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DDoS Attack Detection via Multi-Scale Convolutional Neural Network 认领 引用 被引量：7
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作者 Jieren Cheng Yifu Liu +3 位作者 Xiangyan Tang Victor SSheng Mengyang Li Junqi Li 《Computers, Materials & Continua》 SCIE EI 2020年第3期1317-1333,共17页
Distributed Denial-of-Service(DDoS)has caused great damage to the network in the big data environment.Existing methods are characterized by low computational efficiency,high false alarm rate and high false alarm rate.... Distributed Denial-of-Service(DDoS)has caused great damage to the network in the big data environment.Existing methods are characterized by low computational efficiency,high false alarm rate and high false alarm rate.In this paper,we propose a DDoS attack detection method based on network flow grayscale matrix feature via multi-scale convolutional neural network(CNN).According to the different characteristics of the attack flow and the normal flow in the IP protocol,the seven-tuple is defined to describe the network flow characteristics and converted into a grayscale feature by binary.Based on the network flow grayscale matrix feature(GMF),the convolution kernel of different spatial scales is used to improve the accuracy of feature segmentation,global features and local features of the network flow are extracted.A DDoS attack classifier based on multi-scale convolution neural network is constructed.Experiments show that compared with correlation methods,this method can improve the robustness of the classifier,reduce the false alarm rate and the missing alarm rate. 展开更多
关键词 DDoS attack detection convolutional neural network network flow feature extraction
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Lightweight Multi-scale Convolutional Neural Network for Rice Leaf Disease Recognition 认领 引用 被引量：3
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作者 Chang Zhang Ruiwen Ni +2 位作者 Ye Mu Yu Sun Thobela Louis Tyasi 《Computers, Materials & Continua》 SCIE EI 2023年第1期983-994,共12页
In the field of agricultural information,the identification and prediction of rice leaf disease have always been the focus of research,and deep learning(DL)technology is currently a hot research topic in the field of ... In the field of agricultural information,the identification and prediction of rice leaf disease have always been the focus of research,and deep learning(DL)technology is currently a hot research topic in the field of pattern recognition.The research and development of high-efficiency,highquality and low-cost automatic identification methods for rice diseases that can replace humans is an important means of dealing with the current situation from a technical perspective.This paper mainly focuses on the problem of huge parameters of the Convolutional Neural Network(CNN)model and proposes a recognitionmodel that combines amulti-scale convolution module with a neural network model based on Visual Geometry Group(VGG).The accuracy and loss of the training set and the test set are used to evaluate the performance of the model.The test accuracy of this model is 97.1%that has increased 5.87%over VGG.Furthermore,the memory requirement is 26.1M,only 1.6%of the VGG.Experiment results show that this model performs better in terms of accuracy,recognition speed and memory size. 展开更多
关键词 Rice leaf diseases deep learning lightweight convolution neural networks VGG
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Multi-scale simplified residual convolutional neural network model for predicting compositions of binary magnesium alloys 认领 引用
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作者 Xu Qin Qinghang Wang +6 位作者 Xinqian Zhao Shouxin Xia Li Wang Jiabao Long Yuhui Zhang Yanfu Chai Daolun Chen 《Journal of Magnesium and Alloys》 SCIE EI CAS CSCD 2026年第1期117-123,共7页
This study proposes a multi-scale simplified residual convolutional neural network(MS-SRCNN)for the precise prediction of Mg-Nd binary alloy compositions from scanning electron microscope(SEM)images.A multi-scale data... This study proposes a multi-scale simplified residual convolutional neural network(MS-SRCNN)for the precise prediction of Mg-Nd binary alloy compositions from scanning electron microscope(SEM)images.A multi-scale data structure is established by spatially aligning and stacking SEM images at different magnifications.The MS-SRCNN significantly reduces computational runtime by over 90%compared to traditional architectures like ResNet50,VGG16,and VGG19,without compromising prediction accuracy.The model demonstrates more excellent predictive performance,achieving a>5%increase in R2 compared to single-scale models.Furthermore,the MS-SRCNN exhibits robust composition prediction capability across other Mg-based binary alloys,including Mg-La,Mg-Sn,Mg-Ce,Mg-Sm,Mg-Ag,and Mg-Y,thereby emphasizing its generalization and extrapolation potential.This research establishes a non-destructive,microstructure-informed composition analysis framework,reduces characterization time compared to traditional experiment methods and provides insights into the composition-microstructure relationship in diverse material systems. 展开更多
关键词 Magnesium alloys Composition prediction Scanning electron microscope images Multi-scale simplified residual convolutional neural network
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Ship Magnetic Field Modeling and Extrapolation Based on a Convolutional Neural Network 认领 引用 被引量：1
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作者 Ao Zhou Yadong Zhang +3 位作者 Wentie Yang Zuoshuai Wang Jianxun Wang Zhiwei Chen 《哈尔滨工程大学学报（英文版）》 CSCD 2026年第2期536-549,共14页
Accurate modeling of ship magnetic fields is important for predicting their spatial distribution to improve the magnetic stealth effect of ships.This study proposes an extrapolation model for ship magnetic fields base... Accurate modeling of ship magnetic fields is important for predicting their spatial distribution to improve the magnetic stealth effect of ships.This study proposes an extrapolation model for ship magnetic fields based on genetic algorithms and convolutional neural networks(CNNs).The magnetic probe position matrix of the traditional equivalent source is utilized as input,and the three-directional components of the magnetic field measured by the probes are employed as output.The extrapolation model for ship magnetic fields is obtained through iterative training and fitting with CNNs.Variables such as the number of magnetic dipoles,the distance between magnetic dipoles,the size and quantity of convolutional kernels,batch size,learning rate,and L2 regularization coefficient are optimized to boost the accuracy of the extrapolation model for magnetic fields.The fitting accuracy of the extrapolation model for ship magnetic fields is used as the optimization objective.Based on a finite element simulation model of ship magnetic fields,the accuracy and robustness of the CNN algorithm under different magnetic field conditions are validated using the known standard depth plane,the unknown depth at 1.125 times the standard depth plane,and the unknown depth at 1.25 times the standard depth plane.Results show that,after optimization,the fitting error for the magnetic field extrapolation model based on CNN is 1.50%for the standard depth plane,1.63%for the unknown depth at 1.125 times the standard depth plane,and 2.36%for the unknown depth at 1.25 times the standard depth plane.The error remains below 5%under varying magnetic field conditions.When a random measurement error of 0%-5%is introduced for the magnetic probes,the prediction error at 1.25 times the standard depth plane is 2.30%;with a random error of 0%-10%,the prediction error is 4.95%.This approach significantly improves the accuracy and robustness of magnetic field extrapolation,which makes it an effective and feasible method for ship magnetic field modeling. 展开更多
关键词 Shipboard magnetic field Convolutional neural network Genetic algorithm Equivalent source method Magnetic field extrapolation
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Learning Laws for Deep Convolutional Neural Networks With Guaranteed Convergence 认领 引用 被引量：1
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作者 Sitan Li Chien Chern Cheah 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第1期170-185,共16页
Convolutional neural networks(CNNs)have shown remarkable success across numerous tasks such as image classification,yet the theoretical understanding of their convergence remains underdeveloped compared to their empir... Convolutional neural networks(CNNs)have shown remarkable success across numerous tasks such as image classification,yet the theoretical understanding of their convergence remains underdeveloped compared to their empirical achievements.In this paper,the first filter learning framework with convergence-guaranteed learning laws for end-to-end learning of deep CNNs is proposed.Novel update laws with convergence analysis are formulated based on the mathematical representation of each layer in convolutional neural networks.The proposed learning laws enable concurrent updates of weights across all layers of the deep convolutional neural network and the analysis shows that the training errors converge to certain bounds which are dependent on the approximation errors.Case studies are conducted on benchmark datasets and the results show that the proposed concurrent filter learning framework guarantees the convergence and offers more consistent and reliable results during training with a trade-off in performance compared to stochastic gradient descent methods.This framework represents a significant step towards enhancing the reliability and effectiveness of deep convolutional neural network by developing a theoretical analysis which allows practical implementation of the learning laws with automatic tuning of the learning rate to guarantee the convergence during training. 展开更多
关键词 Convergence convolution neural networks(CNNs) end-to-end learning online learning
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Multi-Source Traffic Information Completion and Perception Method via Graph Convolutional Neural Networks in Intelligent Connected Transportation System 认领 引用
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作者 Pangwei Wang Jie Wang +2 位作者 Zipeng Wang Hangrui Dong Li Wang 《Computers, Materials & Continua》 SCIE EI 2026年第8期1417-1435,共19页
Traffic holographic perception refers to the real-time,high-fidelity,and multi-dimensional sensing of traffic states through the fusion of heterogeneous sensors,including cameras,radars,and connected vehicle data.The ... Traffic holographic perception refers to the real-time,high-fidelity,and multi-dimensional sensing of traffic states through the fusion of heterogeneous sensors,including cameras,radars,and connected vehicle data.The multi-source perception data obtained thereby can provide a complete digital representation of the road network for the Intelligent Transportation System(ITS).However,sensors are vulnerable to environmental interference,which can result in data loss at specific points or along arterial highways for certain periods,potentially undermining system safety and decision-making reliability.To address these challenges,a deep learning method based on Graph Convolutional Networks(GCN)and Gated Recurrent Units(GRU)is proposed,leveraging Artificial Intelligence(AI)and intelligent connected technologies for real-time acquisition of multi-sensor perception data.A feature-level fusion integrates multi-source perception data.GCN captures spatial dependencies from the road network topology,while GRU extracts temporal features from time series,enabling accurate imputation of missing traffic data.The method is evaluated at intelligent connected intersections in the Beijing High-level Autonomous Driving Demonstration Area.Results show that the accuracy of long-term traffic state completion reaches 89.36%,and the Root Mean Square Error(RMSE)is reduced by 17.2%compared to the Long Short-Term Memory(LSTM)baseline.This framework provides a practical solution for deploying traffic holographic perception technology in secure and trustworthy ITS. 展开更多
关键词 Intelligent transportation information security traffic information completion traffic holographic perception AI-driven edge computing graph convolutional neural network
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A Convolutional Neural Network-Based Deep Support Vector Machine for Parkinson’s Disease Detection with Small-Scale and Imbalanced Datasets 认领 引用
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作者 Kwok Tai Chui Varsha Arya +2 位作者 Brij B.Gupta Miguel Torres-Ruiz Razaz Waheeb Attar 《Computers, Materials & Continua》 SCIE EI 2026年第1期1410-1432,共23页
Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using d... Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using deep learning algorithms further enhances performance;nevertheless,it is challenging due to the nature of small-scale and imbalanced PD datasets.This paper proposed a convolutional neural network-based deep support vector machine(CNN-DSVM)to automate the feature extraction process using CNN and extend the conventional SVM to a DSVM for better classification performance in small-scale PD datasets.A customized kernel function reduces the impact of biased classification towards the majority class(healthy candidates in our consideration).An improved generative adversarial network(IGAN)was designed to generate additional training data to enhance the model’s performance.For performance evaluation,the proposed algorithm achieves a sensitivity of 97.6%and a specificity of 97.3%.The performance comparison is evaluated from five perspectives,including comparisons with different data generation algorithms,feature extraction techniques,kernel functions,and existing works.Results reveal the effectiveness of the IGAN algorithm,which improves the sensitivity and specificity by 4.05%–4.72%and 4.96%–5.86%,respectively;and the effectiveness of the CNN-DSVM algorithm,which improves the sensitivity by 1.24%–57.4%and specificity by 1.04%–163%and reduces biased detection towards the majority class.The ablation experiments confirm the effectiveness of individual components.Two future research directions have also been suggested. 展开更多
关键词 Convolutional neural network data generation deep support vector machine feature extraction generative artificial intelligence imbalanced dataset medical diagnosis Parkinson’s disease small-scale dataset
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Fusion for Classification of COVID-19 Patients Using a Discriminative Shape-Texture Convolutional Neural Network with X-Ray Images and Speech Signal Processing 认领 引用
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作者 R.Venkatesh A.Shenbagarajan G.Shenbagalakshmi 《Journal of Bionic Engineering》 SCIE EI CSCD 2026年第3期1899-1917,共19页
Accurate and quick diagnosis of COVID-19 remains a global challenge,as conventional RT-PCR testing suffers from high false-negative rates,delays,and limited availability.Chest X-ray imaging,while more accessible,requi... Accurate and quick diagnosis of COVID-19 remains a global challenge,as conventional RT-PCR testing suffers from high false-negative rates,delays,and limited availability.Chest X-ray imaging,while more accessible,requires expert interpretation and is prone to human error.Although several deep learning methods have been applied,many face issues of overfitting,insufficient feature selection,and reduced reliability.In order to overcome these constraints,we suggest a hybrid framework,COVID-19-DSCNN-XISSP,which integrates a Deep Spatial Convolutional Neural Network(DSCNN)for robust spatial excision of features with the Exponential Incremental Social Ski Driver with Sine Cosine Algorithm(XISSP)for optimal feature selection.This strategy enhances classification accuracy while reducing computational overhead.Extensive experiments on benchmark COVID-19 chest X-ray datasets demonstrate that the proposed model outperforms existing approaches in terms of accuracy,precision,and robustness.The findings highlight the potential of COVID-19-DSCNN-XISSP as an economical,automated,and expandable tool for assisting radiologists in early and reliable detection of COVID-19,thereby supporting improved clinical decision-making and pandemic control. 展开更多
关键词 Bayesian boundary trend filtering COVID-19 Shape-texture discriminative convolutional neural network Lung area specific COVID-19 Xray dataset Multi‐hypothesis fuzzy‐matching radon transform Snow ablation optimizer
Autocorrelation-based convolutional neural network for reconstruction of noisy attosecond streaking traces 认领 引用
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作者 Xiaoxian Zhu Yitan Gao +2 位作者 Yiming Wang Kun Zhao Zhiyi Wei 《Chinese Physics B》 SCIE EI CAS CSCD 2026年第5期432-438,共7页
Attosecond light sources serve as crucial tools for investigating the ultrafast electronic dynamics in matter with remarkable temporal resolution.Traditional methods face difficulties in accurately measuring attosecon... Attosecond light sources serve as crucial tools for investigating the ultrafast electronic dynamics in matter with remarkable temporal resolution.Traditional methods face difficulties in accurately measuring attosecond pulses,and the prevailing approach involves utilizing attosecond streak cameras coupled with inversion algorithms to reconstruct phase information.However,these algorithms often require multiple iterations and extensive computational time.This study investigates the utilization of autocorrelation graphs as inputs for a convolutional neural network(CNN)to invert streaking traces obtained by attosecond streak camera.We explore the noise resistance capability of autocorrelation within the CNN inversion and aim to provide a physical explanation for its effectiveness.The objective of this research is to enhance the accuracy and reliability of CNN inversion for attosecond streaking traces,enabling improved resilience against experimental noises. 展开更多
关键词 attosecond convolutional neural networks autocorrelation attosecond streaking camera phase retrieval noise robustness
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A Multi-Scale Graph Neural Networks Ensemble Approach for Enhanced DDoS Detection 认领 引用
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作者 Noor Mueen Mohammed Ali Hayder Seyed Amin Hosseini Seno +2 位作者 Hamid Noori Davood Zabihzadeh Mehdi Ebady Manaa 《Computers, Materials & Continua》 SCIE EI 2026年第4期1216-1242,共27页
Distributed Denial of Service(DDoS)attacks are one of the severe threats to network infrastructure,sometimes bypassing traditional diagnosis algorithms because of their evolving complexity.PresentMachine Learning(ML)t... Distributed Denial of Service(DDoS)attacks are one of the severe threats to network infrastructure,sometimes bypassing traditional diagnosis algorithms because of their evolving complexity.PresentMachine Learning(ML)techniques for DDoS attack diagnosis normally apply network traffic statistical features such as packet sizes and inter-arrival times.However,such techniques sometimes fail to capture complicated relations among various traffic flows.In this paper,we present a new multi-scale ensemble strategy given the Graph Neural Networks(GNNs)for improving DDoS detection.Our technique divides traffic into macro-and micro-level elements,letting various GNN models to get the two corase-scale anomalies and subtle,stealthy attack models.Through modeling network traffic as graph-structured data,GNNs efficiently learn intricate relations among network entities.The proposed ensemble learning algorithm combines the results of several GNNs to improve generalization,robustness,and scalability.Extensive experiments on three benchmark datasets—UNSW-NB15,CICIDS2017,and CICDDoS2019—show that our approach outperforms traditional machine learning and deep learning models in detecting both high-rate and low-rate(stealthy)DDoS attacks,with significant improvements in accuracy and recall.These findings demonstrate the suggested method’s applicability and robustness for real-world implementation in contexts where several DDoS patterns coexist. 展开更多
关键词 DDoS detection graph neural networks multi-scale learning ensemble learning network security stealth attacks network graphs
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Multi-Label Classification Model Using Graph Convolutional Neural Network for Social Network Nodes 认领 引用
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作者 Junmin Lyu Guangyu Xu +4 位作者 Feng Bao Yu Zhou Yuxin Liu Siyu Lu Wenfeng Zheng 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第2期1235-1256,共22页
Graph neural networks(GNN)have shown strong performance in node classification tasks,yet most existing models rely on uniform or shared weight aggregation,lacking flexibility in modeling the varying strength of relati... Graph neural networks(GNN)have shown strong performance in node classification tasks,yet most existing models rely on uniform or shared weight aggregation,lacking flexibility in modeling the varying strength of relationships among nodes.This paper proposes a novel graph coupling convolutional model that introduces an adaptive weighting mechanism to assign distinct importance to neighboring nodes based on their similarity to the central node.Unlike traditional methods,the proposed coupling strategy enhances the interpretability of node interactions while maintaining competitive classification performance.The model operates in the spatial domain,utilizing adjacency list structures for efficient convolution and addressing the limitations of weight sharing through a coupling-based similarity computation.Extensive experiments are conducted on five graph-structured datasets,including Cora,Citeseer,PubMed,Reddit,and BlogCatalog,as well as a custom topology dataset constructed from the Open University Learning Analytics Dataset(OULAD)educational platform.Results demonstrate that the proposed model achieves good classification accuracy,while significantly reducing training time through direct second-order neighbor fusion and data preprocessing.Moreover,analysis of neighborhood order reveals that considering third-order neighbors offers limited accuracy gains but introduces considerable computational overhead,confirming the efficiency of first-and second-order convolution in practical applications.Overall,the proposed graph coupling model offers a lightweight,interpretable,and effective framework for multi-label node classification in complex networks. 展开更多
关键词 GNN social networks nodes multi-label classification model graphic convolution neural network coupling principle
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Advancing Breast Cancer Molecular Subtyping:A Comparative Study of Convolutional Neural Networks and Vision Transformers on Mammograms 认领 引用
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作者 Chee Chin Lim Hui Wen Tiu +2 位作者 Qi Wei Oung Chiew Chea Lau Xiao Jian Tan 《Computers, Materials & Continua》 SCIE EI 2026年第3期1287-1308,共22页
critical for guiding treatment and improving patient outcomes.Traditional molecular subtyping via immuno-histochemistry(IHC)test is invasive,time-consuming,and may not fully represent tumor heterogeneity.This study pr... critical for guiding treatment and improving patient outcomes.Traditional molecular subtyping via immuno-histochemistry(IHC)test is invasive,time-consuming,and may not fully represent tumor heterogeneity.This study proposes a non-invasive approach using digital mammography images and deep learning algorithm for classifying breast cancer molecular subtypes.Four pretrained models,including two Convolutional Neural Networks(MobileNet_V3_Large and VGG-16)and two Vision Transformers(ViT_B_16 and ViT_Base_Patch16_Clip_224)were fine-tuned to classify images into HER2-enriched,Luminal,Normal-like,and Triple Negative subtypes.Hyperparameter tuning,including learning rate adjustment and layer freezing strategies,was applied to optimize performance.Among the evaluated models,ViT_Base_Patch16_Clip_224 achieved the highest test accuracy(94.44%),with equally high precision,recall,and F1-score of 0.94,demonstrating excellent generalization.MobileNet_V3_Large achieved the same accuracy but showed less training stability.In contrast,VGG-16 recorded the lowest performance,indicating a limitation in its generalizability for this classification task.The study also highlighted the superior performance of the Vision Transformer models over CNNs,particularly due to their ability to capture global contextual features and the benefit of CLIP-based pretraining in ViT_Base_Patch16_Clip_224.To enhance clinical applicability,a graphical user interface(GUI)named“BCMS Dx”was developed for streamlined subtype prediction.Deep learning applied to mammography has proven effective for accurate and non-invasive molecular subtyping.The proposed Vision Transformer-based model and supporting GUI offer a promising direction for augmenting diagnostic workflows,minimizing the need for invasive procedures,and advancing personalized breast cancer management. 展开更多
关键词 Artificial intelligence breast cancer classification convolutional neural network deep learning hyperparameter tuning mammography medical imaging molecular subtypes vision transformer
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A Privacy-Preserving Convolutional Neural Network Inference Framework for AIoT Applications 认领 引用
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作者 Haoran Wang Shuhong Yang +2 位作者 Kuan Shao Tao Xiao Zhenyong Zhang 《Computers, Materials & Continua》 SCIE EI 2026年第1期1354-1371,共18页
With the rapid development of the Artificial Intelligence of Things(AIoT),convolutional neural networks(CNNs)have demonstrated potential and remarkable performance in AIoT applications due to their excellent performan... With the rapid development of the Artificial Intelligence of Things(AIoT),convolutional neural networks(CNNs)have demonstrated potential and remarkable performance in AIoT applications due to their excellent performance in various inference tasks.However,the users have concerns about privacy leakage for the use of AI and the performance and efficiency of computing on resource-constrained IoT edge devices.Therefore,this paper proposes an efficient privacy-preserving CNN framework(i.e.,EPPA)based on the Fully Homomorphic Encryption(FHE)scheme for AIoT application scenarios.In the plaintext domain,we verify schemes with different activation structures to determine the actual activation functions applicable to the corresponding ciphertext domain.Within the encryption domain,we integrate batch normalization(BN)into the convolutional layers to simplify the computation process.For nonlinear activation functions,we use composite polynomials for approximate calculation.Regarding the noise accumulation caused by homomorphic multiplication operations,we realize the refreshment of ciphertext noise through minimal“decryption-encryption”interactions,instead of adopting bootstrapping operations.Additionally,in practical implementation,we convert three-dimensional convolution into two-dimensional convolution to reduce the amount of computation in the encryption domain.Finally,we conduct extensive experiments on four IoT datasets,different CNN architectures,and two platforms with different resource configurations to evaluate the performance of EPPA in detail. 展开更多
关键词 Artificial Intelligence of Things(AIoT) convolutional neural network privacy-preserving fully homomorphic encryption
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Improving Convolutional Neural Network Performance Using Alpha-Based Adaptive Pooling for Image Classification 认领 引用
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作者 Nahdi Saubari Kunfeng Wang +1 位作者 Rachmat Muwardi Andri Pranolo 《Computers, Materials & Continua》 SCIE EI 2026年第6期1502-1525,共24页
This study proposes an Adaptive Pooling method based on an alpha(α)parameter to enhance the effectiveness and stability of convolutional neural networks(CNNs)in image classification tasks.Conventional pooling techniq... This study proposes an Adaptive Pooling method based on an alpha(α)parameter to enhance the effectiveness and stability of convolutional neural networks(CNNs)in image classification tasks.Conventional pooling techniques,such as max pooling and average pooling,often exhibit limited adaptability when applied to datasets with heterogeneous distributions and varying levels of complexity.To address this limitation,the proposed approach introduces anαparameter ranging from 0 to 1 that continuously regulates the contribution of maximum-based and average-based pooling operations in a unified and flexible framework.The proposed method is evaluated using two benchmark datasets,MNIST and CIFAR-10,representing grayscale and color image classification scenarios,respectively.Experiments are conducted across three CNN families with different depths LeNet-5,a deeper custom-built CNN,and ResNet-18 to assess robustness under varying representational capacity.Under the bestαsetting with a 4×4 pooling configuration,Adaptive Pooling exhibits architecture-dependent behavior.On LeNet-5,Adaptive Pooling achieves 87.2%on MNIST and 30.1%on CIFAR-10,compared with 97.8%(max/average pooling)on MNIST and 60.1%(max pooling)/53.9%(average pooling)on CIFAR-10.In contrast,on the deeper custom CNN,Adaptive Pooling becomes competitive,reaching 99.7%on MNIST and 86.1%on CIFAR-10,which is comparable to 99.6%-99.7%on MNIST and 84.5%-86.2%on CIFAR-10 achieved by conventional pooling.On ResNet-18,Adaptive Pooling attains 99.1%on MNIST,while CIFAR-10 performance decreases to 37.2%relative to the default global average pooling baseline(99.7%on MNIST and 89.0%on CIFAR-10),suggesting that performance also depends on where the pooling replacement is applied.Overall,these findings indicate thatα-controlled Adaptive Pooling provides a lightweight and configurable pooling strategy that can improve stability and achieve competitive accuracy in deeper CNNs,although it should be treated as a complementary mechanism rather than a universal replacement across all architectures. 展开更多
关键词 Adaptive pooling alpha parameter convolutional neural network(CNN) image classification MNIST CIFAR-10 model generalization
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Tensor Low-Rank Orthogonal Compression for Convolutional Neural Networks 认领 引用
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作者 Yaping He Xin Luo 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第1期227-229,共3页
Dear Editor,The letter proposes a tensor low-rank orthogonal compression(TLOC)model for a convolutional neural network(CNN),which facilitates its efficient and highly-accurate low-rank representation.Model compression... Dear Editor,The letter proposes a tensor low-rank orthogonal compression(TLOC)model for a convolutional neural network(CNN),which facilitates its efficient and highly-accurate low-rank representation.Model compression is crucial for deploying deep neural network(DNN)models on resource-constrained embedded devices. 展开更多
关键词 model compression convolutional neural network cnn which tensor low rank orthogonal compression deep neural network dnn models embedded devices convolutional neural networks
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Fuzzy C-Means Clustering-Driven Pooling for Robust and Generalizable Convolutional Neural Networks 认领 引用
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作者 Seunggyu Byeon Jung-hun Lee Jong-Deok Kim 《Computers, Materials & Continua》 SCIE EI 2026年第5期579-604,共26页
This paper introduces a fuzzy C-means-based pooling layer for convolutional neural networks that explicitly models local uncertainty and ambiguity.Conventional pooling operations,such as max and average,apply rigid ag... This paper introduces a fuzzy C-means-based pooling layer for convolutional neural networks that explicitly models local uncertainty and ambiguity.Conventional pooling operations,such as max and average,apply rigid aggregation and often discard fine-grained boundary information.In contrast,our method computes soft membershipswithin each receptive field and aggregates cluster-wise responses throughmembership-weighted pooling,thereby preserving informative structure while reducing dimensionality.Being differentiable,the proposed layer operates as standard two-dimensional pooling.We evaluate our approach across various CNN backbones and open datasets,including CIFAR-10/100,STL-10,LFW,and ImageNette,and further probe small training set restrictions on MNIST and Fashion-MNIST.In these settings,the proposed pooling consistently improves accuracy and weighted F1 over conventional baselines,with particularly strong gains when training data are scarce.Even with less than 1%of the training set,ourmethodmaintains reliable performance,indicating improved sample efficiency and robustness to noisy or ambiguous local patterns.Overall,integrating soft memberships into the pooling operator provides a practical and generalizable inductive bias that enhances robustness and generalization in modern CNN pipelines. 展开更多
关键词 Fuzzy logic fuzzy c-means clustering membership-based pooling convolutional neural networks downsampling feature extraction
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Multichannel optical convolutional neural network for color images 认领 引用
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作者 Junpeng Chen Xi Jiang +4 位作者 Jiaming Su Shukai Wu Zheng Huang Haizheng Zhong Hongwei Chen 《Advanced Photonics Nexus》 CSCD 2026年第5期33-45,共13页
Most existing optical convolutional neural networks(OCNNs)are primarily limited to implementingbasic convolution functions,with little focus on leveraging the color dimension.As a result,they can typicallyonly process... Most existing optical convolutional neural networks(OCNNs)are primarily limited to implementingbasic convolution functions,with little focus on leveraging the color dimension.As a result,they can typicallyonly process single-channel or grayscale images,lacking the ability to utilize multichannel information such asRGB.In this work,we propose a multichannel optical convolutional neural network(MOCNN),which is capableof processing both RGB and hyperspectral images using only grayscale sensors by integrating trainable colorfilters into the optical path.Through simulations and experiments,we demonstrate that the performance ofoptical convolution is significantly enhanced by incorporating color information,enabling the system to handlecomplex color-related tasks.Furthermore,we design a loss function tailored to the physical properties ofquantum dots.We demonstrate that this training strategy can be extended to other types of filtering materials.Moreover,the proposed technique can serve as an OCNN-based feature-map acquisition camera withoutadditional imaging components,offering a potential route toward compact,low-cost,and privacy-preservingimage capture with limited task-performance degradation. 展开更多
关键词 optical convolutional neural network optical computing color image multichannel optical computing quantum dot
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MSSTGCN: Multi-Head Self-Attention and Spatial-Temporal Graph Convolutional Network for Multi-Scale Traffic Flow Prediction 认领 引用 被引量：2
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作者 Xinlu Zong Fan Yu +1 位作者 Zhen Chen Xue Xia 《Computers, Materials & Continua》 SCIE EI 2025年第2期3517-3537,共21页
Accurate traffic flow prediction has a profound impact on modern traffic management. Traffic flow has complex spatial-temporal correlations and periodicity, which poses difficulties for precise prediction. To address ... Accurate traffic flow prediction has a profound impact on modern traffic management. Traffic flow has complex spatial-temporal correlations and periodicity, which poses difficulties for precise prediction. To address this problem, a Multi-head Self-attention and Spatial-Temporal Graph Convolutional Network (MSSTGCN) for multiscale traffic flow prediction is proposed. Firstly, to capture the hidden traffic periodicity of traffic flow, traffic flow is divided into three kinds of periods, including hourly, daily, and weekly data. Secondly, a graph attention residual layer is constructed to learn the global spatial features across regions. Local spatial-temporal dependence is captured by using a T-GCN module. Thirdly, a transformer layer is introduced to learn the long-term dependence in time. A position embedding mechanism is introduced to label position information for all traffic sequences. Thus, this multi-head self-attention mechanism can recognize the sequence order and allocate weights for different time nodes. Experimental results on four real-world datasets show that the MSSTGCN performs better than the baseline methods and can be successfully adapted to traffic prediction tasks. 展开更多
关键词 Graph convolutional network traffic flow prediction multi-scale traffic flow spatial-temporal model
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Occluded Gait Emotion Recognition Based on Multi-Scale Suppression Graph Convolutional Network 认领 引用 被引量：1
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作者 Yuxiang Zou Ning He +2 位作者 Jiwu Sun Xunrui Huang Wenhua Wang 《Computers, Materials & Continua》 SCIE EI 2025年第1期1255-1276,共22页
In recent years,gait-based emotion recognition has been widely applied in the field of computer vision.However,existing gait emotion recognition methods typically rely on complete human skeleton data,and their accurac... In recent years,gait-based emotion recognition has been widely applied in the field of computer vision.However,existing gait emotion recognition methods typically rely on complete human skeleton data,and their accuracy significantly declines when the data is occluded.To enhance the accuracy of gait emotion recognition under occlusion,this paper proposes a Multi-scale Suppression Graph ConvolutionalNetwork(MS-GCN).TheMS-GCN consists of three main components:Joint Interpolation Module(JI Moudle),Multi-scale Temporal Convolution Network(MS-TCN),and Suppression Graph Convolutional Network(SGCN).The JI Module completes the spatially occluded skeletal joints using the(K-Nearest Neighbors)KNN interpolation method.The MS-TCN employs convolutional kernels of various sizes to comprehensively capture the emotional information embedded in the gait,compensating for the temporal occlusion of gait information.The SGCN extracts more non-prominent human gait features by suppressing the extraction of key body part features,thereby reducing the negative impact of occlusion on emotion recognition results.The proposed method is evaluated on two comprehensive datasets:Emotion-Gait,containing 4227 real gaits from sources like BML,ICT-Pollick,and ELMD,and 1000 synthetic gaits generated using STEP-Gen technology,and ELMB,consisting of 3924 gaits,with 1835 labeled with emotions such as“Happy,”“Sad,”“Angry,”and“Neutral.”On the standard datasets Emotion-Gait and ELMB,the proposed method achieved accuracies of 0.900 and 0.896,respectively,attaining performance comparable to other state-ofthe-artmethods.Furthermore,on occlusion datasets,the proposedmethod significantly mitigates the performance degradation caused by occlusion compared to other methods,the accuracy is significantly higher than that of other methods. 展开更多
关键词 KNN interpolation multi-scale temporal convolution suppression graph convolutional network gait emotion recognition human skeleton
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Brief application notes for vision transformer (ViT) and convolutional neural network (CNN) in medical imaging 认领 引用
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作者 Wei Kitt Wong Melinda Melinda 《Medical Data Mining》 CAS 2026年第2期34-42,共9页
In contemporary computer vision,convolutional neural networks(CNNs)and vision transformers(ViTs)represent the two primary architectural paradigms for image recognition.While both approaches have been widely adopted in... In contemporary computer vision,convolutional neural networks(CNNs)and vision transformers(ViTs)represent the two primary architectural paradigms for image recognition.While both approaches have been widely adopted in medical imaging applications,they operate based on fundamentally different computational principles.This report attempts to provide brief application notes on ViTs and CNNs,particularly focusing on scenarios that guide the selection of one architecture over the other in practical medical implementations.Generally,CNNs rely on convolutional kernels,localized receptive fields,and weight sharing,enabling efficient hierarchical feature extraction.These properties contribute to strong performance in detecting spatially constrained patterns such as textures,edges,and anatomical boundaries,while maintaining relatively low computational requirements.ViTs,on the other hand,decompose images into smaller segments referred to as tokens and employ self-attention mechanisms to model relationships across the entire image.This global modeling capability allows ViTs to capture long-range dependencies that may be difficult for convolution-based architectures to learn.However,ViTs typically achieve optimal performance when trained on extremely large datasets or when supported by extensive pretraining,as their reduced inductive bias requires greater data exposure to learn robust representations.This report briefly examines the architectural structure,underlying mathematical foundations,and relative performance characteristics of CNNs and ViTs,drawing upon recent findings from contemporary research.Emphasis is placed on understanding how differences in data availability,computational resources,and task requirements influence model effectiveness across medical imaging domains.Most importantly,the report serves as a concise application guide for practitioners seeking informed implementation decisions between these two influential deep learning frameworks. 展开更多
关键词 convolutional neural network vision transformer comparative study medical imaging
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