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A Dual-Detection Method for Cashew Ripeness and Anthrax Based on YOLOv11-NSDDil 认领 引用
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作者 Ran Liu Yawen Chen +1 位作者 Dong Yang Jingjing Yang 《Computers, Materials & Continua》 SCIE EI 2026年第2期1919-1941,共23页
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
关键词 Deep learning object detection multi-scale fusion small object detection miss detection false detection
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Rapid and ultrasensitive point-of-care detection of ASFV antibodies using p30-Fc-labeled nanoparticle-based fluorescence strip-assisted portable immunosensor 认领 引用
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作者 Yang Yang Jiayang Zheng +10 位作者 Yan Zhang Qianming Zhao Yafang Lin Junjie Zhang Zongjie Li Ke Liu Beibei Li Donghua Shao Yafeng Qiu Zhiyong Ma Jianchao Wei 《Journal of Integrative Agriculture》 SCIE CAS CSCD 2026年第6期2630-2633,共4页
Highlights A portable immunosensor device assisted by p30-Fc-labeled nanoparticle-based fiuorescence strips was developed for the rapid detection of African swine fever virus(ASFV)antibodies in the field.The fluoresce... Highlights A portable immunosensor device assisted by p30-Fc-labeled nanoparticle-based fiuorescence strips was developed for the rapid detection of African swine fever virus(ASFV)antibodies in the field.The fluorescent test strip prepared in this study has the advantages of low cost,high specificity and sensitivity,good repeatability and stability.The fiuorescent test strip detection method in this study has extremely high sensitivity and antibodies can be detected four days after ASFV infection,which has high potential application value in the monitoring and control of African swine fever. 展开更多
关键词 fiuorescence strips portable immunosensor device african swine fever ultrasensitive detection fluorescence strip rapid detection fiuorescent test strip detection p fc labeled nanoparticle
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Multi-Class Severity-Aware Fire and Smoke Detection Using YOLOvl2 for Sustainable Intelligent Real-Time Monitoring 认领 引用
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作者 Aminah Almehmadi Ayman Noor +2 位作者 Aziza I.Noor Hanan Almukhalfi Talal H.Noor 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第6期1068-1103,共36页
Fire emergencies have long posed a serious threat to people's lives,real estate assets,and environmental sustainability in civilized societies,especially when combustible events are detected at late stages of deve... Fire emergencies have long posed a serious threat to people's lives,real estate assets,and environmental sustainability in civilized societies,especially when combustible events are detected at late stages of development.Recent advancements in computer vision-based fire detection have enabled automated real-time monitoring;however,most solutions either detect the existence of fire/smoke or employ binary decision-making,which limits visual monitoring systems from being risk-aware.This work introduces a severity-aware fire/smoke detection model that supports intelligent monitoring systems in detecting visual hazards.The goal is to identify varying levels of fire intensity and smoke density and to detect humans in real time.We design a system capable of monitoring environments using components such as sensing devices,network communication buses,cloud data centers,and computer visionbased detectors.The fire/smoke detection model comprises modern deep learning-based object detectors,with the YOLOvl2 model serving as the detection backbone.Moreover,our work proposes a two-stage training method that first learns coarse representations of fire,smoke,and humans,and then adapts the detector for fine-grained,severityaware classification,thereby enhancing severity discrimination and reducing inter-class confusion.We gathered our dataset to comprise approximately 65oo annotated images,split between coarse-grained and severity-aware detection models.The dataset consists of seven classes indicating human presence,three classes indicating varying levels of fire intensity,and three classes indicating varying levels of smoke density.We conducted experiments comparing three baseline object detection architectures(i.e.,YOLOvl2s,RT-DETR-L,and SSDLite320-MobileNetV3)using identical trainingesting configurations.YOLOvl2 has outperformed other baseline object detection architectures,achieving 0.929 mAP@50,0.884 precision,0.876 recall,0.880 F1-score,and 2.48 ms per-image latency,providing the best balance between detection performance and real-time processing capability.Our results indicate that severity-aware detection can improve the early-stage detection of intelligent monitoring systems. 展开更多
关键词 Fire detection smoke detection person detection severity-aware detection YOLOvl2 real-time monitoring
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ZeroDefense:An adaptive hybrid fusion-based intrusion detection system for zero-day threat detection in IoT networks 认领 引用 被引量：1
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作者 Abubakar Wakili Sara Bakkali 《Journal of Electronic Science and Technology》 EI CAS CSCD 2026年第1期29-45,共17页
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
关键词 Anomaly detection Hybrid fusion Internet of things(IoT) Intrusion detection system IoT security Resilient digital infrastructure Zero-day detection
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A lightweight single stage small object detection network based on knowledge distillation and novel feature networks 认领 引用 被引量：1
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作者 Miao ZHANG Xuefang ZHOU Junchao HU 《Optoelectronics Letters》 EI 2026年第5期282-288,共7页
With the increasing demand for traffic sign detection,the challenge of small target detection has become particularly prominent.The present study proposes an innovative approach by integrating knowledge distillation,L... With the increasing demand for traffic sign detection,the challenge of small target detection has become particularly prominent.The present study proposes an innovative approach by integrating knowledge distillation,L2 loss function,and convolutional block attention module(CBAM)mechanism to effectively tackle this issue.This series of improvements not only provide a new idea for small target detection,but also bring significant performance improvement in actual traffic scenes.Then,the integration method of the bidirectional feature pyramid network(BiFPN)is used to enhance the flexibility of the neural network to deal with input of different scales,while speeding up and improving the process of feature fusion.The experimental results demonstrate that when processing the Chinese city traffic sign detection benchmark(CCTSDB)dataset and executing the FLOW-IMG small target detection task,the optimized algorithm shows obvious performance improvement,and its accurate recognition rate jumps to 97%and 84.9%,respectively.For the basic algorithm,two datasets achieved improved accuracy by an innovative approach,improving accuracy by 5.8%and 1.3%,respectively.In terms of resource efficiency,compared to the original teacher model,the newly constructed model reduced the number of computing participants by approximately 15%during execution,while successfully reducing the overall computing task load by 14%. 展开更多
关键词 traffic sign detectionthe integration method knowledge distillationl loss functionand L loss function knowledge distillation small target detection small target detectionbut bidirectional feature pyramid network
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Algorithm improvement for traffic sign detection based on YOLOv8 认领 引用 被引量：1
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作者 Dongmei MA Xuelong LYU Qirong ZHU 《Optoelectronics Letters》 EI 2026年第2期92-97,共6页
An improved algorithm for traffic sign detection based on YOLOv8 is proposed. Firstly, YOLOv8n is used as the base model of the network, the inverted residual mobile block and exponential moving average(iRMB_EMA) atte... An improved algorithm for traffic sign detection based on YOLOv8 is proposed. Firstly, YOLOv8n is used as the base model of the network, the inverted residual mobile block and exponential moving average(iRMB_EMA) attention mechanism is used to improve the model's ability to perceive small targets, which reduces the leakage detection phenomenon of the model, convolution(Conv) is upgraded to receptive-field attention convolution(RFAConv), which improves the model's ability to deal with details and complexity in the image, the idea of adaptive spatial feature fusion(ASFF) is introduced in the detection head, and the small target detection layer, a four-head detection head is designed to improve the model's ability to detect small targets, solves the case of feature loss due to cross-scale fusion, and use of the Inner-minimum points distance intersection over union(MPDIoU) loss function provides a more accurate loss metric by calculating the distance of key points between the predicted and true frames. The experimental results of this algorithm on the public dataset CCTSDB show that the improved model mean average precision(m AP) reaches 82.6%, which is 4% higher than the YOLOv8n. The experimental results of dataset TT100k show that the m AP reaches 84.5%, which is 7.1% higher than the YOLOv8n. This algorithm effectively improves the problem of detail perception and leakage of the model in the detection of small targets, and has a significant detection effect compared to other algorithms. 展开更多
关键词 inverted residual mobile block traffic sign detection perceive small targets leakage detection phenomenon YOLOv base model deal details
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Motion Planning of an Autonomous Underwater Vehicle via the Integrated Design of Detection,Communication and Control 认领 引用
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作者 Tianyi Guo Jing Yan +2 位作者 Xian Yang Tianyi Zhang Xinping Guan 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第1期218-220,共3页
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
关键词 communication waveform motion planning obstacle detection autonomous underwater vehicle integrated detection simultaneous obstacle detection autonomous underwater vehicle auv obstacle environment
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IntrusionNet:Deep Learning-Based Hybrid Model for Detection of Known and Zero-Day Attacks 认领 引用
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作者 Sarmad Dheyaa Azeez Saadaldeen Rashid Ahmed +3 位作者 Muhammad Ilyas Abu Saleh Musa Miah Fahmid Al Farid Md.Hezerul Abdul Karim 《Computers, Materials & Continua》 SCIE EI 2026年第7期1220-1241,共22页
Traditional Intrusion Detection Systems(IDSs)that rely on fixed signatures or basic machine learning often struggle with sophisticated,multi-stage cyberattacks and previously unknown threats.To fix these problems,this... Traditional Intrusion Detection Systems(IDSs)that rely on fixed signatures or basic machine learning often struggle with sophisticated,multi-stage cyberattacks and previously unknown threats.To fix these problems,this paper introduces IntrusionNet,a mixed deep learning system that combines Convolutional Neural Networks(CNN),Recurrent Neural Networks(RNN),and Autoencoders in a two-part design.Differing from typical stacked models,IntrusionNet works on two levels at the same time.First,a supervised CNN-RNN process pulls spatial-temporal data from traffic flows to sort well-known attack patterns.Second,an unsupervised Autoencoder process spots new anomalies by looking at reconstruction error limits.This approach allows the automatic learning of threat traits as they change,without needing someone to do it by hand.The system was tested on the UNSW-NB15 data set,picked because it realistically includes many kinds of attacks,like Fuzzers,Shellcode,and Worms.Tests show that IntrusionNet gets an accuracy of 98.80%and an F1-score of 0.985,doing better than other systems,especially with less common attack types.Also,tests using Precision-Recall(PR)analysis and False Positive Rate(FPR)measurements prove that the model handles class imbalance well,which is key for real-world security.The suggested system can be scaled up easily and performs calculations fast,making it a possible key part of real-time detection in Security Information and Event Management(SIEM)systems. 展开更多
关键词 Intrusion detection system(IDS) deep learning CNN-RNN hybrid anomaly detection UNSW-NB15 network security real-time detection IntrusionNet temporal modeling cybersecurity
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Quantum-Enhanced Intrusion Detection Using Quantum Circuit Born Machines for Zero-Day Attack Detection 认领 引用
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作者 Wajdan Al Malwi Fatima Asiri Muhammad Shahbaz Khan 《Computers, Materials & Continua》 SCIE EI 2026年第7期898-923,共26页
Modern intrusion detection systems(IDS)struggle to recognise zero-day cyberattacks,as classical discriminative models rely on historical attack labels and fail to characterise deviations from normal network behaviour.... Modern intrusion detection systems(IDS)struggle to recognise zero-day cyberattacks,as classical discriminative models rely on historical attack labels and fail to characterise deviations from normal network behaviour.This work presents a hybrid quantum-classical intrusion detection framework in which a Quantum Circuit Born Machine(QCBM)models benign traffic as a probabilistic quantum state.The trained QCBM assigns each network flow a Quantum Anomaly Score(QAS),defined as the negative log-likelihood under the learned benign distribution,which is subsequently fused with classical flow statistics in a Light Gradient Boosted Machine(LightGBM)classifier.The proposed system employs a 16-qubit,three-layer QCBM(approximately 192 quantum gates)trained using up to 106 measurement shots on the CICIDS2017 dataset.Experimental results show that integrating the QAS does not degrade supervised detection performance on known attacks(Accuracy≈0.996,Receiver Operating Characteristic-Area Under Curve(ROC-AUC)≈0.9995),while providing an additional anomaly-sensitive signal under strict zero-day conditions.When entire attack families are withheld during training,the QAS assigns systematically higher anomaly scores to unseen attacks than to benign traffic and achieves unsupervised zero-day ROC-AUC values of approximately 0.78 across multiple attack types.These findings demonstrate that shallow,resource-efficient quantum generative models can act as interpretable probabilistic priors for benign behaviour,complementing classical IDS pipelines and enabling principled anomaly awareness under realistic Noisy Intermediate-Scale Quantum(NISQ)constraints. 展开更多
关键词 Intrusion detection quantum security threat defence quantum circuit born machine QCBM LightGBM zero-day attack detection quantum anomaly detection Quantum Machine Learning(QML) network security
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Highly resistive lead-free perovskite ferroelectric enables self-driven X-ray detection with ultralow dose 认领 引用
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作者 Huawei Yang Jianbo Wu +11 位作者 Zeng-Kui Zhu Hang Li Panpan Yu Ying Zeng Qingran Ding Guirong Chen Lijun Xu Zhangtong Han Haiqing Zhong Yueying Wang Wenhui Wu Junhua Luo 《Chinese Chemical Letters》 SCIE CAS CSCD 2026年第3期746-751,共6页
Lead-free perovskite has become a shining pearl in the field of direct X-ray detection due to its nontoxicity and excellent optoelectronic properties.However,the high limit of detection(LoD)of X-ray detectors due to h... Lead-free perovskite has become a shining pearl in the field of direct X-ray detection due to its nontoxicity and excellent optoelectronic properties.However,the high limit of detection(LoD)of X-ray detectors due to high current noise caused by high operating voltages is a major challenge to overcome.Here,we utilized a zero-dimensional lead-free perovskite ferroelectric material(NMP)3Sb2Br9(1,NMP=Nmethylpyrrolidine)to achieve ultra-low Lo D self-driven X-ray detection.The self-driven detection mode without external bias has been proven to be an effective means of reducing Lo D due to its low current noise characteristics.Additionally,the zero-dimensional distinctive isolated framework results in a high resistivity of 1.39×1011Ωcm,which effectively reduces the current noise and suppresses ion migration.By further combining the ferroelectric-induced bulk photovoltaic effect,the 1-based detector achieves an ultra-low Lo D X-ray detection of 84.1 n Gyair/s under the self-driven mode,which represents a quite advanced level in the lead-free perovskite X-ray detection region.Our work successfully achieved ultra-low Lo D self-driven X-ray detection by combining ferroelectricity with high resistance,providing a promising avenue for the development of low Lo D X-ray detectors. 展开更多
关键词 Zero-dimensional perovskite Lead-free Ferroelectric Self-driven X-ray detection Low limit of detection
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High-precision acoustic detection and recognition based on biomimetic fiber-optic microphones 认领 引用
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作者 XINYUE HE ZEPING WANG +11 位作者 ZHONG DU TAIYUAN ZHANG WENCHAO HE YINGZHAN YAN TINGJUN LU CHUNXIANG LIU LE ZONG YONGJUN HUANG SIQIN GE TENG TAN YU WU BAICHENG YAO 《Photonics Research》 SCIE EI CAS CSCD 2026年第5期1908-1918,共11页
Acoustic sensing is crucial in nature,supporting functions from biological listening to environmental awareness.In photonics,fiber-optic microphones(FOMs)have become essential tools due to their unique high sensitivit... Acoustic sensing is crucial in nature,supporting functions from biological listening to environmental awareness.In photonics,fiber-optic microphones(FOMs)have become essential tools due to their unique high sensitivity,small size,and resistance to electromagnetic interference,useful in fields such as medical diagnostics,resource exploration,and industrial monitoring.Here,taking advantage of the biomimetic concept,we introduce a fiber-optic acoustic sensing method that offers high-fidelity sound detection and precise voiceprint recognition. 展开更多
关键词 acoustic sensing biomimetic fiber optic microphones medical diagnosticsresource explorationand high precision acoustic detection environmental awarenessin biomimetic conceptwe biological listening sound detection
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CoPt graphitic nanozyme enabled naked-eye identification and colorimetric/fluorescent dual-mode detection of phenylenediamine isomers 认领 引用
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作者 Luyao Guan Zhaoxin Wang +2 位作者 Shengkai Li Phouphien Keoingthong Zhuo Chen 《Chinese Chemical Letters》 SCIE CAS CSCD 2026年第2期407-414,共8页
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 H2O2.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/H2O2-,a 3,3′,5,5′-tetramethylbenzidine(TMB)/CoPt@G/H2O2-,and MPD/CoPt@G/H2O2-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
关键词 Copt graphitic nanozyme Phenylenediamine isomers Naked-eye identification Colorimetric detection Fluorescent detection
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Pulse-shaped femtosecond-laser-modified Si:S photodetector for ultrawide-spectrum focal plane arrays and weak-light detection 认领 引用
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作者 Jiaxin Cao Ziyang Zheng +3 位作者 Xu Zhou Guanting Song Qiang Wu Jingjun Xu 《International Journal of Extreme Manufacturing》 SCIE EI CAS CSCD 2026年第3期840-853,共14页
High-performance Si photodetectors featuring an ultrawide spectral range can be realized through femtosecond(fs)-laser modification,enabling their broad application in focal plane arrays(FPAs)for artificial intelligen... High-performance Si photodetectors featuring an ultrawide spectral range can be realized through femtosecond(fs)-laser modification,enabling their broad application in focal plane arrays(FPAs)for artificial intelligence and complex operational scenarios.However,laser-induced surface inhomogeneity and elevated dark currents reduce the signal-to-noise ratio of FPAs in image detector arrays.To address these challenges,a pulse-shaping technique is employed here to precisely control light-matter interactions during laser modification.This approach not only improves the uniformity of surface microstructures but also significantly suppresses dark currents.The optimized device exhibits high responsivity across the visible to near-infrared spectrum,with a peak responsivity of 164.17 A·W-1 at−2.5 V.Notably,the photodetector demonstrates exceptional weak-light detection capacity at room temperature owing to its record-high specific detectivity of 1.71×1014 Jones,surpassing that of all previously reported Si-based photodetectors.These results highlight substantial advancements in the application of fs-laser-modified Si photodetectors,underscoring their potential in fields such as autonomous driving,weak-light detection,and medical monitoring. 展开更多
关键词 femtosecond laser modification pulse shaping visible-near-infrared Si photodetector weak-light detection infrared detection focal plane arrays
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A Prosody-Guided Multi-Stream Framework for Universal Detection of AI-Synthesized Speech across Codec and Vocoder Domains 认领 引用
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作者 Akmalbek Abdusalomov Mukhriddin Mukhiddinov +6 位作者 Fakhriddin Abdirazakov Alpamis Kutlimuratov Nodira Alimova Ilyos Kalandarov Ayhan Istanbullu Rashid Nasimov Young-Im Cho 《Computers, Materials & Continua》 SCIE EI 2026年第7期2247-2267,共21页
Recent advancements in AI-synthesized speech have resulted in highly realistic deepfake audio,posing severe threats to authentication systems and digital media trust.Existing detection models struggle to generalize ac... Recent advancements in AI-synthesized speech have resulted in highly realistic deepfake audio,posing severe threats to authentication systems and digital media trust.Existing detection models struggle to generalize across diverse synthesis methods,especially those involving neural codec-based Audio Language Models(ALMs).In this work,we propose UniTector++,a novel prosody-aware,multi-stream detection architecture that generalizes across vocoder-and codec-based synthesis.UniTector++incorporates three complementary streams—Whisper-based semantic embeddings,high-level prosodic features,and codec artifact representations—fused through a Multi-Domain Adaptive Graph Attention Fusion(MAGAF)module.Furthermore,an Emotion-Consistency Verification Module(ECVM)reinforces alignment between speech style and prosodic content,and a Universal Adversarial Robustness(UAR)head improves resistance against adversarial attacks.Evaluated on three benchmark datasets—ASVspoof2021,PolyFake,and Codecfake—UniTector++achieves state-of-the-art performance with average Equal Error Rate(EER)of 0.57%under unseen synthesis scenarios,outperforming competitive baselines by a relative margin of 28%.Our results demonstrate the model’s superior generalization,interpretability,and robustness,offering a significant advancement in universal deepfake speech detection. 展开更多
关键词 Deepfake speech detection prosody analysis neural codec artifacts whisper model multi-stream fusion emotion-consistency verification AI-synthesized speech spoofing detection
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Real-Time Mouth State Detection Based on a BiGRU-CLPSO Hybrid Model with Facial Landmark Detection for Healthcare Monitoring Applications 认领 引用
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作者 Mong-Fong Horng Thanh-Lam Nguyen +4 位作者 Thanh-Tuan Nguyen Chin-Shiuh Shieh Lan-Yuen Guo Chen-Fu Hung Chun-Chih Lo 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第1期1266-1295,共30页
The global population is rapidly expanding,driving an increasing demand for intelligent healthcare systems.Artificial intelligence(AI)applications in remote patient monitoring and diagnosis have achieved remarkable pr... The global population is rapidly expanding,driving an increasing demand for intelligent healthcare systems.Artificial intelligence(AI)applications in remote patient monitoring and diagnosis have achieved remarkable progress and are emerging as a major development trend.Among these applications,mouth motion tracking and mouth-state detection represent an important direction,providing valuable support for diagnosing neuromuscular disorders such as dysphagia,Bell’s palsy,and Parkinson’s disease.In this study,we focus on developing a real-time system capable of monitoring and detecting mouth state that can be efficiently deployed on edge devices.The proposed system integrates the Facial Landmark Detection technique with an optimized model combining a Bidirectional Gated Recurrent Unit(BiGRU)and Comprehensive Learning Particle Swarm Optimization(CLPSO).We conducted a comprehensive comparison and evaluation of the proposed model against several traditional models using multiple performance metrics,including accuracy,precision,recall,F1-score,cosine similarity,ROC–AUC,and the precision–recall curve.The proposed method achieved an impressive accuracy of 96.57%with an excellent precision of 98.25%on our self-collected dataset,outperforming traditional models and related works in the same field.These findings highlight the potential of the proposed approach for implementation in real-time patient monitoring systems,contributing to improved diagnostic accuracy and supporting healthcare professionals in patient treatment and care. 展开更多
关键词 Remote patient monitoring mouth state detection dysphagia facial landmark detection bidirectional gated recurrent unit comprehensive learning particle swarm optimization
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Negative-One-Day Malware Detection with Generative AI:A Stable Diffusion-Based Proactive Defense Framework 认领 引用
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作者 Sohail Khan Toqeer Ali Syed +3 位作者 Mohammad Nauman Salman Jan It Ee Lee Qamar Wali 《Computers, Materials & Continua》 SCIE EI 2026年第7期1505-1522,共18页
The detection of zero-day malware represents one of the most significant challenges in contemporary cybersecurity.In this paper,we introduce a novel concept called“Negative-One-Day Malware Detection”,which aims to i... The detection of zero-day malware represents one of the most significant challenges in contemporary cybersecurity.In this paper,we introduce a novel concept called“Negative-One-Day Malware Detection”,which aims to identify potentially malicious software before it is actually created by threat actors.Our approach leverages recent advancements in generative AI,specifically diffusion-based generative models,to generate and analyze potential future malware variants.By doing so,we can train detection systems to recognize these variants before they emerge in the wild,thereby closing the critical protection gap that currently exists between malware creation and detection.We demonstrate the effectiveness of our approach through extensive experimentation,showing that our framework can generate executable malware samples that combine characteristics from different families while exhibiting novel behaviors.These synthetically generated samples significantly improve the detection capabilities of security systems when incorporated into training data,providing a proactive rather than reactive approach to cybersecurity. 展开更多
关键词 Adversarial machine learning Generative AI stable diffusion models zero-day malware detection negative-one-day malware detection proactive cyber defense
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PVIIAD:industrial visual anomaly detection method based on partially visible image inpainting 认领 引用
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作者 Qiliang Wu Dongce Fei +2 位作者 Minghui Yao Yan Niu Cong Wang 《Optoelectronics Letters》 EI 2026年第1期34-40,共7页
Industrial anomaly detection is dedicated to identifying and locating regions that deviate from the standard appearance.The prevailing approach achieves unsupervised anomaly detection through the reconstruction of ima... Industrial anomaly detection is dedicated to identifying and locating regions that deviate from the standard appearance.The prevailing approach achieves unsupervised anomaly detection through the reconstruction of images using autoencoders.Due to the simplistic structure of some abnormal regions,the autoencoder can effectively reconstruct these areas,consequently diminishing the model’s anomaly detection capabilities.To address this issue,this paper transforms the reconstruction task into the inpainting-filling-reconstruction task to increase the reconstruction error between abnormal samples and normal samples.The masked regions inpainted by the filling network are used to fill in the input image,thereby achieving an effect similar to masking.Unlike typical masking processes,this approach retains partial authentic information in the input image,rendering it partially visible.This is beneficial for the reconstruction network to repair the masked areas.Due to the consistent structure between the masked region inpainted by the filling network and the normal region,the filled abnormal regions display a complex structure that has not been learned,making it difficult for the reconstruction network to reconstruct the abnormal regions.Experimental results indicate that our method performs better than other methods on both the MVTec AD dataset and the MVTec LOCO AD dataset. 展开更多
关键词 reconstruction error partially visible image inpainting masking autoencoder reconstruction images identifying locating regions deviate standard appearancethe industrial visual anomaly detection anomaly detection
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Advancing Android Ransomware Detection with Hybrid AutoML and Ensemble Learning Approaches 认领 引用
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作者 Kirubavathi Ganapathiyappan Chahana Ravikumar +3 位作者 Raghul Alagunachimuthu Ranganayaki Ayman Altameem Ateeq Ur Rehman Ahmad Almogren 《Computers, Materials & Continua》 SCIE EI 2026年第4期737-766,共30页
Android smartphones have become an integral part of our daily lives,becoming targets for ransomware attacks.Such attacks encrypt user information and ask for payment to recover it.Conventional detection mechanisms,suc... Android smartphones have become an integral part of our daily lives,becoming targets for ransomware attacks.Such attacks encrypt user information and ask for payment to recover it.Conventional detection mechanisms,such as signature-based and heuristic techniques,often fail to detect new and polymorphic ransomware samples.To address this challenge,we employed various ensemble classifiers,such as Random Forest,Gradient Boosting,Bagging,and AutoML models.We aimed to showcase how AutoML can automate processes such as model selection,feature engineering,and hyperparameter optimization,to minimize manual effort while ensuring or enhancing performance compared to traditional approaches.We used this framework to test it with a publicly available dataset from the Kaggle repository,which contains features for Android ransomware network traffic.The dataset comprises 392,024 flow records,divided into eleven groups.There are ten classes for various ransomware types,including SVpeng,PornDroid,Koler,WannaLocker,and Lockerpin.There is also a class for regular traffic.We applied a three-step procedure to select themost relevant features:filter,wrapper,and embeddedmethods.The Bagging classifier was highly accurate,correctly getting 99.84%of the time.The FLAML AutoML framework was evenmore accurate,correctly getting 99.85%of the time.This is indicative of howwellAutoML performs in improving things with minimal human assistance.Our findings indicate that AutoML is an efficient,scalable,and flexible method to discover Android ransomware,and it will facilitate the development of next-generation intrusion detection systems. 展开更多
关键词 Automated machine learning(AutoML) ensemble learning intrusion detection system(IDS) ransomware traffic analysis android ransomware detection
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A colorimetric biosensor for Escherichia coli detection in food based on aptamer-enhanced oxidase-mimicking activity of octahedral Ag2O nanoparticles 认领 引用
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作者 Xiaohuan Huang Ling Zhang +6 位作者 Tingshu Lu Jie Li Mian Wang Wei Dai Chengyin Zhou Jia Xu Yuangen Wu 《Food Innovation and Advances》 2026年第2期185-194,共10页
Escherichia coli(E.coli)poses a grave threat to food safety,underscoring the need for expeditious and precise detection methodologies.Conventional colorimetric approaches are simple,but they frequently lack accuracy a... Escherichia coli(E.coli)poses a grave threat to food safety,underscoring the need for expeditious and precise detection methodologies.Conventional colorimetric approaches are simple,but they frequently lack accuracy and stability.This study constructed an aptamer-based colorimetric biosensor,leveraging the enzyme-mimicking activity of octahedral Ag2O nanoparticles(NPs),mediated by oligonucleotides,aiming for highly sensitive foodborne E.coli detection.The P12-55 aptamer can be adsorbed onto the octahedral Ag2O NPs surface,thereby significantly enhancing their oxidase-mimicking activity.The aptamer enhances activity by promoting·O2−generation,and accelerating electron transfer to the 3,3',5,5'-tetramethylbenzidine(TMB)substrate.In the presence of E.coli in the sensing system,the aptamer exhibits a preferential binding affinity for the bacteria,thereby restoring the oxidase-mimicking activity and enabling the transduction of the detection signal.The biosensor exhibited a range of linear detection of 3×102-3×108CFU·mL−1,and the limit of detection(LOD)was as low as 4 CFU·mL−1.Recovery rates of 99.1%to 104%were achieved in milk and tap water samples,demonstrating outstanding practicality and reliability.This study proposes a novel and efficient method for the rapid detection of harmful bacteria in food,which is of significant importance for food safety assurance. 展开更多
关键词 detection methodologiesconventional Escherichia coli detection colorimetric approaches octahedral Ag O nanoparticles colorimetric biosensorleveraging oxidase mimicking activity colorimetric biosensor food safety
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An Efficient and Dynamic Framework for Multi-Scale Target Detection of Underwater Organisms 认领 引用
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作者 LI Zhuang LI Guixiang +1 位作者 SONG Xiangyang WANG Xinhua 《Journal of Ocean University of China》 SCIE CAS CSCD 2026年第1期150-160,共11页
The continuous decrease in global fishery resources has increased the importance of precise and efficient underwater fish monitoring technology.First,this study proposes an improved underwater target detection framewo... The continuous decrease in global fishery resources has increased the importance of precise and efficient underwater fish monitoring technology.First,this study proposes an improved underwater target detection framework based on YOLOv8,with the aim of enhancing detection accuracy and the ability to recognize multi-scale targets in blurry and complex underwater environments.A streamlined Vision Transformer(ViT)model is used as the feature extraction backbone,which retains global self-attention feature extraction and accelerates training efficiency.In addition,a detection head named Dynamic Head(DyHead)is introduced,which enhances the efficiency of processing various target sizes through multi-scale feature fusion and adaptive attention modules.Furthermore,a dynamic loss function adjustment method called SlideLoss is employed.This method utilizes sliding window technology to adaptively adjust parameters,which optimizes the detection of challenging targets.The experimental results on the RUOD dataset show that the proposed improved model not only significantly enhances the accuracy of target detection but also increases the efficiency of target detection. 展开更多
关键词 underwater target detection complex underwater environment YOLOv8 object detection
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