The 3GPP standard defines the requirements for next-generation wireless networks,with particular attention to Ultra-Reliable Low-Latency Communications(URLLC),critical for applications such as Unmanned Aerial Vehicles... The 3GPP standard defines the requirements for next-generation wireless networks,with particular attention to Ultra-Reliable Low-Latency Communications(URLLC),critical for applications such as Unmanned Aerial Vehicles(UAVs).In this context,Non-Orthogonal Multiple Access(NOMA)has emerged as a promising technique to improve spectrum efficiency and user fairness by allowing multiple users to share the same frequency resources.However,optimizing key parameters–such as beamforming,rate allocation,and UAV trajectory–presents significant challenges due to the nonconvex nature of the problem,especially under stringent URLLC constraints.This paper proposes an advanced deep learning-driven approach to address the resulting complex optimization challenges.We formulate a downlink multiuser UAV,Rate-Splitting Multiple Access(RSMA),and Multiple Input Multiple Output(MIMO)system aimed at maximizing the achievable rate under stringent constraints,including URLLC quality-of-service(QoS),power budgets,rate allocations,and UAV trajectory limitations.Due to the highly nonconvex nature of the optimization problem,we introduce a novel distributed deep reinforcement learning(DRL)framework based on dual-agent deep deterministic policy gradient(DA-DDPG).The proposed framework leverages inception-inspired and deep unfolding architectures to improve feature extraction and convergence in beamforming and rate allocation.For UAV trajectory optimization,we design a dedicated actor-critic agent using a fully connected deep neural network(DNN),further enhanced through incremental learning.Simulation results validate the effectiveness of our approach,demonstrating significant performance gains over existing methods and confirming its potential for real-time URLLC in next-generation UAV communication networks. 展开更多
关键词 Deep learning quality-of-service(QoS) rate-splitting multiple access(RSMA) unmanned aerial vehicle(UAV) ultra-reliable low-latency communication(URLLC)
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A Survey of Federated Learning:Advances in Architecture,Synchronization,and Security Threats 认领 引用 被引量：1
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作者 Faisal Mahmud Fahim Mahmud Rashedur M.Rahman 《Computers, Materials & Continua》 SCIE EI 2026年第3期1-87,共87页
Federated Learning(FL)has become a leading decentralized solution that enables multiple clients to train a model in a collaborative environment without directly sharing raw data,making it suitable for privacy-sensitiv... Federated Learning(FL)has become a leading decentralized solution that enables multiple clients to train a model in a collaborative environment without directly sharing raw data,making it suitable for privacy-sensitive applications such as healthcare,finance,and smart systems.As the field continues to evolve,the research field has become more complex and scattered,covering different system designs,training methods,and privacy techniques.This survey is organized around the three core challenges:how the data is distributed,how models are synchronized,and how to defend against attacks.It provides a structured and up-to-date review of FL research from 2023 to 2025,offering a unified taxonomy that categorizes works by data distribution(Horizontal FL,Vertical FL,Federated Transfer Learning,and Personalized FL),training synchronization(synchronous and asynchronous FL),optimization strategies,and threat models(data leakage and poisoning attacks).In particular,we summarize the latest contributions in Vertical FL frameworks for secure multi-party learning,communication-efficient Horizontal FL,and domain-adaptive Federated Transfer Learning.Furthermore,we examine synchronization techniques addressing system heterogeneity,including straggler mitigation in synchronous FL and staleness management in asynchronous FL.The survey covers security threats in FL,such as gradient inversion,membership inference,and poisoning attacks,as well as their defense strategies that include privacy-preserving aggregation and anomaly detection.The paper concludes by outlining unresolved issues and highlighting challenges in handling personalized models,scalability,and real-world adoption. 展开更多
关键词 Federated learning(FL) horizontal federated learning(HFL) vertical federated learning(VFL) federated transfer learning(FTL) personalized federated learning synchronous federated learning(SFL) asynchronous federated learning(AFL) data leakage poisoning attacks privacy-preserving machine learning
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Blood-based machine learning classifiers for early diagnosis of gastric cancer via multiple miRNAs 认领 引用 被引量：1
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作者 Fu-Chao Ma Guan-Lan Zhang +7 位作者 Bang-Teng Chi Yu-Lu Tang Wei Peng Ai-Qun Liu Gang Chen Jin-Biao Gao Dan-Ming Wei Lian-Ying Ge 《World Journal of Gastrointestinal Oncology》 SCIE 2025年第4期416-426,共11页
BACKGROUND Early screening methods for gastric cancer(GC)are lacking;therefore,the disease often progresses to an advanced stage when patients first start to exhibit typical symptoms.Endoscopy and pathological biopsy ... BACKGROUND Early screening methods for gastric cancer(GC)are lacking;therefore,the disease often progresses to an advanced stage when patients first start to exhibit typical symptoms.Endoscopy and pathological biopsy remain the primary diagnostic approaches,but they are invasive and not yet widely applicable for early popu-lation screening.miRNA is a highly conserved type of RNA that exists stably in plasma.Dysfunction of miRNA is linked to tumorigenesis and progression,indicating that individual miRNAs or combinations of multiple miRNAs may serve as potential biomarkers.AIM To identify effective plasma miRNA biomarkers and investigate the clinical value of combining multiple miRNAs for early detection of GC.METHODS Plasma samples from multiple centres were collected.Differentially expressed genes among healthy controls,early-stage GC patients,and advanced-stage GC patients were identified through small RNA sequencing(sRNA-seq)and validated via real-time quantitative reverse transcription polymerase chain reaction(RT-qPCR).A Wilcoxon signed-rank test was used to investigate the differences in miRNAs.Sequencing datasets of GC serum samples were retrieved from the Gene Expression Omnibus(GEO),ArrayExpress,and The Cancer Genome Atlas databases,and a multilayer perceptron-artificial neural network(MLP-ANN)model was constructed for the key risk miRNAs.The pROC package was used to assess the discriminatory efficacy of the model.RESULTS Plasma samples of 107 normal,71 early GC and 97 advanced GC patients were obtained from three centres,and serum samples of 8443 normal and 1583 GC patients were obtained from the GEO database.The sRNA-seq and RT-qPCR experiments revealed that miR-452-5p,miR-5010-5p,miR-27b-5p,miR-5189-5p,miR-552-5p and miR-199b-5p were significantly increased in early GC patients compared with healthy controls and in advanced GC patients compared with early GC patients(P<0.05).An MLP-ANN model was constructed for the six key miRNAs.The area under the curve(AUC)within the training cohort was 0.983[95% confidence interval(CI):0.980–0.986].In the two validation cohorts,the AUCs were 0.995(95%CI:0.987 to nearly 1.000)and 0.979(95%CI:0.972–0.986),respectively.CONCLUSION Potential miRNA biomarkers,including miR-452-5p,miR-5010-5p,miR-27b-5p,miR-5189-5p,miR-552-5p and miR-199b-5p,were identified.A GC classifier based on these miRNAs was developed,benefiting early detection and population screening. 展开更多
关键词 Gastric cancer miRNA Biological marker Machine learning Serum
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Graph-based multi-agent reinforcement learning for collaborative search and tracking of multiple UAVs 认领 引用 被引量：5
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作者 Bocheng ZHAO Mingying HUO +4 位作者 Zheng LI Wenyu FENG Ze YU Naiming QI Shaohai WANG 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2025年第3期109-123,共15页
This paper investigates the challenges associated with Unmanned Aerial Vehicle (UAV) collaborative search and target tracking in dynamic and unknown environments characterized by limited field of view. The primary obj... This paper investigates the challenges associated with Unmanned Aerial Vehicle (UAV) collaborative search and target tracking in dynamic and unknown environments characterized by limited field of view. The primary objective is to explore the unknown environments to locate and track targets effectively. To address this problem, we propose a novel Multi-Agent Reinforcement Learning (MARL) method based on Graph Neural Network (GNN). Firstly, a method is introduced for encoding continuous-space multi-UAV problem data into spatial graphs which establish essential relationships among agents, obstacles, and targets. Secondly, a Graph AttenTion network (GAT) model is presented, which focuses exclusively on adjacent nodes, learns attention weights adaptively and allows agents to better process information in dynamic environments. Reward functions are specifically designed to tackle exploration challenges in environments with sparse rewards. By introducing a framework that integrates centralized training and distributed execution, the advancement of models is facilitated. Simulation results show that the proposed method outperforms the existing MARL method in search rate and tracking performance with less collisions. The experiments show that the proposed method can be extended to applications with a larger number of agents, which provides a potential solution to the challenging problem of multi-UAV autonomous tracking in dynamic unknown environments. 展开更多
关键词 Unmanned aerial vehicle(UAV) Multi-agent reinforcement learning(MARL) Graph attention network(GAT) Tracking Dynamic and unknown environment
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A Distributed Cooperative Dynamic Task Planning Algorithm for Multiple Satellites Based on Multi-agent Hybrid Learning 认领 引用 被引量：18
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作者 WANG Chong LI Jun +2 位作者 JING Ning WANG Jun CHEN Hao 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2011年第4期493-505,共13页
Traditionally,heuristic re-planning algorithms are used to tackle the problem of dynamic task planning for multiple satellites.However,the traditional heuristic strategies depend on the concrete tasks,which often affe... Traditionally,heuristic re-planning algorithms are used to tackle the problem of dynamic task planning for multiple satellites.However,the traditional heuristic strategies depend on the concrete tasks,which often affect the result’s optimality.Noticing that the historical information of cooperative task planning will impact the latter planning results,we propose a hybrid learning algorithm for dynamic multi-satellite task planning,which is based on the multi-agent reinforcement learning of policy iteration and the transfer learning.The reinforcement learning strategy of each satellite is described with neural networks.The policy neural network individuals with the best topological structure and weights are found by applying co-evolutionary search iteratively.To avoid the failure of the historical learning caused by the randomly occurring observation requests,a novel approach is proposed to balance the quality and efficiency of the task planning,which converts the historical learning strategy to the current initial learning strategy by applying the transfer learning algorithm.The simulations and analysis show the feasibility and adaptability of the proposed approach especially for the situation with randomly occurring observation requests. 展开更多
关键词 multiple satellites dynamic task planning problem multi-agent systems reinforcement learning neuroevolution of augmenting topologies transfer learning
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Forecasting Solar Energy Production across Multiple Sites Using Deep Learning 认领 引用 被引量：1
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作者 Samira Marhraoui Basma Saad +2 位作者 Hassan Silkan Said Laasri Asmaa El Hannani 《Energy Engineering》 EI 2025年第7期2653-2672,共20页
Photovoltaic(PV)power forecasting is essential for balancing energy supply and demand in renewable energy systems.However,the performance of PV panels varies across different technologies due to differences in efficie... Photovoltaic(PV)power forecasting is essential for balancing energy supply and demand in renewable energy systems.However,the performance of PV panels varies across different technologies due to differences in efficiency and how they process solar radiation.This study evaluates the effectiveness of deep learning models in predicting PV power generation for three panel technologies:Hybrid-Si,Mono-Si,and Poly-Si,across three forecasting horizons:1-step,12-step,and 24-step.Among the tested models,the Convolutional Neural Network—Long Short-Term Memory(CNN-LSTM)architecture exhibited superior performance,particularly for the 24-step horizon,achieving R2=0.9793 and MAE 0.0162 for the Poly-Si array,followed by Mono-Si(R2=0.9768)and Hybrid-Si arrays(R2=0.9769).These findings demonstrate that the CNN-LSTM model can provide accurate and reliable PV power predictions for all studied technologies.By identifying the most suitable predictive model for each panel technology,this study contributes to optimizing PV power forecasting and improving energy management strategies. 展开更多
关键词 CNN-LSTM deep learning models forecasting horizons PV energy prediction accuracy solar panel technologies
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CoLM2S:Contrastive self‐supervised learning on attributed multiplex graph network with multi‐scale information 认领 引用 被引量：2
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作者 Beibei Han Yingmei Wei +1 位作者 Qingyong Wang Shanshan Wan 《CAAI Transactions on Intelligence Technology》 SCIE EI 2023年第4期1464-1479,共16页
Contrastive self‐supervised representation learning on attributed graph networks with Graph Neural Networks has attracted considerable research interest recently.However,there are still two challenges.First,most of t... Contrastive self‐supervised representation learning on attributed graph networks with Graph Neural Networks has attracted considerable research interest recently.However,there are still two challenges.First,most of the real‐word system are multiple relations,where entities are linked by different types of relations,and each relation is a view of the graph network.Second,the rich multi‐scale information(structure‐level and feature‐level)of the graph network can be seen as self‐supervised signals,which are not fully exploited.A novel contrastive self‐supervised representation learning framework on attributed multiplex graph networks with multi‐scale(named CoLM2S)information is presented in this study.It mainly contains two components:intra‐relation contrast learning and interrelation contrastive learning.Specifically,the contrastive self‐supervised representation learning framework on attributed single‐layer graph networks with multi‐scale information(CoLMS)framework with the graph convolutional network as encoder to capture the intra‐relation information with multi‐scale structure‐level and feature‐level selfsupervised signals is introduced first.The structure‐level information includes the edge structure and sub‐graph structure,and the feature‐level information represents the output of different graph convolutional layer.Second,according to the consensus assumption among inter‐relations,the CoLM2S framework is proposed to jointly learn various graph relations in attributed multiplex graph network to achieve global consensus node embedding.The proposed method can fully distil the graph information.Extensive experiments on unsupervised node clustering and graph visualisation tasks demonstrate the effectiveness of our methods,and it outperforms existing competitive baselines. 展开更多
关键词 attributed multiplex graph network contrastive self‐supervised learning graph representation learning multiscale information
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Learning Manipulation from Expert Demonstrations Based on Multiple Data Associations and Physical Constraints 认领 引用
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作者 Yangqing Ye Yaojie Mao +5 位作者 Shiming Qiu Chuan’guo Tang Zhirui Pan Weiwei Wan Shibo Cai Guanjun Bao 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2025年第2期279-294,共16页
Learning from demonstration is widely regarded as a promising paradigm for robots to acquire diverse skills.Other than the artificial learning from observation-action pairs for machines,humans can learn to imitate in ... Learning from demonstration is widely regarded as a promising paradigm for robots to acquire diverse skills.Other than the artificial learning from observation-action pairs for machines,humans can learn to imitate in a more versatile and effective manner:acquiring skills through mere“observation”.Video to Command task is widely perceived as a promising approach for task-based learning,which yet faces two key challenges:(1)High redundancy and low frame rate of fine-grained action sequences make it difficult to manipulate objects robustly and accurately.(2)Video to Command models often prioritize accuracy and richness of output commands over physical capabilities,leading to impractical or unsafe instructions for robots.This article presents a novel Video to Command framework that employs multiple data associations and physical constraints.First,we introduce an object-level appearancecontrasting multiple data association strategy to effectively associate manipulated objects in visually complex environments,capturing dynamic changes in video content.Then,we propose a multi-task Video to Command model that utilizes object-level video content changes to compile expert demonstrations into manipulation commands.Finally,a multi-task hybrid loss function is proposed to train a Video to Command model that adheres to the constraints of the physical world and manipulation tasks.Our method achieved over 10%on BLEU_N,METEOR,ROUGE_L,and CIDEr compared to the up-to-date methods.The dual-arm robot prototype was established to demonstrate the whole process of learning from an expert demonstration of multiple skills and then executing the tasks by a robot. 展开更多
关键词 Videos to command Multiple data associations Multi-task model Multi-task hybrid loss function Physical constraints
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