In recent years,the rapid advancement of artificial intelligence(AI)technology has enabled AI-assisted negative screening to significantly enhance physicians'efficiency through image feature analysis and multimoda...In recent years,the rapid advancement of artificial intelligence(AI)technology has enabled AI-assisted negative screening to significantly enhance physicians'efficiency through image feature analysis and multimodal data modeling,allowing them to focus more on diagnosing positive cases.Meanwhile,multispectral imaging(MSI)integrates spectral and spatial resolution to capture subtle tissue features invisible to the human eye,providing high-resolution data support for pathological analysis.Combining AI technology with MSI and employing quantitative methods to analyze multiband biomarkers(such as absorbance differences in keratin pearls)can effectively improve diagnostic specificity and reduce subjective errors in manual slide interpretation.To address the challenge of identifying negative tissue sections,we developed a discrimination algorithm powered by MSI.We demonstrated its efficacy using cutaneous squamous cell carcinoma(cSCC)as a representative case study.The algorithm achieved 100%accuracy in excluding negative cases and effectively mitigated the false-positive problem caused by cSCC heterogeneity.We constructed a multispectral image(MSI)dataset acquired at 520 nm,600 nm,and 630 nm wavelengths.Subsequently,we employed an optimized MobileViT model for tissue classification and performed comparative analyses against other models.The experimental results showed that our optimized MobileViT model achieved superior performance in identifying negative tissue sections,with a perfect accuracy rate of 100%.Thus,our results confirm the feasibility of integrating MSI with AI to exclude negative cases with perfect accuracy,offering a novel solution to alleviate the workload of pathologists.展开更多
Visible and infrared(RGB-IR)fusion object detection plays an important role in security,disaster relief,etc.In recent years,deep-learning-based RGB-IR fusion detection methods have been developing rapidly,but still st...Visible and infrared(RGB-IR)fusion object detection plays an important role in security,disaster relief,etc.In recent years,deep-learning-based RGB-IR fusion detection methods have been developing rapidly,but still struggle to deal with the complex and changing scenarios captured by drones,mainly due to two reasons:(A)RGB-IR fusion detectors are susceptible to inferior inputs that degrade performance and stability.(B)RGB-IR fusion detectors are susceptible to redundant features that reduce accuracy and efficiency.In this paper,an innovative RGB-IR fusion detection framework based on global-local feature optimization,named GLFDet,is proposed to improve the detection performance and efficiency of drone-captured objects.The key components of GLFDet include a Global Feature Optimization(GFO)module,a Local Feature Optimization(LFO)module and a Channel Separation Fusion(CSF)module.Specifically,GFO calculates the information content of the input image from the frequency domain and optimizes the features holistically.Then,LFO dynamically selects high-value features and filters out low-value features before fusion,which significantly improves the efficiency of fusion.Finally,CSF fuses the RGB and IR features across the corresponding channels,which avoids the rearrangement of the channel relationships and enhances the model stability.Extensive experimental results show that the proposed method achieves the best performance on three popular RGB-IR datasets Drone Vehicle,VEDAI,and LLVIP.In addition,GLFDet is more lightweight than other comparable models,making it more appealing to edge devices such as drones.The code is available at https://github.com/lao chen330/GLFDet.展开更多
In this study,a multifunctional aptamer-conjugated magnetic covalent organic framework(COF)-CuO/Au nanozyme(MCOF-CuO/Au@apt)was developed as a“three-in-one”platform for dual-signal colorimetric and fluorescent detec...In this study,a multifunctional aptamer-conjugated magnetic covalent organic framework(COF)-CuO/Au nanozyme(MCOF-CuO/Au@apt)was developed as a“three-in-one”platform for dual-signal colorimetric and fluorescent detection of Vibrio parahaemolyticus.The nanozyme integrated magnetic separation,peroxidase-like catalytic activity,and specific target recognition through an aptamer-based strategy.Upon binding to V.parahaemolyticus,the catalytic oxidation of tetra-aminophenylethylene(TPE-4A)by the nanozyme was selectively inhibited,resulting in distinct colorimetric and fluorescent signals that significantly enhanced the detection accuracy and reliability.The proposed method exhibited high sensitivity,with limits of detection(LOD)of 21 and 7 CFU/mL for the colorimetric and fluorescent assays,respectively.The performance of this method was validated using real seafood samples,including Penaeus vannamei,Mytilus coruscus,and Crassostrea gigas,which showed high recovery rates(101.11%-107.30%)and excellent reproducibility.The system also demonstrated strong specificity and accuracy under various conditions,confirming its robustness and practical applicability.Collectively,this innovative platform presents a promising solution for the rapid,versatile,and sensitive detection of V.parahaemolyticus in seafood,with considerable potential to advance food safety diagnosis and on-site monitoring.展开更多
【Objective】This study aimed to establish a quintuple PCR method for rapid and simultaneous detection of Ralstonia solanacearum,Fusarium spp.,Pectobacterium spp.,Enterobacter spp.,and Pythium spp.,which provided tech...【Objective】This study aimed to establish a quintuple PCR method for rapid and simultaneous detection of Ralstonia solanacearum,Fusarium spp.,Pectobacterium spp.,Enterobacter spp.,and Pythium spp.,which provided technical support for early diagnosis of various soil-borne diseases on ginger.【Method】For five types of soil-borne pathogens causing ginger bacterial wilt and rhizome rot,specific primer combinations were designed and screened,the optimal quintuple reaction system was established by exploring optimal primer concentrations,annealing temperature,and sensitivity,and was applied to detect field plant samples to verify its utility.【Result】Specific primers pairs Rs1F/Rs1R,En1F/En1R,and Py1F/Py1R were designed according to flic gene of Ralstonia solanacearum,rpoB gene of Enterobacter spp.,and 18S rDNA of Pythium spp.,and combined with reported Fusarium spp.specific primers Fu3/Fu4 and specific primers 23SPecF/23SPecR of Pectobacterium spp.,a quintuple PCR reaction system for ginger soil-borne pathogens has been established(25.00μL):above primer dosage was 1.20,0.20,0.60,1.60,and 0.15μL respectively;2×PCR Mix 12.50μL;DNA templates of different pathogens were 1.00μL each;added ddH_(2)O to 25.00μL.Annealing temperature was optimized to 55.4℃.The specific fragments with sizes of 516,370,266,207,and 159 bp could be amplified simultaneously in the established quintuple PCR system,and the detection limit of this system for Ralstonia solanacearum,Enterobacter spp.and Pythium spp.reached 10^(-1)pg/μL,for Fusarium spp.and Pectobacterium spp.was 1 pg/μL,and for detecting five pathogens simultaneously was 10^(3)pg/μL.The multiplex PCR system established in this study could successfully detect the diseased plant samples from the field.【Conclusion】The quintuple PCR system established is able to rapid ly and accurately detect Ralstonia solanacearum,Fusarium spp.,Pectobacterium spp.,Enterobacter spp.,and Pythium spp.,which provides a useful tool for timely diagnosis and epidemic monitoring of various soil-borne diseases of ginger.展开更多
Dear Editor,This letter studies the motion planning issue for an autonomous underwater vehicle(AUV)in obstacle environment.We propose a novel integrated detection-communication waveform that enables simultaneous obsta...Dear Editor,This letter studies the motion planning issue for an autonomous underwater vehicle(AUV)in obstacle environment.We propose a novel integrated detection-communication waveform that enables simultaneous obstacle detection and self-localization.展开更多
In the field of smart agriculture,accurate and efficient object detection technology is crucial for automated crop management.A particularly challenging task in this domain is small object detection,such as the identi...In the field of smart agriculture,accurate and efficient object detection technology is crucial for automated crop management.A particularly challenging task in this domain is small object detection,such as the identification of immature fruits or early stage disease spots.These objects pose significant difficulties due to their small pixel coverage,limited feature information,substantial scale variations,and high susceptibility to complex background interference.These challenges frequently result in inadequate accuracy and robustness in current detection models.This study addresses two critical needs in the cashew cultivation industry—fruitmaturity and anthracnose detection—by proposing an improved YOLOv11-NSDDil model.The method introduces three key technological innovations:(1)The SDDil module is designed and integrated into the backbone network.This module combines depthwise separable convolution with the SimAM attention mechanism to expand the receptive field and enhance contextual semantic capture at a low computational cost,effectively alleviating the feature deficiency problem caused by limited pixel coverage of small objects.Simultaneously,the SDmodule dynamically enhances discriminative features and suppresses background noise,significantly improving the model’s feature discrimination capability in complex environments;(2)The introduction of the DynamicScalSeq-Zoom_cat neck network,significantly improving multi-scale feature fusion;and(3)The optimization of the Minimum Point Distance Intersection over Union(MPDIoU)loss function,which enhances bounding box localization accuracy byminimizing vertex distance.Experimental results on a self-constructed cashew dataset containing 1123 images demonstrate significant performance improvements in the enhanced model:mAP50 reaches 0.825,a 4.6% increase compared to the originalYOLOv11;mAP50-95 improves to 0.624,a 6.5% increase;and recall rises to 0.777,a 2.4%increase.This provides a reliable technical solution for intelligent quality inspection of agricultural products and holds broad application prospects.展开更多
Zero-day attacks present a critical cybersecurity challenge for Internet of things(IoT)infrastructures,where the inability of signature-based intrusion detection systems(IDSs)to recognize novel threat behaviors compro...Zero-day attacks present a critical cybersecurity challenge for Internet of things(IoT)infrastructures,where the inability of signature-based intrusion detection systems(IDSs)to recognize novel threat behaviors compromises both system reliability and operational continuity.Existing hybrid IDS solutions often struggle to balance accurate classification of known attacks with reliable anomaly detection,particularly under the computational constraints of IoT environments.To address this gap,we introduce ZeroDefense,an adaptive fusion-based IDS designed for simultaneous detection of known intrusions and emerging zero-day threats.The framework employs a four-layer architecture consisting of i)feature standardization and class balancing,ii)anomaly detection using isolation forest,autoencoder,and local outlier factor,iii)fine-grained attack classification via random forest,extreme gradient boosting(XGBoost),light gradient boosting machine(LightGBM),and attentive interpretable tabular learning(TabNet),and iv)a confidence-aware fusion engine that adaptively selects the most reliable decision path.Suspicious or previously unseen traffic is isolated early through fused anomaly scoring,while benign and known-malicious flows are processed through supervised classification for precise attack labeling.With an anomaly cascaded decision pipeline,a dynamic confidence-driven fusion mechanism,and a deploymentconscious design,ZeroDefense enables real-time inference on IoT edge gateways.Evaluation on the CICIoT2023 benchmark demonstrates 99.94% overall accuracy and 95.64%macro-average F1-score for known attacks,while 5.76% of traffic is successfully flagged as potential zero-day activity,with inference latency maintained below 100 ms/flow.These results indicate that ZeroDefense offers a scalable,resilient,and practically deployable defense capability for modern IoT infrastructures.展开更多
Distributed Denial-of-Service(DDoS)attacks pose severe threats to Industrial Control Networks(ICNs),where service disruption can cause significant economic losses and operational risks.Existing signature-based methods...Distributed Denial-of-Service(DDoS)attacks pose severe threats to Industrial Control Networks(ICNs),where service disruption can cause significant economic losses and operational risks.Existing signature-based methods are ineffective against novel attacks,and traditional machine learning models struggle to capture the complex temporal dependencies and dynamic traffic patterns inherent in ICN environments.To address these challenges,this study proposes a deep feature-driven hybrid framework that integrates Transformer,BiLSTM,and KNN to achieve accurate and robust DDoS detection.The Transformer component extracts global temporal dependencies from network traffic flows,while BiLSTM captures fine-grained sequential dynamics.The learned embeddings are then classified using an instance-based KNN layer,enhancing decision boundary precision.This cascaded architecture balances feature abstraction and locality preservation,improving both generalization and robustness.The proposed approach was evaluated on a newly collected real-time ICN traffic dataset and further validated using the public CIC-IDS2017 and Edge-IIoT datasets to demonstrate generalization.Comprehensive metrics including accuracy,precision,recall,F1-score,ROC-AUC,PR-AUC,false positive rate(FPR),and detection latency were employed.Results show that the hybrid framework achieves 98.42%accuracy with an ROC-AUC of 0.992 and FPR below 1%,outperforming baseline machine learning and deep learning models.Robustness experiments under Gaussian noise perturbations confirmed stable performance with less than 2%accuracy degradation.Moreover,detection latency remained below 2.1 ms per sample,indicating suitability for real-time ICS deployment.In summary,the proposed hybrid temporal learning and instance-based classification model offers a scalable and effective solution for DDoS detection in industrial control environments.By combining global contextual modeling,sequential learning,and instance-based refinement,the framework demonstrates strong adaptability across datasets and resilience against noise,providing practical utility for safeguarding critical infrastructure.展开更多
Modern manufacturing processes have become more reliant on automation because of the accelerated transition from Industry 3.0 to Industry 4.0.Manual inspection of products on assembly lines remains inefficient,prone t...Modern manufacturing processes have become more reliant on automation because of the accelerated transition from Industry 3.0 to Industry 4.0.Manual inspection of products on assembly lines remains inefficient,prone to errors and lacks consistency,emphasizing the need for a reliable and automated inspection system.Leveraging both object detection and image segmentation approaches,this research proposes a vision-based solution for the detection of various kinds of tools in the toolkit using deep learning(DL)models.Two Intel RealSense D455f depth cameras were arranged in a top down configuration to capture both RGB and depth images of the toolkits.After applying multiple constraints and enhancing them through preprocessing and augmentation,a dataset consisting of 3300 annotated RGB-D photos was generated.Several DL models were selected through a comprehensive assessment of mean Average Precision(mAP),precision-recall equilibrium,inference latency(target≥30 FPS),and computational burden,resulting in a preference for YOLO and Region-based Convolutional Neural Networks(R-CNN)variants over ViT-based models due to the latter’s increased latency and resource requirements.YOLOV5,YOLOV8,YOLOV11,Faster R-CNN,and Mask R-CNN were trained on the annotated dataset and evaluated using key performance metrics(Recall,Accuracy,F1-score,and Precision).YOLOV11 demonstrated balanced excellence with 93.0%precision,89.9%recall,and a 90.6%F1-score in object detection,as well as 96.9%precision,95.3%recall,and a 96.5%F1-score in instance segmentation with an average inference time of 25 ms per frame(≈40 FPS),demonstrating real-time performance.Leveraging these results,a YOLOV11-based windows application was successfully deployed in a real-time assembly line environment,where it accurately processed live video streams to detect and segment tools within toolkits,demonstrating its practical effectiveness in industrial automation.The application is capable of precisely measuring socket dimensions by utilising edge detection techniques on YOLOv11 segmentation masks,in addition to detection and segmentation.This makes it possible to do specification-level quality control right on the assembly line,which improves the ability to examine things in real time.The implementation is a big step forward for intelligent manufacturing in the Industry 4.0 paradigm.It provides a scalable,efficient,and accurate way to do automated inspection and dimensional verification activities.展开更多
To address critical challenges in nighttime ship detection—high small-target missed detection(over 20%),insufficient lightweighting,and limited generalization due to scarce,low-quality datasets—this study proposes a...To address critical challenges in nighttime ship detection—high small-target missed detection(over 20%),insufficient lightweighting,and limited generalization due to scarce,low-quality datasets—this study proposes a systematic solution.First,a high-quality Night-Ships dataset is constructed via CycleGAN-based day-night transfer,combined with a dual-threshold cleaning strategy(Laplacian variance sharpness filtering and brightness-color deviation screening).Second,a Cross-stage Lightweight Fusion-You Only Look Once version 8(CLF-YOLOv8)is proposed with key improvements:the Neck network is reconstructed by replacing Cross Stage Partial(CSP)structure with the Cross Stage Partial Multi-Scale Convolutional Block(CSP-MSCB)and integrating Bidirectional Feature Pyramid Network(BiFPN)for weighted multi-scale fusion to enhance small-target detection;a Lightweight Shared Convolutional and Separated Batch Normalization Detection-Head(LSCSBD-Head)with shared convolutions and layer-wise Batch Normalization(BN)reduces parameters to 1.8M(42% fewer than YOLOv8n);and the FocalMinimum Point Distance Intersection over Union(Focal-MPDIoU)loss combines Minimum Point Distance Intersection over Union(MPDIoU)geometric constraints and Focal weighting to optimize low-overlap targets.Experiments show CLFYOLOv8 achieves 97.6%mAP@0.5(0.7% higher than YOLOv8n)with 1.8 M parameters,outperforming mainstream models in small-target detection,overlapping target discrimination,and adaptability to complex lighting.展开更多
Adversarial Reinforcement Learning(ARL)models for intelligent devices and Network Intrusion Detection Systems(NIDS)improve systemresilience against sophisticated cyber-attacks.As a core component of ARL,Adversarial Tr...Adversarial Reinforcement Learning(ARL)models for intelligent devices and Network Intrusion Detection Systems(NIDS)improve systemresilience against sophisticated cyber-attacks.As a core component of ARL,Adversarial Training(AT)enables NIDS agents to discover and prevent newattack paths by exposing them to competing examples,thereby increasing detection accuracy,reducing False Positives(FPs),and enhancing network security.To develop robust decision-making capabilities for real-world network disruptions and hostile activity,NIDS agents are trained in adversarial scenarios to monitor the current state and notify management of any abnormal or malicious activity.The accuracy and timeliness of the IDS were crucial to the network’s availability and reliability at this time.This paper analyzes ARL applications in NIDS,revealing State-of-The-Art(SoTA)methodology,issues,and future research prospects.This includes Reinforcement Machine Learning(RML)-based NIDS,which enables an agent to interact with the environment to achieve a goal,andDeep Reinforcement Learning(DRL)-based NIDS,which can solve complex decision-making problems.Additionally,this survey study addresses cybersecurity adversarial circumstances and their importance for ARL and NIDS.Architectural design,RL algorithms,feature representation,and training methodologies are examined in the ARL-NIDS study.This comprehensive study evaluates ARL for intelligent NIDS research,benefiting cybersecurity researchers,practitioners,and policymakers.The report promotes cybersecurity defense research and innovation.展开更多
Parkinson’s disease remains a major clinical issue in terms of early detection,especially during its prodromal stage when symptoms are not evident or not distinct.To address this problem,we proposed a new deep learni...Parkinson’s disease remains a major clinical issue in terms of early detection,especially during its prodromal stage when symptoms are not evident or not distinct.To address this problem,we proposed a new deep learning 2-based approach for detecting Parkinson’s disease before any of the overt symptoms develop during their prodromal stage.We used 5 publicly accessible datasets,including UCI Parkinson’s Voice,Spiral Drawings,PaHaW,NewHandPD,and PPMI,and implemented a dual stream CNN–BiLSTM architecture with Fisher-weighted feature merging and SHAP-based explanation.The findings reveal that the model’s performance was superior and achieved 98.2%,a F1-score of 0.981,and AUC of 0.991 on the UCI Voice dataset.The model’s performance on the remaining datasets was also comparable,with up to a 2–7 percent betterment in accuracy compared to existing strong models such as CNN–RNN–MLP,ILN–GNet,and CASENet.Across the evidence,the findings back the diagnostic promise of micro-tremor assessment and demonstrate that combining temporal and spatial features with a scatter-based segment for a multi-modal approach can be an effective and scalable platform for an“early,”interpretable PD screening system.展开更多
The rapid growth of IoT networks necessitates efficient Intrusion Detection Systems(IDS)capable of addressing dynamic security threats under constrained resource environments.This paper proposes a hybrid IDS for IoT n...The rapid growth of IoT networks necessitates efficient Intrusion Detection Systems(IDS)capable of addressing dynamic security threats under constrained resource environments.This paper proposes a hybrid IDS for IoT networks,integrating Support Vector Machine(SVM)and Genetic Algorithm(GA)for feature selection and parameter optimization.The GA reduces the feature set from 41 to 7,achieving a 30%reduction in overhead while maintaining an attack detection rate of 98.79%.Evaluated on the NSL-KDD dataset,the system demonstrates an accuracy of 97.36%,a recall of 98.42%,and an F1-score of 96.67%,with a low false positive rate of 1.5%.Additionally,it effectively detects critical User-to-Root(U2R)attacks at a rate of 96.2%and Remote-to-Local(R2L)attacks at 95.8%.Performance tests validate the system’s scalability for networks with up to 2000 nodes,with detection latencies of 120 ms at 65%CPU utilization in small-scale deployments and 250 ms at 85%CPU utilization in large-scale scenarios.Parameter sensitivity analysis enhances model robustness,while false positive examination aids in reducing administrative overhead for practical deployment.This IDS offers an effective,scalable,and resource-efficient solution for real-world IoT system security,outperforming traditional approaches.展开更多
Online examinations have become a dominant assessment mode,increasing concerns over academic integrity.To address the critical challenge of detecting cheating behaviours,this study proposes a hybrid deep learning appr...Online examinations have become a dominant assessment mode,increasing concerns over academic integrity.To address the critical challenge of detecting cheating behaviours,this study proposes a hybrid deep learning approach that combines visual detection and temporal behaviour classification.The methodology utilises object detection models—You Only Look Once(YOLOv12),Faster Region-based Convolutional Neural Network(RCNN),and Single Shot Detector(SSD)MobileNet—integrated with classification models such as Convolutional Neural Networks(CNN),Bidirectional Gated Recurrent Unit(Bi-GRU),and CNN-LSTM(Long Short-Term Memory).Two distinct datasets were used:the Online Exam Proctoring(EOP)dataset from Michigan State University and the School of Computer Science,Duy Tan Unievrsity(SCS-DTU)dataset collected in a controlled classroom setting.A diverse set of cheating behaviours,including book usage,unauthorised interaction,internet access,and mobile phone use,was categorised.Comprehensive experiments evaluated the models based on accuracy,precision,recall,training time,inference speed,and memory usage.We evaluate nine detector-classifier pairings under a unified budget and score them via a calibrated harmonic mean of detection and classification accuracies,enabling deployment-oriented selection under latency and memory constraints.Macro-Precision/Recall/F1 and Receiver Operating Characteristic-Area Under the Curve(ROC-AUC)are reported for the top configurations,revealing consistent advantages of object-centric pipelines for fine-grained cheating cues.The highest overall score is achieved by YOLOv12+CNN(97.15%accuracy),while SSD-MobileNet+CNN provides the best speed-efficiency trade-off for edge devices.This research provides valuable insights into selecting and deploying appropriate deep learning models for maintaining exam integrity under varying resource constraints.展开更多
Small object detection has been a focus of attention since the emergence of deep learning-based object detection.Although classical object detection frameworks have made significant contributions to the development of...Small object detection has been a focus of attention since the emergence of deep learning-based object detection.Although classical object detection frameworks have made significant contributions to the development of object detection,there are still many issues to be resolved in detecting small objects due to the inherent complexity and diversity of real-world visual scenes.In particular,the YOLO(You Only Look Once)series of detection models,renowned for their real-time performance,have undergone numerous adaptations aimed at improving the detection of small targets.In this survey,we summarize the state-of-the-art YOLO-based small object detection methods.This review presents a systematic categorization of YOLO-based approaches for small-object detection,organized into four methodological avenues,namely attention-based feature enhancement,detection-head optimization,loss function,and multi-scale feature fusion strategies.We then examine the principal challenges addressed by each category.Finally,we analyze the performance of thesemethods on public benchmarks and,by comparing current approaches,identify limitations and outline directions for future research.展开更多
Illicit web ecosystems,encompassing phishing,illegal online gambling,scam platforms,and malicious advertising,have rapidly expanded in scale and complexity,creating severe social,financial,and cybersecurity risks.Trad...Illicit web ecosystems,encompassing phishing,illegal online gambling,scam platforms,and malicious advertising,have rapidly expanded in scale and complexity,creating severe social,financial,and cybersecurity risks.Traditional rule-based and blacklist-driven detection approaches struggle to cope with polymorphic,multilingual,and adversarially manipulated threats,resulting in increasing demand for Artificial Intelligence(AI)-based solutions.This review provides a comprehensive synthesis of research on AI-driven threat detection for illicit web environments.It surveys detection models across multiple modalities,including text-based analysis of Uniform Resource Locator(URL)and HyperText Markup Language(HTML),vision-based recognition of webpage layouts and logos,graphbased modeling of domain and infrastructure relationships,and sequence modeling using transformer architectures.In addition,the paper examines system architectures,data collection and labeling pipelines,real-time detection frameworks,and widely used benchmark datasets,while also discussing their inherent limitations related to imbalance,representativeness,and reproducibility.The review highlights critical challenges such as evasion strategies,cross-lingual detection barriers,deployment latency,and explainability gaps.Furthermore,it identifies emerging research directions,including the use of Generative Adversarial Network(GAN)for threat simulation,few-shot and self-supervised learning for data-scarce environments,Explainable Artificial Intelligence(XAI)for transparency,and predictive AI for proactive threat forecasting.By integrating technical,legal,and societal perspectives,this survey offers a structured foundation for researchers and practitioners to design resilient,adaptive,and trustworthy AI-based defense systems against illicit web threats.展开更多
The rapid proliferation of Internet of Things(IoT)devices in critical healthcare infrastructure has introduced significant security and privacy challenges that demand innovative,distributed architectural solutions.Thi...The rapid proliferation of Internet of Things(IoT)devices in critical healthcare infrastructure has introduced significant security and privacy challenges that demand innovative,distributed architectural solutions.This paper proposes FE-ACS(Fog-Edge Adaptive Cybersecurity System),a novel hierarchical security framework that intelligently distributes AI-powered anomaly detection algorithms across edge,fog,and cloud layers to optimize security efficacy,latency,and privacy.Our comprehensive evaluation demonstrates that FE-ACS achieves superior detection performance with an AUC-ROC of 0.985 and an F1-score of 0.923,while maintaining significantly lower end-to-end latency(18.7 ms)compared to cloud-centric(152.3 ms)and fog-only(34.5 ms)architectures.The system exhibits exceptional scalability,supporting up to 38,000 devices with logarithmic performance degradation—a 67×improvement over conventional cloud-based approaches.By incorporating differential privacy mechanisms with balanced privacy-utility tradeoffs(ε=1.0–1.5),FE-ACS maintains 90%–93%detection accuracy while ensuring strong privacy guarantees for sensitive healthcare data.Computational efficiency analysis reveals that our architecture achieves a detection rate of 12,400 events per second with only 12.3 mJ energy consumption per inference.In healthcare risk assessment,FE-ACS demonstrates robust operational viability with low patient safety risk(14.7%)and high system reliability(94.0%).The proposed framework represents a significant advancement in distributed security architectures,offering a scalable,privacy-preserving,and real-time solution for protecting healthcare IoT ecosystems against evolving cyber threats.展开更多
Breast cancer screening programs rely heavily on mammography for early detection;however,diagnostic performance is strongly affected by inter-reader variability,breast density,and the limitations of conven-tional comp...Breast cancer screening programs rely heavily on mammography for early detection;however,diagnostic performance is strongly affected by inter-reader variability,breast density,and the limitations of conven-tional computer-aided detection systems.Recent advances in deep learning have enabled more robust and scalable solutions for large-scale screening,yet a systematic comparison of modern object detection architectures on nationally representative datasets remains limited.This study presents a comprehensive quantitative comparison of prominent deep learning–based object detection architectures for Artificial Intelligence-assisted mammography analysis using the MammosighTR dataset,developed within the Turkish National Breast Cancer Screening Program.The dataset comprises 12,740 patient cases collected between 2016 and 2022,annotated with BI-RADS categories,breast density levels,and lesion localization labels.A total of 31 models were evaluated,including One-Stage,Two-Stage,and Transformer-based architectures,under a unified experimental framework at both patient and breast levels.The results demonstrate that Two-Stage architectures consistently outperform One-Stage models,achieving approximately 2%–4%higher Macro F1-Scores and more balanced precision–recall trade-offs,with Double-Head R-CNN and Dynamic R-CNN yielding the highest overall performance(Macro F1≈0.84–0.86).This advantage is primarily attributed to the region proposal mechanism and improved class balance inherent to Two-Stage designs.One-Stage detectors exhibited higher sensitivity and faster inference,reaching Recall values above 0.88,but experienced minor reductions in Precision and overall accuracy(≈1%–2%)compared with Two-Stage models.Among Transformer-based architectures,Deformable DEtection TRansformer demonstrated strong robustness and consistency across datasets,achieving Macro F1-Scores comparable to CNN-based detectors(≈0.83–0.85)while exhibiting minimal performance degradation under distributional shifts.Breast density–based analysis revealed increased misclassification rates in medium-density categories(types B and C),whereas Transformer-based architectures maintained more stable performance in high-density type D tissue.These findings quantitatively confirm that both architectural design and tissue characteristics play a decisive role in diagnostic accuracy.Overall,the study provides a reproducible benchmark and highlights the potential of hybrid approaches that combine the accuracy of Two-Stage detectors with the contextual modeling capability of Transformer architectures for clinically reliable breast cancer screening systems.展开更多
Log anomaly detection is essential for maintaining the reliability and security of large-scale networked systems.Most traditional techniques rely on log parsing in the reprocessing stage and utilize handcrafted featur...Log anomaly detection is essential for maintaining the reliability and security of large-scale networked systems.Most traditional techniques rely on log parsing in the reprocessing stage and utilize handcrafted features that limit their adaptability across various systems.In this study,we propose a hybrid model,BertGCN,that integrates BERT-based contextual embedding with Graph Convolutional Networks(GCNs)to identify anomalies in raw system logs,thereby eliminating the need for log parsing.TheBERT module captures semantic representations of log messages,while the GCN models the structural relationships among log entries through a text-based graph.This combination enables BertGCN to capture both the contextual and semantic characteristics of log data.BertGCN showed excellent performance on the HDFS and BGL datasets,demonstrating its effectiveness and resilience in detecting anomalies.Compared to multiple baselines,our proposed BertGCN showed improved precision,recall,and F1 scores.展开更多
Simultaneous identification and quantitative detection of phenylenediamine(PDA)isomers,including o-phenylenediamine(OPD),m-phenylenediamine(MPD),and p-phenylenediamine(PPD),are essential for environmental risk assessm...Simultaneous identification and quantitative detection of phenylenediamine(PDA)isomers,including o-phenylenediamine(OPD),m-phenylenediamine(MPD),and p-phenylenediamine(PPD),are essential for environmental risk assessment and human health protection.However,current visual detection methods can only distinguish individual PDA isomers and failed to identify binary or ternary mixtures.Herein,a highly active and ultrastable peroxidase(POD)-like CoPt graphitic nanozyme was used for naked-eye identification and colorimetric/fluorescent(FL)dual-mode quantitative detection of PDA isomers.The CoPt@G nanozyme effectively catalyzed the oxidation of OPD,MPD,PPD,OPD+PPD,OPD+MPD,MPD+PPD and OPD+MPD+PPD into yellow,colorless,lilac,yellow,yellow,wine red and reddish-brown products,respectively,in the presence of H_(2)O_(2).Thus,the MPD,PPD,MPD+PPD and OPD+MPD+PPD were easily identified based on the distinct color of their oxidation products,and the OPD,OPD+PPD,OPD+MPD could be further identified by the additional addition of MPD or PPD.Subsequently,CoPt@G/H_(2)O_(2)-,a 3,3′,5,5′-tetramethylbenzidine(TMB)/CoPt@G/H_(2)O_(2)-,and MPD/CoPt@G/H_(2)O_(2)-enabled colorimetric/FL dual-mode platforms for the quantitative detection of OPD,MPD and PPD were proposed.The experimental results illustrated that the constructed sensing platforms exhibit satisfactory sensitivity,comparable to that reported in previous studies.Finally,the evaluation of PDAs in water samples was realized,yielding satisfactory recoveries.This work expanded the application prospects of nanozymes in assessing environmental risks and protection of human security.展开更多
基金funded by the Natural Science Foundation of Shanghai Municipality(No.21ZR1440500)the Shanghai Science and Technology Commission(Grant No.21S31902700).
文摘In recent years,the rapid advancement of artificial intelligence(AI)technology has enabled AI-assisted negative screening to significantly enhance physicians'efficiency through image feature analysis and multimodal data modeling,allowing them to focus more on diagnosing positive cases.Meanwhile,multispectral imaging(MSI)integrates spectral and spatial resolution to capture subtle tissue features invisible to the human eye,providing high-resolution data support for pathological analysis.Combining AI technology with MSI and employing quantitative methods to analyze multiband biomarkers(such as absorbance differences in keratin pearls)can effectively improve diagnostic specificity and reduce subjective errors in manual slide interpretation.To address the challenge of identifying negative tissue sections,we developed a discrimination algorithm powered by MSI.We demonstrated its efficacy using cutaneous squamous cell carcinoma(cSCC)as a representative case study.The algorithm achieved 100%accuracy in excluding negative cases and effectively mitigated the false-positive problem caused by cSCC heterogeneity.We constructed a multispectral image(MSI)dataset acquired at 520 nm,600 nm,and 630 nm wavelengths.Subsequently,we employed an optimized MobileViT model for tissue classification and performed comparative analyses against other models.The experimental results showed that our optimized MobileViT model achieved superior performance in identifying negative tissue sections,with a perfect accuracy rate of 100%.Thus,our results confirm the feasibility of integrating MSI with AI to exclude negative cases with perfect accuracy,offering a novel solution to alleviate the workload of pathologists.
基金supported by the National Natural Science Foundation of China(No.62276204)the Fundamental Research Funds for the Central Universities,China(No.YJSJ24011)+1 种基金the Natural Science Basic Research Program of Shaanxi,China(Nos.2022JM-340 and 2023-JC-QN-0710)the China Postdoctoral Science Foundation(Nos.2020T130494 and 2018M633470)。
文摘Visible and infrared(RGB-IR)fusion object detection plays an important role in security,disaster relief,etc.In recent years,deep-learning-based RGB-IR fusion detection methods have been developing rapidly,but still struggle to deal with the complex and changing scenarios captured by drones,mainly due to two reasons:(A)RGB-IR fusion detectors are susceptible to inferior inputs that degrade performance and stability.(B)RGB-IR fusion detectors are susceptible to redundant features that reduce accuracy and efficiency.In this paper,an innovative RGB-IR fusion detection framework based on global-local feature optimization,named GLFDet,is proposed to improve the detection performance and efficiency of drone-captured objects.The key components of GLFDet include a Global Feature Optimization(GFO)module,a Local Feature Optimization(LFO)module and a Channel Separation Fusion(CSF)module.Specifically,GFO calculates the information content of the input image from the frequency domain and optimizes the features holistically.Then,LFO dynamically selects high-value features and filters out low-value features before fusion,which significantly improves the efficiency of fusion.Finally,CSF fuses the RGB and IR features across the corresponding channels,which avoids the rearrangement of the channel relationships and enhances the model stability.Extensive experimental results show that the proposed method achieves the best performance on three popular RGB-IR datasets Drone Vehicle,VEDAI,and LLVIP.In addition,GLFDet is more lightweight than other comparable models,making it more appealing to edge devices such as drones.The code is available at https://github.com/lao chen330/GLFDet.
文摘In this study,a multifunctional aptamer-conjugated magnetic covalent organic framework(COF)-CuO/Au nanozyme(MCOF-CuO/Au@apt)was developed as a“three-in-one”platform for dual-signal colorimetric and fluorescent detection of Vibrio parahaemolyticus.The nanozyme integrated magnetic separation,peroxidase-like catalytic activity,and specific target recognition through an aptamer-based strategy.Upon binding to V.parahaemolyticus,the catalytic oxidation of tetra-aminophenylethylene(TPE-4A)by the nanozyme was selectively inhibited,resulting in distinct colorimetric and fluorescent signals that significantly enhanced the detection accuracy and reliability.The proposed method exhibited high sensitivity,with limits of detection(LOD)of 21 and 7 CFU/mL for the colorimetric and fluorescent assays,respectively.The performance of this method was validated using real seafood samples,including Penaeus vannamei,Mytilus coruscus,and Crassostrea gigas,which showed high recovery rates(101.11%-107.30%)and excellent reproducibility.The system also demonstrated strong specificity and accuracy under various conditions,confirming its robustness and practical applicability.Collectively,this innovative platform presents a promising solution for the rapid,versatile,and sensitive detection of V.parahaemolyticus in seafood,with considerable potential to advance food safety diagnosis and on-site monitoring.
基金National Natural Science Foundation of China(32270237)Guangxi Key Research and Development Plan Project(Guike AB21238002)Basic Scientific Research Project of Guangxi Academy of Agricultural Sciences(Guinongke 2024YP082)。
文摘【Objective】This study aimed to establish a quintuple PCR method for rapid and simultaneous detection of Ralstonia solanacearum,Fusarium spp.,Pectobacterium spp.,Enterobacter spp.,and Pythium spp.,which provided technical support for early diagnosis of various soil-borne diseases on ginger.【Method】For five types of soil-borne pathogens causing ginger bacterial wilt and rhizome rot,specific primer combinations were designed and screened,the optimal quintuple reaction system was established by exploring optimal primer concentrations,annealing temperature,and sensitivity,and was applied to detect field plant samples to verify its utility.【Result】Specific primers pairs Rs1F/Rs1R,En1F/En1R,and Py1F/Py1R were designed according to flic gene of Ralstonia solanacearum,rpoB gene of Enterobacter spp.,and 18S rDNA of Pythium spp.,and combined with reported Fusarium spp.specific primers Fu3/Fu4 and specific primers 23SPecF/23SPecR of Pectobacterium spp.,a quintuple PCR reaction system for ginger soil-borne pathogens has been established(25.00μL):above primer dosage was 1.20,0.20,0.60,1.60,and 0.15μL respectively;2×PCR Mix 12.50μL;DNA templates of different pathogens were 1.00μL each;added ddH_(2)O to 25.00μL.Annealing temperature was optimized to 55.4℃.The specific fragments with sizes of 516,370,266,207,and 159 bp could be amplified simultaneously in the established quintuple PCR system,and the detection limit of this system for Ralstonia solanacearum,Enterobacter spp.and Pythium spp.reached 10^(-1)pg/μL,for Fusarium spp.and Pectobacterium spp.was 1 pg/μL,and for detecting five pathogens simultaneously was 10^(3)pg/μL.The multiplex PCR system established in this study could successfully detect the diseased plant samples from the field.【Conclusion】The quintuple PCR system established is able to rapid ly and accurately detect Ralstonia solanacearum,Fusarium spp.,Pectobacterium spp.,Enterobacter spp.,and Pythium spp.,which provides a useful tool for timely diagnosis and epidemic monitoring of various soil-borne diseases of ginger.
基金supported in part by the National Natural Science Foundation of China(U25A20473,62222314)the YanZhao Young Scientist Project of Hebei Province(F2024203047)+2 种基金the Natural Science Foundation of Hebei Province(F2022203001,F2024203072)the State Key Laboratory of Submarine Geoscience(sglkt2025-7)the Education Department Foundation of Hebei Province(JCZX2025027)。
文摘Dear Editor,This letter studies the motion planning issue for an autonomous underwater vehicle(AUV)in obstacle environment.We propose a novel integrated detection-communication waveform that enables simultaneous obstacle detection and self-localization.
基金supported by Hebei North University Doctoral Research Fund Project(No.BSJJ202315)the Youth Research Fund Project of Higher Education Institutions in Hebei Province(No.QN2024146).
文摘In the field of smart agriculture,accurate and efficient object detection technology is crucial for automated crop management.A particularly challenging task in this domain is small object detection,such as the identification of immature fruits or early stage disease spots.These objects pose significant difficulties due to their small pixel coverage,limited feature information,substantial scale variations,and high susceptibility to complex background interference.These challenges frequently result in inadequate accuracy and robustness in current detection models.This study addresses two critical needs in the cashew cultivation industry—fruitmaturity and anthracnose detection—by proposing an improved YOLOv11-NSDDil model.The method introduces three key technological innovations:(1)The SDDil module is designed and integrated into the backbone network.This module combines depthwise separable convolution with the SimAM attention mechanism to expand the receptive field and enhance contextual semantic capture at a low computational cost,effectively alleviating the feature deficiency problem caused by limited pixel coverage of small objects.Simultaneously,the SDmodule dynamically enhances discriminative features and suppresses background noise,significantly improving the model’s feature discrimination capability in complex environments;(2)The introduction of the DynamicScalSeq-Zoom_cat neck network,significantly improving multi-scale feature fusion;and(3)The optimization of the Minimum Point Distance Intersection over Union(MPDIoU)loss function,which enhances bounding box localization accuracy byminimizing vertex distance.Experimental results on a self-constructed cashew dataset containing 1123 images demonstrate significant performance improvements in the enhanced model:mAP50 reaches 0.825,a 4.6% increase compared to the originalYOLOv11;mAP50-95 improves to 0.624,a 6.5% increase;and recall rises to 0.777,a 2.4%increase.This provides a reliable technical solution for intelligent quality inspection of agricultural products and holds broad application prospects.
文摘Zero-day attacks present a critical cybersecurity challenge for Internet of things(IoT)infrastructures,where the inability of signature-based intrusion detection systems(IDSs)to recognize novel threat behaviors compromises both system reliability and operational continuity.Existing hybrid IDS solutions often struggle to balance accurate classification of known attacks with reliable anomaly detection,particularly under the computational constraints of IoT environments.To address this gap,we introduce ZeroDefense,an adaptive fusion-based IDS designed for simultaneous detection of known intrusions and emerging zero-day threats.The framework employs a four-layer architecture consisting of i)feature standardization and class balancing,ii)anomaly detection using isolation forest,autoencoder,and local outlier factor,iii)fine-grained attack classification via random forest,extreme gradient boosting(XGBoost),light gradient boosting machine(LightGBM),and attentive interpretable tabular learning(TabNet),and iv)a confidence-aware fusion engine that adaptively selects the most reliable decision path.Suspicious or previously unseen traffic is isolated early through fused anomaly scoring,while benign and known-malicious flows are processed through supervised classification for precise attack labeling.With an anomaly cascaded decision pipeline,a dynamic confidence-driven fusion mechanism,and a deploymentconscious design,ZeroDefense enables real-time inference on IoT edge gateways.Evaluation on the CICIoT2023 benchmark demonstrates 99.94% overall accuracy and 95.64%macro-average F1-score for known attacks,while 5.76% of traffic is successfully flagged as potential zero-day activity,with inference latency maintained below 100 ms/flow.These results indicate that ZeroDefense offers a scalable,resilient,and practically deployable defense capability for modern IoT infrastructures.
基金supported by the Extral High Voltage Power Transmission Company,China Southern Power Grid Co.,Ltd.
文摘Distributed Denial-of-Service(DDoS)attacks pose severe threats to Industrial Control Networks(ICNs),where service disruption can cause significant economic losses and operational risks.Existing signature-based methods are ineffective against novel attacks,and traditional machine learning models struggle to capture the complex temporal dependencies and dynamic traffic patterns inherent in ICN environments.To address these challenges,this study proposes a deep feature-driven hybrid framework that integrates Transformer,BiLSTM,and KNN to achieve accurate and robust DDoS detection.The Transformer component extracts global temporal dependencies from network traffic flows,while BiLSTM captures fine-grained sequential dynamics.The learned embeddings are then classified using an instance-based KNN layer,enhancing decision boundary precision.This cascaded architecture balances feature abstraction and locality preservation,improving both generalization and robustness.The proposed approach was evaluated on a newly collected real-time ICN traffic dataset and further validated using the public CIC-IDS2017 and Edge-IIoT datasets to demonstrate generalization.Comprehensive metrics including accuracy,precision,recall,F1-score,ROC-AUC,PR-AUC,false positive rate(FPR),and detection latency were employed.Results show that the hybrid framework achieves 98.42%accuracy with an ROC-AUC of 0.992 and FPR below 1%,outperforming baseline machine learning and deep learning models.Robustness experiments under Gaussian noise perturbations confirmed stable performance with less than 2%accuracy degradation.Moreover,detection latency remained below 2.1 ms per sample,indicating suitability for real-time ICS deployment.In summary,the proposed hybrid temporal learning and instance-based classification model offers a scalable and effective solution for DDoS detection in industrial control environments.By combining global contextual modeling,sequential learning,and instance-based refinement,the framework demonstrates strong adaptability across datasets and resilience against noise,providing practical utility for safeguarding critical infrastructure.
文摘Modern manufacturing processes have become more reliant on automation because of the accelerated transition from Industry 3.0 to Industry 4.0.Manual inspection of products on assembly lines remains inefficient,prone to errors and lacks consistency,emphasizing the need for a reliable and automated inspection system.Leveraging both object detection and image segmentation approaches,this research proposes a vision-based solution for the detection of various kinds of tools in the toolkit using deep learning(DL)models.Two Intel RealSense D455f depth cameras were arranged in a top down configuration to capture both RGB and depth images of the toolkits.After applying multiple constraints and enhancing them through preprocessing and augmentation,a dataset consisting of 3300 annotated RGB-D photos was generated.Several DL models were selected through a comprehensive assessment of mean Average Precision(mAP),precision-recall equilibrium,inference latency(target≥30 FPS),and computational burden,resulting in a preference for YOLO and Region-based Convolutional Neural Networks(R-CNN)variants over ViT-based models due to the latter’s increased latency and resource requirements.YOLOV5,YOLOV8,YOLOV11,Faster R-CNN,and Mask R-CNN were trained on the annotated dataset and evaluated using key performance metrics(Recall,Accuracy,F1-score,and Precision).YOLOV11 demonstrated balanced excellence with 93.0%precision,89.9%recall,and a 90.6%F1-score in object detection,as well as 96.9%precision,95.3%recall,and a 96.5%F1-score in instance segmentation with an average inference time of 25 ms per frame(≈40 FPS),demonstrating real-time performance.Leveraging these results,a YOLOV11-based windows application was successfully deployed in a real-time assembly line environment,where it accurately processed live video streams to detect and segment tools within toolkits,demonstrating its practical effectiveness in industrial automation.The application is capable of precisely measuring socket dimensions by utilising edge detection techniques on YOLOv11 segmentation masks,in addition to detection and segmentation.This makes it possible to do specification-level quality control right on the assembly line,which improves the ability to examine things in real time.The implementation is a big step forward for intelligent manufacturing in the Industry 4.0 paradigm.It provides a scalable,efficient,and accurate way to do automated inspection and dimensional verification activities.
基金the Shandong Provincial Key Research and Development Program(Grant No.2024SFGC0201).
文摘To address critical challenges in nighttime ship detection—high small-target missed detection(over 20%),insufficient lightweighting,and limited generalization due to scarce,low-quality datasets—this study proposes a systematic solution.First,a high-quality Night-Ships dataset is constructed via CycleGAN-based day-night transfer,combined with a dual-threshold cleaning strategy(Laplacian variance sharpness filtering and brightness-color deviation screening).Second,a Cross-stage Lightweight Fusion-You Only Look Once version 8(CLF-YOLOv8)is proposed with key improvements:the Neck network is reconstructed by replacing Cross Stage Partial(CSP)structure with the Cross Stage Partial Multi-Scale Convolutional Block(CSP-MSCB)and integrating Bidirectional Feature Pyramid Network(BiFPN)for weighted multi-scale fusion to enhance small-target detection;a Lightweight Shared Convolutional and Separated Batch Normalization Detection-Head(LSCSBD-Head)with shared convolutions and layer-wise Batch Normalization(BN)reduces parameters to 1.8M(42% fewer than YOLOv8n);and the FocalMinimum Point Distance Intersection over Union(Focal-MPDIoU)loss combines Minimum Point Distance Intersection over Union(MPDIoU)geometric constraints and Focal weighting to optimize low-overlap targets.Experiments show CLFYOLOv8 achieves 97.6%mAP@0.5(0.7% higher than YOLOv8n)with 1.8 M parameters,outperforming mainstream models in small-target detection,overlapping target discrimination,and adaptability to complex lighting.
文摘Adversarial Reinforcement Learning(ARL)models for intelligent devices and Network Intrusion Detection Systems(NIDS)improve systemresilience against sophisticated cyber-attacks.As a core component of ARL,Adversarial Training(AT)enables NIDS agents to discover and prevent newattack paths by exposing them to competing examples,thereby increasing detection accuracy,reducing False Positives(FPs),and enhancing network security.To develop robust decision-making capabilities for real-world network disruptions and hostile activity,NIDS agents are trained in adversarial scenarios to monitor the current state and notify management of any abnormal or malicious activity.The accuracy and timeliness of the IDS were crucial to the network’s availability and reliability at this time.This paper analyzes ARL applications in NIDS,revealing State-of-The-Art(SoTA)methodology,issues,and future research prospects.This includes Reinforcement Machine Learning(RML)-based NIDS,which enables an agent to interact with the environment to achieve a goal,andDeep Reinforcement Learning(DRL)-based NIDS,which can solve complex decision-making problems.Additionally,this survey study addresses cybersecurity adversarial circumstances and their importance for ARL and NIDS.Architectural design,RL algorithms,feature representation,and training methodologies are examined in the ARL-NIDS study.This comprehensive study evaluates ARL for intelligent NIDS research,benefiting cybersecurity researchers,practitioners,and policymakers.The report promotes cybersecurity defense research and innovation.
基金supported via funding from Prince Sattam bin Abdulaziz University project number(PSAU/2025/03/32440).
文摘Parkinson’s disease remains a major clinical issue in terms of early detection,especially during its prodromal stage when symptoms are not evident or not distinct.To address this problem,we proposed a new deep learning 2-based approach for detecting Parkinson’s disease before any of the overt symptoms develop during their prodromal stage.We used 5 publicly accessible datasets,including UCI Parkinson’s Voice,Spiral Drawings,PaHaW,NewHandPD,and PPMI,and implemented a dual stream CNN–BiLSTM architecture with Fisher-weighted feature merging and SHAP-based explanation.The findings reveal that the model’s performance was superior and achieved 98.2%,a F1-score of 0.981,and AUC of 0.991 on the UCI Voice dataset.The model’s performance on the remaining datasets was also comparable,with up to a 2–7 percent betterment in accuracy compared to existing strong models such as CNN–RNN–MLP,ILN–GNet,and CASENet.Across the evidence,the findings back the diagnostic promise of micro-tremor assessment and demonstrate that combining temporal and spatial features with a scatter-based segment for a multi-modal approach can be an effective and scalable platform for an“early,”interpretable PD screening system.
文摘The rapid growth of IoT networks necessitates efficient Intrusion Detection Systems(IDS)capable of addressing dynamic security threats under constrained resource environments.This paper proposes a hybrid IDS for IoT networks,integrating Support Vector Machine(SVM)and Genetic Algorithm(GA)for feature selection and parameter optimization.The GA reduces the feature set from 41 to 7,achieving a 30%reduction in overhead while maintaining an attack detection rate of 98.79%.Evaluated on the NSL-KDD dataset,the system demonstrates an accuracy of 97.36%,a recall of 98.42%,and an F1-score of 96.67%,with a low false positive rate of 1.5%.Additionally,it effectively detects critical User-to-Root(U2R)attacks at a rate of 96.2%and Remote-to-Local(R2L)attacks at 95.8%.Performance tests validate the system’s scalability for networks with up to 2000 nodes,with detection latencies of 120 ms at 65%CPU utilization in small-scale deployments and 250 ms at 85%CPU utilization in large-scale scenarios.Parameter sensitivity analysis enhances model robustness,while false positive examination aids in reducing administrative overhead for practical deployment.This IDS offers an effective,scalable,and resource-efficient solution for real-world IoT system security,outperforming traditional approaches.
文摘Online examinations have become a dominant assessment mode,increasing concerns over academic integrity.To address the critical challenge of detecting cheating behaviours,this study proposes a hybrid deep learning approach that combines visual detection and temporal behaviour classification.The methodology utilises object detection models—You Only Look Once(YOLOv12),Faster Region-based Convolutional Neural Network(RCNN),and Single Shot Detector(SSD)MobileNet—integrated with classification models such as Convolutional Neural Networks(CNN),Bidirectional Gated Recurrent Unit(Bi-GRU),and CNN-LSTM(Long Short-Term Memory).Two distinct datasets were used:the Online Exam Proctoring(EOP)dataset from Michigan State University and the School of Computer Science,Duy Tan Unievrsity(SCS-DTU)dataset collected in a controlled classroom setting.A diverse set of cheating behaviours,including book usage,unauthorised interaction,internet access,and mobile phone use,was categorised.Comprehensive experiments evaluated the models based on accuracy,precision,recall,training time,inference speed,and memory usage.We evaluate nine detector-classifier pairings under a unified budget and score them via a calibrated harmonic mean of detection and classification accuracies,enabling deployment-oriented selection under latency and memory constraints.Macro-Precision/Recall/F1 and Receiver Operating Characteristic-Area Under the Curve(ROC-AUC)are reported for the top configurations,revealing consistent advantages of object-centric pipelines for fine-grained cheating cues.The highest overall score is achieved by YOLOv12+CNN(97.15%accuracy),while SSD-MobileNet+CNN provides the best speed-efficiency trade-off for edge devices.This research provides valuable insights into selecting and deploying appropriate deep learning models for maintaining exam integrity under varying resource constraints.
基金supported in part by the by Chongqing Research Program of Basic Research and Frontier Technology under Grant CSTB2025NSCQ-GPX1309.
文摘Small object detection has been a focus of attention since the emergence of deep learning-based object detection.Although classical object detection frameworks have made significant contributions to the development of object detection,there are still many issues to be resolved in detecting small objects due to the inherent complexity and diversity of real-world visual scenes.In particular,the YOLO(You Only Look Once)series of detection models,renowned for their real-time performance,have undergone numerous adaptations aimed at improving the detection of small targets.In this survey,we summarize the state-of-the-art YOLO-based small object detection methods.This review presents a systematic categorization of YOLO-based approaches for small-object detection,organized into four methodological avenues,namely attention-based feature enhancement,detection-head optimization,loss function,and multi-scale feature fusion strategies.We then examine the principal challenges addressed by each category.Finally,we analyze the performance of thesemethods on public benchmarks and,by comparing current approaches,identify limitations and outline directions for future research.
基金supported by the Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea goverment(MSIT)(No.RS-2024-00439139,Development of a Cyber Crisis Response and Resilience Test Evaluation Systems)this research was supported by the MSIT(Ministry of Science and ICT),Korea,under the Graduate School of Virtual Convergence support program(IITP-2026-RS-2023-00254129)supervised by the IITP(Institute for Information&Communications Technology Planning&Evaluation)+1 种基金supported by the“Regional Innovation System&Education(RISE)”through the Seoul RISE Center,funded by the Ministry of Education(MOE)and the Seoul Metropolitan Government(2026-RISE-01-018-05)supported by QuadMiners Corp.
文摘Illicit web ecosystems,encompassing phishing,illegal online gambling,scam platforms,and malicious advertising,have rapidly expanded in scale and complexity,creating severe social,financial,and cybersecurity risks.Traditional rule-based and blacklist-driven detection approaches struggle to cope with polymorphic,multilingual,and adversarially manipulated threats,resulting in increasing demand for Artificial Intelligence(AI)-based solutions.This review provides a comprehensive synthesis of research on AI-driven threat detection for illicit web environments.It surveys detection models across multiple modalities,including text-based analysis of Uniform Resource Locator(URL)and HyperText Markup Language(HTML),vision-based recognition of webpage layouts and logos,graphbased modeling of domain and infrastructure relationships,and sequence modeling using transformer architectures.In addition,the paper examines system architectures,data collection and labeling pipelines,real-time detection frameworks,and widely used benchmark datasets,while also discussing their inherent limitations related to imbalance,representativeness,and reproducibility.The review highlights critical challenges such as evasion strategies,cross-lingual detection barriers,deployment latency,and explainability gaps.Furthermore,it identifies emerging research directions,including the use of Generative Adversarial Network(GAN)for threat simulation,few-shot and self-supervised learning for data-scarce environments,Explainable Artificial Intelligence(XAI)for transparency,and predictive AI for proactive threat forecasting.By integrating technical,legal,and societal perspectives,this survey offers a structured foundation for researchers and practitioners to design resilient,adaptive,and trustworthy AI-based defense systems against illicit web threats.
基金supported by the Deanship of Graduate Studies and Scientific Research at Jouf University under grant No.(DGSSR-2025-02-01276).
文摘The rapid proliferation of Internet of Things(IoT)devices in critical healthcare infrastructure has introduced significant security and privacy challenges that demand innovative,distributed architectural solutions.This paper proposes FE-ACS(Fog-Edge Adaptive Cybersecurity System),a novel hierarchical security framework that intelligently distributes AI-powered anomaly detection algorithms across edge,fog,and cloud layers to optimize security efficacy,latency,and privacy.Our comprehensive evaluation demonstrates that FE-ACS achieves superior detection performance with an AUC-ROC of 0.985 and an F1-score of 0.923,while maintaining significantly lower end-to-end latency(18.7 ms)compared to cloud-centric(152.3 ms)and fog-only(34.5 ms)architectures.The system exhibits exceptional scalability,supporting up to 38,000 devices with logarithmic performance degradation—a 67×improvement over conventional cloud-based approaches.By incorporating differential privacy mechanisms with balanced privacy-utility tradeoffs(ε=1.0–1.5),FE-ACS maintains 90%–93%detection accuracy while ensuring strong privacy guarantees for sensitive healthcare data.Computational efficiency analysis reveals that our architecture achieves a detection rate of 12,400 events per second with only 12.3 mJ energy consumption per inference.In healthcare risk assessment,FE-ACS demonstrates robust operational viability with low patient safety risk(14.7%)and high system reliability(94.0%).The proposed framework represents a significant advancement in distributed security architectures,offering a scalable,privacy-preserving,and real-time solution for protecting healthcare IoT ecosystems against evolving cyber threats.
文摘Breast cancer screening programs rely heavily on mammography for early detection;however,diagnostic performance is strongly affected by inter-reader variability,breast density,and the limitations of conven-tional computer-aided detection systems.Recent advances in deep learning have enabled more robust and scalable solutions for large-scale screening,yet a systematic comparison of modern object detection architectures on nationally representative datasets remains limited.This study presents a comprehensive quantitative comparison of prominent deep learning–based object detection architectures for Artificial Intelligence-assisted mammography analysis using the MammosighTR dataset,developed within the Turkish National Breast Cancer Screening Program.The dataset comprises 12,740 patient cases collected between 2016 and 2022,annotated with BI-RADS categories,breast density levels,and lesion localization labels.A total of 31 models were evaluated,including One-Stage,Two-Stage,and Transformer-based architectures,under a unified experimental framework at both patient and breast levels.The results demonstrate that Two-Stage architectures consistently outperform One-Stage models,achieving approximately 2%–4%higher Macro F1-Scores and more balanced precision–recall trade-offs,with Double-Head R-CNN and Dynamic R-CNN yielding the highest overall performance(Macro F1≈0.84–0.86).This advantage is primarily attributed to the region proposal mechanism and improved class balance inherent to Two-Stage designs.One-Stage detectors exhibited higher sensitivity and faster inference,reaching Recall values above 0.88,but experienced minor reductions in Precision and overall accuracy(≈1%–2%)compared with Two-Stage models.Among Transformer-based architectures,Deformable DEtection TRansformer demonstrated strong robustness and consistency across datasets,achieving Macro F1-Scores comparable to CNN-based detectors(≈0.83–0.85)while exhibiting minimal performance degradation under distributional shifts.Breast density–based analysis revealed increased misclassification rates in medium-density categories(types B and C),whereas Transformer-based architectures maintained more stable performance in high-density type D tissue.These findings quantitatively confirm that both architectural design and tissue characteristics play a decisive role in diagnostic accuracy.Overall,the study provides a reproducible benchmark and highlights the potential of hybrid approaches that combine the accuracy of Two-Stage detectors with the contextual modeling capability of Transformer architectures for clinically reliable breast cancer screening systems.
基金funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,under grant no.(GPIP:1074-612-2024).
文摘Log anomaly detection is essential for maintaining the reliability and security of large-scale networked systems.Most traditional techniques rely on log parsing in the reprocessing stage and utilize handcrafted features that limit their adaptability across various systems.In this study,we propose a hybrid model,BertGCN,that integrates BERT-based contextual embedding with Graph Convolutional Networks(GCNs)to identify anomalies in raw system logs,thereby eliminating the need for log parsing.TheBERT module captures semantic representations of log messages,while the GCN models the structural relationships among log entries through a text-based graph.This combination enables BertGCN to capture both the contextual and semantic characteristics of log data.BertGCN showed excellent performance on the HDFS and BGL datasets,demonstrating its effectiveness and resilience in detecting anomalies.Compared to multiple baselines,our proposed BertGCN showed improved precision,recall,and F1 scores.
基金supported by the National Key Research and Development Program of China(No.2022YFC2403500)the National Natural Science Foundation of China(No.22225401)+1 种基金the Science and Technology Innovation Program of Hunan Province(No.2020RC4017)the Guizhou Provincial Science and Technology Projects(No.ZK[2023]293).
文摘Simultaneous identification and quantitative detection of phenylenediamine(PDA)isomers,including o-phenylenediamine(OPD),m-phenylenediamine(MPD),and p-phenylenediamine(PPD),are essential for environmental risk assessment and human health protection.However,current visual detection methods can only distinguish individual PDA isomers and failed to identify binary or ternary mixtures.Herein,a highly active and ultrastable peroxidase(POD)-like CoPt graphitic nanozyme was used for naked-eye identification and colorimetric/fluorescent(FL)dual-mode quantitative detection of PDA isomers.The CoPt@G nanozyme effectively catalyzed the oxidation of OPD,MPD,PPD,OPD+PPD,OPD+MPD,MPD+PPD and OPD+MPD+PPD into yellow,colorless,lilac,yellow,yellow,wine red and reddish-brown products,respectively,in the presence of H_(2)O_(2).Thus,the MPD,PPD,MPD+PPD and OPD+MPD+PPD were easily identified based on the distinct color of their oxidation products,and the OPD,OPD+PPD,OPD+MPD could be further identified by the additional addition of MPD or PPD.Subsequently,CoPt@G/H_(2)O_(2)-,a 3,3′,5,5′-tetramethylbenzidine(TMB)/CoPt@G/H_(2)O_(2)-,and MPD/CoPt@G/H_(2)O_(2)-enabled colorimetric/FL dual-mode platforms for the quantitative detection of OPD,MPD and PPD were proposed.The experimental results illustrated that the constructed sensing platforms exhibit satisfactory sensitivity,comparable to that reported in previous studies.Finally,the evaluation of PDAs in water samples was realized,yielding satisfactory recoveries.This work expanded the application prospects of nanozymes in assessing environmental risks and protection of human security.
