Traditional anomaly detection methods often assume that data points are independent or exhibit regularly structured relationships,as in Euclidean data such as time series or image grids.However,real-world data frequen...Traditional anomaly detection methods often assume that data points are independent or exhibit regularly structured relationships,as in Euclidean data such as time series or image grids.However,real-world data frequently involve irregular,interconnected structures,requiring a shift toward non-Euclidean approaches.This study introduces a novel anomaly detection framework designed to handle non-Euclidean data by modeling transactions as graph signals.By leveraging graph convolution filters,we extract meaningful connection strengths that capture relational dependencies often overlooked in traditional methods.Utilizing the Graph Convolutional Networks(GCN)framework,we integrate graph-based embeddings with conventional anomaly detection models,enhancing performance through relational insights.Ourmethod is validated on European credit card transaction data,demonstrating its effectiveness in detecting fraudulent transactions,particularly thosewith subtle patterns that evade traditional,amountbased detection techniques.The results highlight the advantages of incorporating temporal and structural dependencies into fraud detection,showcasing the robustness and applicability of our approach in complex,real-world scenarios.展开更多
The increasing number of interconnected devices and the incorporation of smart technology into contemporary healthcare systems have significantly raised the attack surface of cyber threats.The early detection of threa...The increasing number of interconnected devices and the incorporation of smart technology into contemporary healthcare systems have significantly raised the attack surface of cyber threats.The early detection of threats is both necessary and complex,yet these interconnected healthcare settings generate enormous amounts of heterogeneous data.Traditional Intrusion Detection Systems(IDS),which are generally centralized and machine learning-based,often fail to address the rapidly changing nature of cyberattacks and are challenged by ethical concerns related to patient data privacy.Moreover,traditional AI-driven IDS usually face challenges in handling large-scale,heterogeneous healthcare data while ensuring data privacy and operational efficiency.To address these issues,emerging technologies such as Big Data Analytics(BDA)and Federated Learning(FL)provide a hybrid framework for scalable,adaptive intrusion detection in IoT-driven healthcare systems.Big data techniques enable processing large-scale,highdimensional healthcare data,and FL can be used to train a model in a decentralized manner without transferring raw data,thereby maintaining privacy between institutions.This research proposes a privacy-preserving Federated Learning–based model that efficiently detects cyber threats in connected healthcare systems while ensuring distributed big data processing,privacy,and compliance with ethical regulations.To strengthen the reliability of the reported findings,the resultswere validated using cross-dataset testing and 95%confidence intervals derived frombootstrap analysis,confirming consistent performance across heterogeneous healthcare data distributions.This solution takes a significant step toward securing next-generation healthcare infrastructure by combining scalability,privacy,adaptability,and earlydetection capabilities.The proposed global model achieves a test accuracy of 99.93%±0.03(95%CI)and amiss-rate of only 0.07%±0.02,representing state-of-the-art performance in privacy-preserving intrusion detection.The proposed FL-driven IDS framework offers an efficient,privacy-preserving,and scalable solution for securing next-generation healthcare infrastructures by combining adaptability,early detection,and ethical data management.展开更多
Modern intrusion detection systems(MIDS)face persistent challenges in coping with the rapid evolution of cyber threats,high-volume network traffic,and imbalanced datasets.Traditional models often lack the robustness a...Modern intrusion detection systems(MIDS)face persistent challenges in coping with the rapid evolution of cyber threats,high-volume network traffic,and imbalanced datasets.Traditional models often lack the robustness and explainability required to detect novel and sophisticated attacks effectively.This study introduces an advanced,explainable machine learning framework for multi-class IDS using the KDD99 and IDS datasets,which reflects real-world network behavior through a blend of normal and diverse attack classes.The methodology begins with sophisticated data preprocessing,incorporating both RobustScaler and QuantileTransformer to address outliers and skewed feature distributions,ensuring standardized and model-ready inputs.Critical dimensionality reduction is achieved via the Harris Hawks Optimization(HHO)algorithm—a nature-inspired metaheuristic modeled on hawks’hunting strategies.HHO efficiently identifies the most informative features by optimizing a fitness function based on classification performance.Following feature selection,the SMOTE is applied to the training data to resolve class imbalance by synthetically augmenting underrepresented attack types.The stacked architecture is then employed,combining the strengths of XGBoost,SVM,and RF as base learners.This layered approach improves prediction robustness and generalization by balancing bias and variance across diverse classifiers.The model was evaluated using standard classification metrics:precision,recall,F1-score,and overall accuracy.The best overall performance was recorded with an accuracy of 99.44%for UNSW-NB15,demonstrating the model’s effectiveness.After balancing,the model demonstrated a clear improvement in detecting the attacks.We tested the model on four datasets to show the effectiveness of the proposed approach and performed the ablation study to check the effect of each parameter.Also,the proposed model is computationaly efficient.To support transparency and trust in decision-making,explainable AI(XAI)techniques are incorporated that provides both global and local insight into feature contributions,and offers intuitive visualizations for individual predictions.This makes it suitable for practical deployment in cybersecurity environments that demand both precision and accountability.展开更多
Multivariate anomaly detection plays a critical role in maintaining the stable operation of information systems.However,in existing research,multivariate data are often influenced by various factors during the data co...Multivariate anomaly detection plays a critical role in maintaining the stable operation of information systems.However,in existing research,multivariate data are often influenced by various factors during the data collection process,resulting in temporal misalignment or displacement.Due to these factors,the node representations carry substantial noise,which reduces the adaptability of the multivariate coupled network structure and subsequently degrades anomaly detection performance.Accordingly,this study proposes a novel multivariate anomaly detection model grounded in graph structure learning.Firstly,a recommendation strategy is employed to identify strongly coupled variable pairs,which are then used to construct a recommendation-driven multivariate coupling network.Secondly,a multi-channel graph encoding layer is used to dynamically optimize the structural properties of the multivariate coupling network,while a multi-head attention mechanism enhances the spatial characteristics of the multivariate data.Finally,unsupervised anomaly detection is conducted using a dynamic threshold selection algorithm.Experimental results demonstrate that effectively integrating the structural and spatial features of multivariate data significantly mitigates anomalies caused by temporal dependency misalignment.展开更多
In modern industrial production,foreign object detection in complex environments is crucial to ensure product quality and production safety.Detection systems based on deep-learning image processing algorithms often fa...In modern industrial production,foreign object detection in complex environments is crucial to ensure product quality and production safety.Detection systems based on deep-learning image processing algorithms often face challenges with handling high-resolution images and achieving accurate detection against complex backgrounds.To address these issues,this study employs the PatchCore unsupervised anomaly detection algorithm combined with data augmentation techniques to enhance the system’s generalization capability across varying lighting conditions,viewing angles,and object scales.The proposed method is evaluated in a complex industrial detection scenario involving the bogie of an electric multiple unit(EMU).A dataset consisting of complex backgrounds,diverse lighting conditions,and multiple viewing angles is constructed to validate the performance of the detection system in real industrial environments.Experimental results show that the proposed model achieves an average area under the receiver operating characteristic curve(AUROC)of 0.92 and an average F1 score of 0.85.Combined with data augmentation,the proposed model exhibits improvements in AUROC by 0.06 and F1 score by 0.03,demonstrating enhanced accuracy and robustness for foreign object detection in complex industrial settings.In addition,the effects of key factors on detection performance are systematically analyzed,providing practical guidance for parameter selection in real industrial applications.展开更多
Social bots are automated programs designed to spread rumors and misinformation,posing significant threats to online security.Existing research shows that the structure of a social network significantly affects the be...Social bots are automated programs designed to spread rumors and misinformation,posing significant threats to online security.Existing research shows that the structure of a social network significantly affects the behavioral patterns of social bots:a higher number of connected components weakens their collaborative capabilities,thereby reducing their proportion within the overall network.However,current social bot detection methods still make limited use of topological features.Furthermore,both graph neural network(GNN)-based methods that rely on local features and those that leverage global features suffer from their own limitations,and existing studies lack an effective fusion of multi-scale information.To address these issues,this paper proposes a topology-aware multi-scale social bot detection method,which jointly learns local and global representations through a co-training mechanism.At the local level,topological features are effectively embedded into node representations,enhancing expressiveness while alleviating the over-smoothing problem in GNNs.At the global level,a clustering attention mechanism is introduced to learn global node representations,mitigating the over-globalization problem.Experimental results demonstrate that our method effectively overcomes the limitations of single-scale approaches.Our code is publicly available at https://anonymous.4open.science/r/TopoMSG-2C41/(accessed on 27 October 2025).展开更多
Lateral movement represents the most covert and critical phase of Advanced Persistent Threats(APTs),and its detection still faces two primary challenges:sample scarcity and“cold start”of new entities.To address thes...Lateral movement represents the most covert and critical phase of Advanced Persistent Threats(APTs),and its detection still faces two primary challenges:sample scarcity and“cold start”of new entities.To address these challenges,we propose an Uncertainty-Driven Graph Embedding-Enhanced Lateral Movement Detection framework(UGEA-LMD).First,the framework employs event-level incremental encoding on a continuous-time graph to capture fine-grained behavioral evolution,enabling newly appearing nodes to retain temporal contextual awareness even in the absence of historical interactions and thereby fundamentally mitigating the cold-start problem.Second,in the embedding space,we model the dependency structure among feature dimensions using a Gaussian copula to quantify the uncertainty distribution,and generate augmented samples with consistent structural and semantic properties through adaptive sampling,thus expanding the representation space of sparse samples and enhancing the model’s generalization under sparse sample conditions.Unlike static graph methods that cannot model temporal dependencies or data augmentation techniques that depend on predefined structures,UGEA-LMD offers both superior temporaldynamic modeling and structural generalization.Experimental results on the large-scale LANL log dataset demonstrate that,under the transductive setting,UGEA-LMD achieves an AUC of 0.9254;even when 10%of nodes or edges are withheld during training,UGEA-LMD significantly outperforms baseline methods on metrics such as recall and AUC,confirming its robustness and generalization capability in sparse-sample and cold-start scenarios.展开更多
The exponential expansion of the Internet of Things(IoT),Industrial Internet of Things(IIoT),and Transportation Management of Things(TMoT)produces vast amounts of real-time streaming data.Ensuring system dependability...The exponential expansion of the Internet of Things(IoT),Industrial Internet of Things(IIoT),and Transportation Management of Things(TMoT)produces vast amounts of real-time streaming data.Ensuring system dependability,operational efficiency,and security depends on the identification of anomalies in these dynamic and resource-constrained systems.Due to their high computational requirements and inability to efficiently process continuous data streams,traditional anomaly detection techniques often fail in IoT systems.This work presents a resource-efficient adaptive anomaly detection model for real-time streaming data in IoT systems.Extensive experiments were carried out on multiple real-world datasets,achieving an average accuracy score of 96.06%with an execution time close to 7.5 milliseconds for each individual streaming data point,demonstrating its potential for real-time,resourceconstrained applications.The model uses Principal Component Analysis(PCA)for dimensionality reduction and a Z-score technique for anomaly detection.It maintains a low computational footprint with a sliding window mechanism,enabling incremental data processing and identification of both transient and sustained anomalies without storing historical data.The system uses a Multivariate Linear Regression(MLR)based imputation technique that estimates missing or corrupted sensor values,preserving data integrity prior to anomaly detection.The suggested solution is appropriate for many uses in smart cities,industrial automation,environmental monitoring,IoT security,and intelligent transportation systems,and is particularly well-suited for resource-constrained edge devices.展开更多
With the rapid development of the industrial Internet,the network security environment has become increasingly complex and variable.Intrusion detection,a core technology for ensuring the security of industrial control...With the rapid development of the industrial Internet,the network security environment has become increasingly complex and variable.Intrusion detection,a core technology for ensuring the security of industrial control systems,faces the challenge of unbalanced data samples,particularly the low detection rates for minority class attack samples.Therefore,this paper proposes a data enhancement method for intrusion detection in the industrial Internet based on a Self-Attention Wasserstein Generative Adversarial Network(SA-WGAN)to address the low detection rates of minority class attack samples in unbalanced intrusion detection scenarios.The proposed method integrates a selfattention mechanism with a Wasserstein Generative Adversarial Network(WGAN).The self-attention mechanism automatically learns important features from the input data and assigns different weights to emphasize the key features related to intrusion behaviors,providing strong guidance for subsequent data generation.The WGAN generates new data samples through adversarial training to expand the original dataset.In the SA-WGAN framework,the WGAN directs the data generation process based on the key features extracted by the self-attention mechanism,ensuring that the generated samples exhibit both diversity and similarity to real data.Experimental results demonstrate that the SA-WGAN-based data enhancement method significantly improves detection performance for attack samples from minority classes,addresses issues of insufficient data and category imbalance,and enhances the generalization ability and overall performance of the intrusion detection model.展开更多
Rotating machinery is critical to industrial systems,necessitating robust anomaly detection(AD)to ensure operational safety and prevent failures.However,in real-world scenarios,monitoring data is typically unlabeled a...Rotating machinery is critical to industrial systems,necessitating robust anomaly detection(AD)to ensure operational safety and prevent failures.However,in real-world scenarios,monitoring data is typically unlabeled and often consists of normal samples contaminated with a small proportion of unknown anomalies.To address this,this paper proposes a diffusion-based AD method,Anomaly Detection Denoising Diffusion Probabilistic Model(AD-DDPM)for robust AD.The method employs a U-attention-net to capture local and global features and introduces a filtered contrastive mechanism to mitigate the impact of contaminated training data.By leveraging the probabilistic nature of diffusion models,AD-DDPM effectively models normal data distributions,achieving superior AD even with polluted samples.Experimental validation on fault simulation datasets demonstrates the method’s exceptional performance,outperforming traditional machine learning and deep learning baselines.The proposed approach offers a promising solution for reliable health monitoring in industrial settings.展开更多
Data augmentation plays an important role in boosting the performance of 3D models,while very few studies handle the 3D point cloud data with this technique.Global augmentation and cut-paste are commonly used augmenta...Data augmentation plays an important role in boosting the performance of 3D models,while very few studies handle the 3D point cloud data with this technique.Global augmentation and cut-paste are commonly used augmentation techniques for point clouds,where global augmentation is applied to the entire point cloud of the scene,and cut-paste samples objects from other frames into the current frame.Both types of data augmentation can improve performance,but the cut-paste technique cannot effectively deal with the occlusion relationship between the foreground object and the background scene and the rationality of object sampling,which may be counterproductive and may hurt the overall performance.In addition,LiDAR is susceptible to signal loss,external occlusion,extreme weather and other factors,which can easily cause object shape changes,while global augmentation and cut-paste cannot effectively enhance the robustness of the model.To this end,we propose Syn-Aug,a synchronous data augmentation framework for LiDAR-based 3D object detection.Specifically,we first propose a novel rendering-based object augmentation technique(Ren-Aug)to enrich training data while enhancing scene realism.Second,we propose a local augmentation technique(Local-Aug)to generate local noise by rotating and scaling objects in the scene while avoiding collisions,which can improve generalisation performance.Finally,we make full use of the structural information of 3D labels to make the model more robust by randomly changing the geometry of objects in the training frames.We verify the proposed framework with four different types of 3D object detectors.Experimental results show that our proposed Syn-Aug significantly improves the performance of various 3D object detectors in the KITTI and nuScenes datasets,proving the effectiveness and generality of Syn-Aug.On KITTI,four different types of baseline models using Syn-Aug improved mAP by 0.89%,1.35%,1.61%and 1.14%respectively.On nuScenes,four different types of baseline models using Syn-Aug improved mAP by 14.93%,10.42%,8.47%and 6.81%respectively.The code is available at https://github.com/liuhuaijjin/Syn-Aug.展开更多
Seismic data plays a pivotal role in fault detection,offering critical insights into subsurface structures and seismic hazards.Understanding fault detection from seismic data is essential for mitigating seismic risks ...Seismic data plays a pivotal role in fault detection,offering critical insights into subsurface structures and seismic hazards.Understanding fault detection from seismic data is essential for mitigating seismic risks and guiding land-use plans.This paper presents a comprehensive review of existing methodologies for fault detection,focusing on the application of Machine Learning(ML)and Deep Learning(DL)techniques to enhance accuracy and efficiency.Various ML and DL approaches are analyzed with respect to fault segmentation,adaptive learning,and fault detection models.These techniques,benchmarked against established seismic datasets,reveal significant improvements over classical methods in terms of accuracy and computational efficiency.Additionally,this review highlights emerging trends,including hybrid model applications and the integration of real-time data processing for seismic fault detection.By providing a detailed comparative analysis of current methodologies,this review aims to guide future research and foster advancements in the effectiveness and reliability of seismic studies.Ultimately,the study seeks to bridge the gap between theoretical investigations and practical implementations in fault detection.展开更多
Anomaly detection is an important task for maintaining the performance of cloud data center.Traditional anomaly detection primarily examines individual Virtual Machine(VM)behavior,neglecting the impact of interactions...Anomaly detection is an important task for maintaining the performance of cloud data center.Traditional anomaly detection primarily examines individual Virtual Machine(VM)behavior,neglecting the impact of interactions among multiple VMs on Key Performance Indicator(KPI)data,e.g.,memory utilization.Furthermore,the nonstationarity,high complexity,and uncertain periodicity of KPI data in VM also bring difficulties to deep learningbased anomaly detection tasks.To settle these challenges,this paper proposes MCBiWGAN-GTN,a multi-channel semi-supervised time series anomaly detection algorithm based on the Bidirectional Wasserstein Generative Adversarial Network with Graph-Time Network(BiWGAN-GTN)and the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN).(a)The BiWGAN-GTN algorithm is proposed to extract spatiotemporal information from data.(b)The loss function of BiWGAN-GTN is redesigned to solve the abnormal data intrusion problem during the training process.(c)MCBiWGAN-GTN is designed to reduce data complexity through CEEMDAN for time series decomposition and utilizes BiWGAN-GTN to train different components.(d)To adapt the proposed algorithm for the entire cloud data center,a cloud data center anomaly detection framework based on Swarm Learning(SL)is designed.The evaluation results on a real-world cloud data center dataset show that MCBiWGAN-GTN outperforms the baseline,with an F1-score of 0.96,an accuracy of 0.935,a precision of 0.954,a recall of 0.967,and an FPR of 0.203.The experiments also verify the stability of MCBiWGAN-GTN,the impact of parameter configurations,and the effectiveness of the proposed SL framework.展开更多
Dear Editor,Health management is essential to ensure battery performance and safety, while data-driven learning system is a promising solution to enable efficient state of health(SoH) estimation of lithium-ion(Liion) ...Dear Editor,Health management is essential to ensure battery performance and safety, while data-driven learning system is a promising solution to enable efficient state of health(SoH) estimation of lithium-ion(Liion) batteries. However, the time-consuming signal data acquisition and the lack of interpretability of model still hinder its efficient deployment. Motivated by this, this letter proposes a novel and interpretable data-driven learning strategy through combining the benefits of explainable AI and non-destructive ultrasonic detection for battery SoH estimation. Specifically, after equipping battery with advanced ultrasonic sensor to promise fast real-time ultrasonic signal measurement, an interpretable data-driven learning strategy named generalized additive neural decision ensemble(GANDE) is designed to rapidly estimate battery SoH and explain the effects of the involved ultrasonic features of interest.展开更多
A deep-sea riser is a crucial component of the mining system used to lift seafloor mineral resources to the vessel.Even minor damage to the riser can lead to substantial financial losses,environmental impacts,and safe...A deep-sea riser is a crucial component of the mining system used to lift seafloor mineral resources to the vessel.Even minor damage to the riser can lead to substantial financial losses,environmental impacts,and safety hazards.However,identifying modal parameters for structural health monitoring remains a major challenge due to its large deformations and flexibility.Vibration signal-based methods are essential for detecting damage and enabling timely maintenance to minimize losses.However,accurately extracting features from one-dimensional(1D)signals is often hindered by various environmental factors and measurement noises.To address this challenge,a novel approach based on a residual convolutional auto-encoder(RCAE)is proposed for detecting damage in deep-sea mining risers,incorporating a data fusion strategy.First,principal component analysis(PCA)is applied to reduce environmental fluctuations and fuse multisensor strain readings.Subsequently,a 1D-RCAE is used to extract damage-sensitive features(DSFs)from the fused dataset.A Mahalanobis distance indicator is established to compare the DSFs of the testing and healthy risers.The specific threshold for these distances is determined using the 3σcriterion,which is employed to assess whether damage has occurred in the testing riser.The effectiveness and robustness of the proposed approach are verified through numerical simulations of a 500-m riser and experimental tests on a 6-m riser.Moreover,the impact of contaminated noise and environmental fluctuations is examined.Results show that the proposed PCA-1D-RCAE approach can effectively detect damage and is resilient to measurement noise and environmental fluctuations.The accuracy exceeds 98%under noise-free conditions and remains above 90%even with 10 dB noise.This novel approach has the potential to establish a new standard for evaluating the health and integrity of risers during mining operations,thereby reducing the high costs and risks associated with failures.Maintenance activities can be scheduled more efficiently by enabling early and accurate detection of riser damage,minimizing downtime and avoiding catastrophic failures.展开更多
Semantic communication(SemCom)aims to achieve high-fidelity information delivery under low communication consumption by only guaranteeing semantic accuracy.Nevertheless,semantic communication still suffers from unexpe...Semantic communication(SemCom)aims to achieve high-fidelity information delivery under low communication consumption by only guaranteeing semantic accuracy.Nevertheless,semantic communication still suffers from unexpected channel volatility and thus developing a re-transmission mechanism(e.g.,hybrid automatic repeat request[HARQ])becomes indispensable.In that regard,instead of discarding previously transmitted information,the incremental knowledge-based HARQ(IK-HARQ)is deemed as a more effective mechanism that could sufficiently utilize the information semantics.However,considering the possible existence of semantic ambiguity in image transmission,a simple bit-level cyclic redundancy check(CRC)might compromise the performance of IK-HARQ.Therefore,there emerges a strong incentive to revolutionize the CRC mechanism,thus more effectively reaping the benefits of both SemCom and HARQ.In this paper,built on top of swin transformer-based joint source-channel coding(JSCC)and IK-HARQ,we propose a semantic image transmission framework SC-TDA-HARQ.In particular,different from the conventional CRC,we introduce a topological data analysis(TDA)-based error detection method,which capably digs out the inner topological and geometric information of images,to capture semantic information and determine the necessity for re-transmission.Extensive numerical results validate the effectiveness and efficiency of the proposed SC-TDA-HARQ framework,especially under the limited bandwidth condition,and manifest the superiority of TDA-based error detection method in image transmission.展开更多
The integration of the Internet of Things(IoT)into healthcare systems improves patient care,boosts operational efficiency,and contributes to cost-effective healthcare delivery.However,overcoming several associated cha...The integration of the Internet of Things(IoT)into healthcare systems improves patient care,boosts operational efficiency,and contributes to cost-effective healthcare delivery.However,overcoming several associated challenges,such as data security,interoperability,and ethical concerns,is crucial to realizing the full potential of IoT in healthcare.Real-time anomaly detection plays a key role in protecting patient data and maintaining device integrity amidst the additional security risks posed by interconnected systems.In this context,this paper presents a novelmethod for healthcare data privacy analysis.The technique is based on the identification of anomalies in cloud-based Internet of Things(IoT)networks,and it is optimized using explainable artificial intelligence.For anomaly detection,the Radial Boltzmann Gaussian Temporal Fuzzy Network(RBGTFN)is used in the process of doing information privacy analysis for healthcare data.Remora Colony SwarmOptimization is then used to carry out the optimization of the network.The performance of the model in identifying anomalies across a variety of healthcare data is evaluated by an experimental study.This evaluation suggested that themodel measures the accuracy,precision,latency,Quality of Service(QoS),and scalability of themodel.A remarkable 95%precision,93%latency,89%quality of service,98%detection accuracy,and 96%scalability were obtained by the suggested model,as shown by the subsequent findings.展开更多
Data-driven techniques are reshaping blast furnace iron-making process(BFIP)modeling,but their“black-box”nature often obscures interpretability and accuracy.To overcome these limitations,our mechanism and data co-dr...Data-driven techniques are reshaping blast furnace iron-making process(BFIP)modeling,but their“black-box”nature often obscures interpretability and accuracy.To overcome these limitations,our mechanism and data co-driven strategy(MDCDS)enhances model transparency and molten iron quality(MIQ)prediction.By zoning the furnace and applying mechanism-based features for material and thermal trends,coupled with a novel stationary broad feature learning system(StaBFLS),interference caused by nonstationary process characteristics are mitigated and the intrinsic information embedded in BFIP is mined.Subsequently,by integrating stationary feature representation with mechanism features,our temporal matching broad learning system(TMBLS)aligns process and quality variables using MIQ as the target.This integration allows us to establish process monitoring statistics using both mechanism and data-driven features,as well as detect modeling deviations.Validated against real-world BFIP data,our MDCDS model demonstrates consistent process alignment,robust feature extraction,and improved MIQ modeling—Yielding better fault detection.Additionally,we offer detailed insights into the validation process,including parameter baselining and optimization.展开更多
An intrusion detection （ID） model is proposed based on the fuzzy data mining method. A major difficulty of anomaly ID is that patterns of the normal behavior change with time. In addition, an actual intrusion with a...An intrusion detection （ID） model is proposed based on the fuzzy data mining method. A major difficulty of anomaly ID is that patterns of the normal behavior change with time. In addition, an actual intrusion with a small deviation may match normal patterns. So the intrusion behavior cannot be detected by the detection system.To solve the problem, fuzzy data mining technique is utilized to extract patterns representing the normal behavior of a network. A set of fuzzy association rules mined from the network data are shown as a model of “normal behaviors”. To detect anomalous behaviors, fuzzy association rules are generated from new audit data and the similarity with sets mined from “normal” data is computed. If the similarity values are lower than a threshold value,an alarm is given. Furthermore, genetic algorithms are used to adjust the fuzzy membership functions and to select an appropriate set of features.展开更多
Lung cancer continues to be a leading cause of cancer-related deaths worldwide,emphasizing the critical need for improved diagnostic techniques.Early detection of lung tumors significantly increases the chances of suc...Lung cancer continues to be a leading cause of cancer-related deaths worldwide,emphasizing the critical need for improved diagnostic techniques.Early detection of lung tumors significantly increases the chances of successful treatment and survival.However,current diagnostic methods often fail to detect tumors at an early stage or to accurately pinpoint their location within the lung tissue.Single-model deep learning technologies for lung cancer detection,while beneficial,cannot capture the full range of features present in medical imaging data,leading to incomplete or inaccurate detection.Furthermore,it may not be robust enough to handle the wide variability in medical images due to different imaging conditions,patient anatomy,and tumor characteristics.To overcome these disadvantages,dual-model or multi-model approaches can be employed.This research focuses on enhancing the detection of lung cancer by utilizing a combination of two learning models:a Convolutional Neural Network(CNN)for categorization and the You Only Look Once(YOLOv8)architecture for real-time identification and pinpointing of tumors.CNNs automatically learn to extract hierarchical features from raw image data,capturing patterns such as edges,textures,and complex structures that are crucial for identifying lung cancer.YOLOv8 incorporates multiscale feature extraction,enabling the detection of tumors of varying sizes and scales within a single image.This is particularly beneficial for identifying small or irregularly shaped tumors that may be challenging to detect.Furthermore,through the utilization of cutting-edge data augmentation methods,such as Deep Convolutional Generative Adversarial Networks(DCGAN),the suggested approach can handle the issue of limited data and boost the models’ability to learn from diverse and comprehensive datasets.The combined method not only improved accuracy and localization but also ensured efficient real-time processing,which is crucial for practical clinical applications.The CNN achieved an accuracy of 97.67%in classifying lung tissues into healthy and cancerous categories.The YOLOv8 model achieved an Intersection over Union(IoU)score of 0.85 for tumor localization,reflecting high precision in detecting and marking tumor boundaries within the images.Finally,the incorporation of synthetic images generated by DCGAN led to a 10%improvement in both the CNN classification accuracy and YOLOv8 detection performance.展开更多
基金supported by the National Research Foundation of Korea(NRF)funded by the Korea government(RS-2023-00249743)Additionally,this research was supported by the Global-Learning&Academic Research Institution for Master’s,PhD Students,and Postdocs(LAMP)Program of the National Research Foundation of Korea(NRF)grant funded by the Ministry of Education(RS-2024-00443714)This research was also supported by the“Research Base Construction Fund Support Program”funded by Jeonbuk National University in 2025.
文摘Traditional anomaly detection methods often assume that data points are independent or exhibit regularly structured relationships,as in Euclidean data such as time series or image grids.However,real-world data frequently involve irregular,interconnected structures,requiring a shift toward non-Euclidean approaches.This study introduces a novel anomaly detection framework designed to handle non-Euclidean data by modeling transactions as graph signals.By leveraging graph convolution filters,we extract meaningful connection strengths that capture relational dependencies often overlooked in traditional methods.Utilizing the Graph Convolutional Networks(GCN)framework,we integrate graph-based embeddings with conventional anomaly detection models,enhancing performance through relational insights.Ourmethod is validated on European credit card transaction data,demonstrating its effectiveness in detecting fraudulent transactions,particularly thosewith subtle patterns that evade traditional,amountbased detection techniques.The results highlight the advantages of incorporating temporal and structural dependencies into fraud detection,showcasing the robustness and applicability of our approach in complex,real-world scenarios.
文摘The increasing number of interconnected devices and the incorporation of smart technology into contemporary healthcare systems have significantly raised the attack surface of cyber threats.The early detection of threats is both necessary and complex,yet these interconnected healthcare settings generate enormous amounts of heterogeneous data.Traditional Intrusion Detection Systems(IDS),which are generally centralized and machine learning-based,often fail to address the rapidly changing nature of cyberattacks and are challenged by ethical concerns related to patient data privacy.Moreover,traditional AI-driven IDS usually face challenges in handling large-scale,heterogeneous healthcare data while ensuring data privacy and operational efficiency.To address these issues,emerging technologies such as Big Data Analytics(BDA)and Federated Learning(FL)provide a hybrid framework for scalable,adaptive intrusion detection in IoT-driven healthcare systems.Big data techniques enable processing large-scale,highdimensional healthcare data,and FL can be used to train a model in a decentralized manner without transferring raw data,thereby maintaining privacy between institutions.This research proposes a privacy-preserving Federated Learning–based model that efficiently detects cyber threats in connected healthcare systems while ensuring distributed big data processing,privacy,and compliance with ethical regulations.To strengthen the reliability of the reported findings,the resultswere validated using cross-dataset testing and 95%confidence intervals derived frombootstrap analysis,confirming consistent performance across heterogeneous healthcare data distributions.This solution takes a significant step toward securing next-generation healthcare infrastructure by combining scalability,privacy,adaptability,and earlydetection capabilities.The proposed global model achieves a test accuracy of 99.93%±0.03(95%CI)and amiss-rate of only 0.07%±0.02,representing state-of-the-art performance in privacy-preserving intrusion detection.The proposed FL-driven IDS framework offers an efficient,privacy-preserving,and scalable solution for securing next-generation healthcare infrastructures by combining adaptability,early detection,and ethical data management.
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R104)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Modern intrusion detection systems(MIDS)face persistent challenges in coping with the rapid evolution of cyber threats,high-volume network traffic,and imbalanced datasets.Traditional models often lack the robustness and explainability required to detect novel and sophisticated attacks effectively.This study introduces an advanced,explainable machine learning framework for multi-class IDS using the KDD99 and IDS datasets,which reflects real-world network behavior through a blend of normal and diverse attack classes.The methodology begins with sophisticated data preprocessing,incorporating both RobustScaler and QuantileTransformer to address outliers and skewed feature distributions,ensuring standardized and model-ready inputs.Critical dimensionality reduction is achieved via the Harris Hawks Optimization(HHO)algorithm—a nature-inspired metaheuristic modeled on hawks’hunting strategies.HHO efficiently identifies the most informative features by optimizing a fitness function based on classification performance.Following feature selection,the SMOTE is applied to the training data to resolve class imbalance by synthetically augmenting underrepresented attack types.The stacked architecture is then employed,combining the strengths of XGBoost,SVM,and RF as base learners.This layered approach improves prediction robustness and generalization by balancing bias and variance across diverse classifiers.The model was evaluated using standard classification metrics:precision,recall,F1-score,and overall accuracy.The best overall performance was recorded with an accuracy of 99.44%for UNSW-NB15,demonstrating the model’s effectiveness.After balancing,the model demonstrated a clear improvement in detecting the attacks.We tested the model on four datasets to show the effectiveness of the proposed approach and performed the ablation study to check the effect of each parameter.Also,the proposed model is computationaly efficient.To support transparency and trust in decision-making,explainable AI(XAI)techniques are incorporated that provides both global and local insight into feature contributions,and offers intuitive visualizations for individual predictions.This makes it suitable for practical deployment in cybersecurity environments that demand both precision and accountability.
基金supported by Natural Science Foundation of Qinghai Province(2025-ZJ-994M)Scientific Research Innovation Capability Support Project for Young Faculty(SRICSPYF-BS2025007)National Natural Science Foundation of China(62566050).
文摘Multivariate anomaly detection plays a critical role in maintaining the stable operation of information systems.However,in existing research,multivariate data are often influenced by various factors during the data collection process,resulting in temporal misalignment or displacement.Due to these factors,the node representations carry substantial noise,which reduces the adaptability of the multivariate coupled network structure and subsequently degrades anomaly detection performance.Accordingly,this study proposes a novel multivariate anomaly detection model grounded in graph structure learning.Firstly,a recommendation strategy is employed to identify strongly coupled variable pairs,which are then used to construct a recommendation-driven multivariate coupling network.Secondly,a multi-channel graph encoding layer is used to dynamically optimize the structural properties of the multivariate coupling network,while a multi-head attention mechanism enhances the spatial characteristics of the multivariate data.Finally,unsupervised anomaly detection is conducted using a dynamic threshold selection algorithm.Experimental results demonstrate that effectively integrating the structural and spatial features of multivariate data significantly mitigates anomalies caused by temporal dependency misalignment.
文摘In modern industrial production,foreign object detection in complex environments is crucial to ensure product quality and production safety.Detection systems based on deep-learning image processing algorithms often face challenges with handling high-resolution images and achieving accurate detection against complex backgrounds.To address these issues,this study employs the PatchCore unsupervised anomaly detection algorithm combined with data augmentation techniques to enhance the system’s generalization capability across varying lighting conditions,viewing angles,and object scales.The proposed method is evaluated in a complex industrial detection scenario involving the bogie of an electric multiple unit(EMU).A dataset consisting of complex backgrounds,diverse lighting conditions,and multiple viewing angles is constructed to validate the performance of the detection system in real industrial environments.Experimental results show that the proposed model achieves an average area under the receiver operating characteristic curve(AUROC)of 0.92 and an average F1 score of 0.85.Combined with data augmentation,the proposed model exhibits improvements in AUROC by 0.06 and F1 score by 0.03,demonstrating enhanced accuracy and robustness for foreign object detection in complex industrial settings.In addition,the effects of key factors on detection performance are systematically analyzed,providing practical guidance for parameter selection in real industrial applications.
基金supported by“the Fundamental Research Funds for the Central Universities”(Grant No.CUCAI2511).
文摘Social bots are automated programs designed to spread rumors and misinformation,posing significant threats to online security.Existing research shows that the structure of a social network significantly affects the behavioral patterns of social bots:a higher number of connected components weakens their collaborative capabilities,thereby reducing their proportion within the overall network.However,current social bot detection methods still make limited use of topological features.Furthermore,both graph neural network(GNN)-based methods that rely on local features and those that leverage global features suffer from their own limitations,and existing studies lack an effective fusion of multi-scale information.To address these issues,this paper proposes a topology-aware multi-scale social bot detection method,which jointly learns local and global representations through a co-training mechanism.At the local level,topological features are effectively embedded into node representations,enhancing expressiveness while alleviating the over-smoothing problem in GNNs.At the global level,a clustering attention mechanism is introduced to learn global node representations,mitigating the over-globalization problem.Experimental results demonstrate that our method effectively overcomes the limitations of single-scale approaches.Our code is publicly available at https://anonymous.4open.science/r/TopoMSG-2C41/(accessed on 27 October 2025).
基金supported by the Zhongyuan University of Technology Discipline Backbone Teacher Support Program Project(No.GG202417)the Key Research and Development Program of Henan under Grant 251111212000.
文摘Lateral movement represents the most covert and critical phase of Advanced Persistent Threats(APTs),and its detection still faces two primary challenges:sample scarcity and“cold start”of new entities.To address these challenges,we propose an Uncertainty-Driven Graph Embedding-Enhanced Lateral Movement Detection framework(UGEA-LMD).First,the framework employs event-level incremental encoding on a continuous-time graph to capture fine-grained behavioral evolution,enabling newly appearing nodes to retain temporal contextual awareness even in the absence of historical interactions and thereby fundamentally mitigating the cold-start problem.Second,in the embedding space,we model the dependency structure among feature dimensions using a Gaussian copula to quantify the uncertainty distribution,and generate augmented samples with consistent structural and semantic properties through adaptive sampling,thus expanding the representation space of sparse samples and enhancing the model’s generalization under sparse sample conditions.Unlike static graph methods that cannot model temporal dependencies or data augmentation techniques that depend on predefined structures,UGEA-LMD offers both superior temporaldynamic modeling and structural generalization.Experimental results on the large-scale LANL log dataset demonstrate that,under the transductive setting,UGEA-LMD achieves an AUC of 0.9254;even when 10%of nodes or edges are withheld during training,UGEA-LMD significantly outperforms baseline methods on metrics such as recall and AUC,confirming its robustness and generalization capability in sparse-sample and cold-start scenarios.
基金funded by the Ongoing Research Funding Program(ORF-2025-890)King Saud University,Riyadh,Saudi Arabia and was supported by the Competitive Research Fund of theUniversity of Aizu,Japan.
文摘The exponential expansion of the Internet of Things(IoT),Industrial Internet of Things(IIoT),and Transportation Management of Things(TMoT)produces vast amounts of real-time streaming data.Ensuring system dependability,operational efficiency,and security depends on the identification of anomalies in these dynamic and resource-constrained systems.Due to their high computational requirements and inability to efficiently process continuous data streams,traditional anomaly detection techniques often fail in IoT systems.This work presents a resource-efficient adaptive anomaly detection model for real-time streaming data in IoT systems.Extensive experiments were carried out on multiple real-world datasets,achieving an average accuracy score of 96.06%with an execution time close to 7.5 milliseconds for each individual streaming data point,demonstrating its potential for real-time,resourceconstrained applications.The model uses Principal Component Analysis(PCA)for dimensionality reduction and a Z-score technique for anomaly detection.It maintains a low computational footprint with a sliding window mechanism,enabling incremental data processing and identification of both transient and sustained anomalies without storing historical data.The system uses a Multivariate Linear Regression(MLR)based imputation technique that estimates missing or corrupted sensor values,preserving data integrity prior to anomaly detection.The suggested solution is appropriate for many uses in smart cities,industrial automation,environmental monitoring,IoT security,and intelligent transportation systems,and is particularly well-suited for resource-constrained edge devices.
基金supported by the National Natural Science Foundation of China(62473341)Key Technologies R&D Program of Henan Province(242102211071,252102211086,252102210166).
文摘With the rapid development of the industrial Internet,the network security environment has become increasingly complex and variable.Intrusion detection,a core technology for ensuring the security of industrial control systems,faces the challenge of unbalanced data samples,particularly the low detection rates for minority class attack samples.Therefore,this paper proposes a data enhancement method for intrusion detection in the industrial Internet based on a Self-Attention Wasserstein Generative Adversarial Network(SA-WGAN)to address the low detection rates of minority class attack samples in unbalanced intrusion detection scenarios.The proposed method integrates a selfattention mechanism with a Wasserstein Generative Adversarial Network(WGAN).The self-attention mechanism automatically learns important features from the input data and assigns different weights to emphasize the key features related to intrusion behaviors,providing strong guidance for subsequent data generation.The WGAN generates new data samples through adversarial training to expand the original dataset.In the SA-WGAN framework,the WGAN directs the data generation process based on the key features extracted by the self-attention mechanism,ensuring that the generated samples exhibit both diversity and similarity to real data.Experimental results demonstrate that the SA-WGAN-based data enhancement method significantly improves detection performance for attack samples from minority classes,addresses issues of insufficient data and category imbalance,and enhances the generalization ability and overall performance of the intrusion detection model.
基金supported by The National Natural Science Foundation of China under Grant(5247512)National Key Lab of Aerospace Power System and Plasma Technology Foundation(APSPT202304002).
文摘Rotating machinery is critical to industrial systems,necessitating robust anomaly detection(AD)to ensure operational safety and prevent failures.However,in real-world scenarios,monitoring data is typically unlabeled and often consists of normal samples contaminated with a small proportion of unknown anomalies.To address this,this paper proposes a diffusion-based AD method,Anomaly Detection Denoising Diffusion Probabilistic Model(AD-DDPM)for robust AD.The method employs a U-attention-net to capture local and global features and introduces a filtered contrastive mechanism to mitigate the impact of contaminated training data.By leveraging the probabilistic nature of diffusion models,AD-DDPM effectively models normal data distributions,achieving superior AD even with polluted samples.Experimental validation on fault simulation datasets demonstrates the method’s exceptional performance,outperforming traditional machine learning and deep learning baselines.The proposed approach offers a promising solution for reliable health monitoring in industrial settings.
基金supported by National Natural Science Foundation of China(61673186 and 61871196)Beijing Normal University Education Reform Project(jx2024040)Guangdong Undergraduate Universities Teaching Quality and Reform Project(jx2024309).
文摘Data augmentation plays an important role in boosting the performance of 3D models,while very few studies handle the 3D point cloud data with this technique.Global augmentation and cut-paste are commonly used augmentation techniques for point clouds,where global augmentation is applied to the entire point cloud of the scene,and cut-paste samples objects from other frames into the current frame.Both types of data augmentation can improve performance,but the cut-paste technique cannot effectively deal with the occlusion relationship between the foreground object and the background scene and the rationality of object sampling,which may be counterproductive and may hurt the overall performance.In addition,LiDAR is susceptible to signal loss,external occlusion,extreme weather and other factors,which can easily cause object shape changes,while global augmentation and cut-paste cannot effectively enhance the robustness of the model.To this end,we propose Syn-Aug,a synchronous data augmentation framework for LiDAR-based 3D object detection.Specifically,we first propose a novel rendering-based object augmentation technique(Ren-Aug)to enrich training data while enhancing scene realism.Second,we propose a local augmentation technique(Local-Aug)to generate local noise by rotating and scaling objects in the scene while avoiding collisions,which can improve generalisation performance.Finally,we make full use of the structural information of 3D labels to make the model more robust by randomly changing the geometry of objects in the training frames.We verify the proposed framework with four different types of 3D object detectors.Experimental results show that our proposed Syn-Aug significantly improves the performance of various 3D object detectors in the KITTI and nuScenes datasets,proving the effectiveness and generality of Syn-Aug.On KITTI,four different types of baseline models using Syn-Aug improved mAP by 0.89%,1.35%,1.61%and 1.14%respectively.On nuScenes,four different types of baseline models using Syn-Aug improved mAP by 14.93%,10.42%,8.47%and 6.81%respectively.The code is available at https://github.com/liuhuaijjin/Syn-Aug.
文摘Seismic data plays a pivotal role in fault detection,offering critical insights into subsurface structures and seismic hazards.Understanding fault detection from seismic data is essential for mitigating seismic risks and guiding land-use plans.This paper presents a comprehensive review of existing methodologies for fault detection,focusing on the application of Machine Learning(ML)and Deep Learning(DL)techniques to enhance accuracy and efficiency.Various ML and DL approaches are analyzed with respect to fault segmentation,adaptive learning,and fault detection models.These techniques,benchmarked against established seismic datasets,reveal significant improvements over classical methods in terms of accuracy and computational efficiency.Additionally,this review highlights emerging trends,including hybrid model applications and the integration of real-time data processing for seismic fault detection.By providing a detailed comparative analysis of current methodologies,this review aims to guide future research and foster advancements in the effectiveness and reliability of seismic studies.Ultimately,the study seeks to bridge the gap between theoretical investigations and practical implementations in fault detection.
基金supported in part by National Natural Science Foundation of China under Grant 62071078in part by Sichuan Province Science and Technology Program under Grant 2021YFQ0053。
文摘Anomaly detection is an important task for maintaining the performance of cloud data center.Traditional anomaly detection primarily examines individual Virtual Machine(VM)behavior,neglecting the impact of interactions among multiple VMs on Key Performance Indicator(KPI)data,e.g.,memory utilization.Furthermore,the nonstationarity,high complexity,and uncertain periodicity of KPI data in VM also bring difficulties to deep learningbased anomaly detection tasks.To settle these challenges,this paper proposes MCBiWGAN-GTN,a multi-channel semi-supervised time series anomaly detection algorithm based on the Bidirectional Wasserstein Generative Adversarial Network with Graph-Time Network(BiWGAN-GTN)and the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN).(a)The BiWGAN-GTN algorithm is proposed to extract spatiotemporal information from data.(b)The loss function of BiWGAN-GTN is redesigned to solve the abnormal data intrusion problem during the training process.(c)MCBiWGAN-GTN is designed to reduce data complexity through CEEMDAN for time series decomposition and utilizes BiWGAN-GTN to train different components.(d)To adapt the proposed algorithm for the entire cloud data center,a cloud data center anomaly detection framework based on Swarm Learning(SL)is designed.The evaluation results on a real-world cloud data center dataset show that MCBiWGAN-GTN outperforms the baseline,with an F1-score of 0.96,an accuracy of 0.935,a precision of 0.954,a recall of 0.967,and an FPR of 0.203.The experiments also verify the stability of MCBiWGAN-GTN,the impact of parameter configurations,and the effectiveness of the proposed SL framework.
基金supported by the National Natural Science Foundation of China(62373224,62333013,U23A20327)the Natural Science Foundation of Shandong Province(ZR2024JQ021)
文摘Dear Editor,Health management is essential to ensure battery performance and safety, while data-driven learning system is a promising solution to enable efficient state of health(SoH) estimation of lithium-ion(Liion) batteries. However, the time-consuming signal data acquisition and the lack of interpretability of model still hinder its efficient deployment. Motivated by this, this letter proposes a novel and interpretable data-driven learning strategy through combining the benefits of explainable AI and non-destructive ultrasonic detection for battery SoH estimation. Specifically, after equipping battery with advanced ultrasonic sensor to promise fast real-time ultrasonic signal measurement, an interpretable data-driven learning strategy named generalized additive neural decision ensemble(GANDE) is designed to rapidly estimate battery SoH and explain the effects of the involved ultrasonic features of interest.
基金the National Key Research and Development Program of China(No.2023 YFC2811600)the National Natural Science Foundation of China(Nos.52301349,52088102)+1 种基金the Major Science and Technology Innovation Program of Qingdao(No.223-3-hygg-10-hy)the Qingdao Science Foundation for Post-doctoral Scientists(Nos.QDBSH20220202070,QDBSH20220201015)。
文摘A deep-sea riser is a crucial component of the mining system used to lift seafloor mineral resources to the vessel.Even minor damage to the riser can lead to substantial financial losses,environmental impacts,and safety hazards.However,identifying modal parameters for structural health monitoring remains a major challenge due to its large deformations and flexibility.Vibration signal-based methods are essential for detecting damage and enabling timely maintenance to minimize losses.However,accurately extracting features from one-dimensional(1D)signals is often hindered by various environmental factors and measurement noises.To address this challenge,a novel approach based on a residual convolutional auto-encoder(RCAE)is proposed for detecting damage in deep-sea mining risers,incorporating a data fusion strategy.First,principal component analysis(PCA)is applied to reduce environmental fluctuations and fuse multisensor strain readings.Subsequently,a 1D-RCAE is used to extract damage-sensitive features(DSFs)from the fused dataset.A Mahalanobis distance indicator is established to compare the DSFs of the testing and healthy risers.The specific threshold for these distances is determined using the 3σcriterion,which is employed to assess whether damage has occurred in the testing riser.The effectiveness and robustness of the proposed approach are verified through numerical simulations of a 500-m riser and experimental tests on a 6-m riser.Moreover,the impact of contaminated noise and environmental fluctuations is examined.Results show that the proposed PCA-1D-RCAE approach can effectively detect damage and is resilient to measurement noise and environmental fluctuations.The accuracy exceeds 98%under noise-free conditions and remains above 90%even with 10 dB noise.This novel approach has the potential to establish a new standard for evaluating the health and integrity of risers during mining operations,thereby reducing the high costs and risks associated with failures.Maintenance activities can be scheduled more efficiently by enabling early and accurate detection of riser damage,minimizing downtime and avoiding catastrophic failures.
基金supported in part by the National Key Research and Development Program of China under Grant 2024YFE0200600in part by the National Natural Science Foundation of China under Grant 62071425+3 种基金in part by the Zhejiang Key Research and Development Plan under Grant 2022C01093in part by the Zhejiang Provincial Natural Science Foundation of China under Grant LR23F010005in part by the National Key Laboratory of Wireless Communications Foundation under Grant 2023KP01601in part by the Big Data and Intelligent Computing Key Lab of CQUPT under Grant BDIC-2023-B-001.
文摘Semantic communication(SemCom)aims to achieve high-fidelity information delivery under low communication consumption by only guaranteeing semantic accuracy.Nevertheless,semantic communication still suffers from unexpected channel volatility and thus developing a re-transmission mechanism(e.g.,hybrid automatic repeat request[HARQ])becomes indispensable.In that regard,instead of discarding previously transmitted information,the incremental knowledge-based HARQ(IK-HARQ)is deemed as a more effective mechanism that could sufficiently utilize the information semantics.However,considering the possible existence of semantic ambiguity in image transmission,a simple bit-level cyclic redundancy check(CRC)might compromise the performance of IK-HARQ.Therefore,there emerges a strong incentive to revolutionize the CRC mechanism,thus more effectively reaping the benefits of both SemCom and HARQ.In this paper,built on top of swin transformer-based joint source-channel coding(JSCC)and IK-HARQ,we propose a semantic image transmission framework SC-TDA-HARQ.In particular,different from the conventional CRC,we introduce a topological data analysis(TDA)-based error detection method,which capably digs out the inner topological and geometric information of images,to capture semantic information and determine the necessity for re-transmission.Extensive numerical results validate the effectiveness and efficiency of the proposed SC-TDA-HARQ framework,especially under the limited bandwidth condition,and manifest the superiority of TDA-based error detection method in image transmission.
基金funded by Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah under grant No.(RG-6-611-43)the authors,therefore,acknowledge with thanks DSR technical and financial support.
文摘The integration of the Internet of Things(IoT)into healthcare systems improves patient care,boosts operational efficiency,and contributes to cost-effective healthcare delivery.However,overcoming several associated challenges,such as data security,interoperability,and ethical concerns,is crucial to realizing the full potential of IoT in healthcare.Real-time anomaly detection plays a key role in protecting patient data and maintaining device integrity amidst the additional security risks posed by interconnected systems.In this context,this paper presents a novelmethod for healthcare data privacy analysis.The technique is based on the identification of anomalies in cloud-based Internet of Things(IoT)networks,and it is optimized using explainable artificial intelligence.For anomaly detection,the Radial Boltzmann Gaussian Temporal Fuzzy Network(RBGTFN)is used in the process of doing information privacy analysis for healthcare data.Remora Colony SwarmOptimization is then used to carry out the optimization of the network.The performance of the model in identifying anomalies across a variety of healthcare data is evaluated by an experimental study.This evaluation suggested that themodel measures the accuracy,precision,latency,Quality of Service(QoS),and scalability of themodel.A remarkable 95%precision,93%latency,89%quality of service,98%detection accuracy,and 96%scalability were obtained by the suggested model,as shown by the subsequent findings.
基金supported in part by the National Natural Science Foundation of China(61933015,61703371,62273030)the Central University Basic Research Fund of China(K20200002)(for NGICS Platform,Zhejiang University)the Social Development Project of Zhejiang Provincial Public Technology Research(LGF19F030004,LGG21F030015).
文摘Data-driven techniques are reshaping blast furnace iron-making process(BFIP)modeling,but their“black-box”nature often obscures interpretability and accuracy.To overcome these limitations,our mechanism and data co-driven strategy(MDCDS)enhances model transparency and molten iron quality(MIQ)prediction.By zoning the furnace and applying mechanism-based features for material and thermal trends,coupled with a novel stationary broad feature learning system(StaBFLS),interference caused by nonstationary process characteristics are mitigated and the intrinsic information embedded in BFIP is mined.Subsequently,by integrating stationary feature representation with mechanism features,our temporal matching broad learning system(TMBLS)aligns process and quality variables using MIQ as the target.This integration allows us to establish process monitoring statistics using both mechanism and data-driven features,as well as detect modeling deviations.Validated against real-world BFIP data,our MDCDS model demonstrates consistent process alignment,robust feature extraction,and improved MIQ modeling—Yielding better fault detection.Additionally,we offer detailed insights into the validation process,including parameter baselining and optimization.
文摘An intrusion detection （ID） model is proposed based on the fuzzy data mining method. A major difficulty of anomaly ID is that patterns of the normal behavior change with time. In addition, an actual intrusion with a small deviation may match normal patterns. So the intrusion behavior cannot be detected by the detection system.To solve the problem, fuzzy data mining technique is utilized to extract patterns representing the normal behavior of a network. A set of fuzzy association rules mined from the network data are shown as a model of “normal behaviors”. To detect anomalous behaviors, fuzzy association rules are generated from new audit data and the similarity with sets mined from “normal” data is computed. If the similarity values are lower than a threshold value,an alarm is given. Furthermore, genetic algorithms are used to adjust the fuzzy membership functions and to select an appropriate set of features.
文摘Lung cancer continues to be a leading cause of cancer-related deaths worldwide,emphasizing the critical need for improved diagnostic techniques.Early detection of lung tumors significantly increases the chances of successful treatment and survival.However,current diagnostic methods often fail to detect tumors at an early stage or to accurately pinpoint their location within the lung tissue.Single-model deep learning technologies for lung cancer detection,while beneficial,cannot capture the full range of features present in medical imaging data,leading to incomplete or inaccurate detection.Furthermore,it may not be robust enough to handle the wide variability in medical images due to different imaging conditions,patient anatomy,and tumor characteristics.To overcome these disadvantages,dual-model or multi-model approaches can be employed.This research focuses on enhancing the detection of lung cancer by utilizing a combination of two learning models:a Convolutional Neural Network(CNN)for categorization and the You Only Look Once(YOLOv8)architecture for real-time identification and pinpointing of tumors.CNNs automatically learn to extract hierarchical features from raw image data,capturing patterns such as edges,textures,and complex structures that are crucial for identifying lung cancer.YOLOv8 incorporates multiscale feature extraction,enabling the detection of tumors of varying sizes and scales within a single image.This is particularly beneficial for identifying small or irregularly shaped tumors that may be challenging to detect.Furthermore,through the utilization of cutting-edge data augmentation methods,such as Deep Convolutional Generative Adversarial Networks(DCGAN),the suggested approach can handle the issue of limited data and boost the models’ability to learn from diverse and comprehensive datasets.The combined method not only improved accuracy and localization but also ensured efficient real-time processing,which is crucial for practical clinical applications.The CNN achieved an accuracy of 97.67%in classifying lung tissues into healthy and cancerous categories.The YOLOv8 model achieved an Intersection over Union(IoU)score of 0.85 for tumor localization,reflecting high precision in detecting and marking tumor boundaries within the images.Finally,the incorporation of synthetic images generated by DCGAN led to a 10%improvement in both the CNN classification accuracy and YOLOv8 detection performance.
