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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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Spatial-Temporal Graph Fusion with Dual-Scale Convolution for Traffic Flow Prediction 认领 引用
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作者 Dan Wang Mengyi Cui +1 位作者 Zhenhua Yu Yukang Liu 《Computers, Materials & Continua》 SCIE EI 2026年第6期1375-1396,共22页
Traffic flow prediction is of great importance in traffic planning,road resource management,and congestion mitigation.However,existing prediction have significant limitations in modeling multi-scale spatial-temporal f... Traffic flow prediction is of great importance in traffic planning,road resource management,and congestion mitigation.However,existing prediction have significant limitations in modeling multi-scale spatial-temporal features,particularly in capturing temporal periodicity and spatial dependency in dynamically evolving traffic networks.This paper proposes a novel framework of traffic flow prediction,referred to as Adaptive Graph Fusion Dual-scale Convolutional Network(AGFDCN),which integrates spatial-temporal dynamic graphs with dual-scale convolutional networks.Specifically,we introduce a Dual-Scale Temporal Network,which combines long-and short-term dilated causal convolutions with a temporal decay-aware attention mechanism to efficiently capture traffic patterns across multiple temporal scales.Furthermore,we design a Dynamic Adaptive Graph Module,which models complex spatial dependencies in traffic networks through an adaptive graph fusion mechanism and a dual-path attention-gated module.Finally,the temporal and spatial representations are integrated by employing a gated fusion mechanism,enhancing the overall prediction performance.Experimental results obtained based on three highway datasets(i.e.,PEMS04,PEMS07 and PEMS08)verify that the proposed model outperforms several state-of-the-art baselines in various evaluation metrics.Compared to the spatial-temporal graph model AGCRN with best performance in the baseline models,the proposed model exhibits significant improvements across all datasets:it achieves reduces of MAE by 42.07%and RMSE by 35.43%on PEMS04;MAE by 28.35%and RMSE by 29.28%on PEMS07;and MAE by 30.52%and RMSE by 30.73%on PEMS08,respectively,validating its effectiveness in modeling complex spatial-temporal traffic data and its robustness in handling sudden traffic changes. 展开更多
关键词 Dual-scale convolution dual-path attention-gated module adaptive graph fusion spatial-temporal dynamic graph traffic flow prediction
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AFSTGCN:Prediction for multivariate time series using an adaptive fused spatial-temporal graph convolutional network 认领 引用 被引量：2
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作者 Yuteng Xiao Kaijian Xia +5 位作者 Hongsheng Yin Yu-Dong Zhang Zhenjiang Qian Zhaoyang Liu Yuehan Liang Xiaodan Li 《Digital Communications and Networks》 SCIE CSCD 2024年第2期292-303,共12页
The prediction for Multivariate Time Series(MTS)explores the interrelationships among variables at historical moments,extracts their relevant characteristics,and is widely used in finance,weather,complex industries an... The prediction for Multivariate Time Series(MTS)explores the interrelationships among variables at historical moments,extracts their relevant characteristics,and is widely used in finance,weather,complex industries and other fields.Furthermore,it is important to construct a digital twin system.However,existing methods do not take full advantage of the potential properties of variables,which results in poor predicted accuracy.In this paper,we propose the Adaptive Fused Spatial-Temporal Graph Convolutional Network(AFSTGCN).First,to address the problem of the unknown spatial-temporal structure,we construct the Adaptive Fused Spatial-Temporal Graph(AFSTG)layer.Specifically,we fuse the spatial-temporal graph based on the interrelationship of spatial graphs.Simultaneously,we construct the adaptive adjacency matrix of the spatial-temporal graph using node embedding methods.Subsequently,to overcome the insufficient extraction of disordered correlation features,we construct the Adaptive Fused Spatial-Temporal Graph Convolutional(AFSTGC)module.The module forces the reordering of disordered temporal,spatial and spatial-temporal dependencies into rule-like data.AFSTGCN dynamically and synchronously acquires potential temporal,spatial and spatial-temporal correlations,thereby fully extracting rich hierarchical feature information to enhance the predicted accuracy.Experiments on different types of MTS datasets demonstrate that the model achieves state-of-the-art single-step and multi-step performance compared with eight other deep learning models. 展开更多
关键词 Adaptive adjacency matrix Digital twin Graph convolutional network Multivariate time series prediction Spatial-temporal graph
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Improved Adaptive Random Convolutional Network Coding Algorithm 认领 引用 被引量：2
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作者 Guo Wangmei Cai Ning Wang Xiao 《China Communications》 SCIE CSCD 2012年第11期63-69,共7页
To address the issue of field size in random network coding, we propose an Improved Adaptive Random Convolutional Network Coding (IARCNC) algorithm to considerably reduce the amount of occupied memory. The operation o... To address the issue of field size in random network coding, we propose an Improved Adaptive Random Convolutional Network Coding (IARCNC) algorithm to considerably reduce the amount of occupied memory. The operation of IARCNC is similar to that of Adaptive Random Convolutional Network Coding (ARCNC), with the coefficients of local encoding kernels chosen uniformly at random over a small finite field. The difference is that the length of the local encoding kernels at the nodes used by IARCNC is constrained by the depth; meanwhile, increases until all the related sink nodes can be decoded. This restriction can make the code length distribution more reasonable. Therefore, IARCNC retains the advantages of ARCNC, such as a small decoding delay and partial adaptation to an unknown topology without an early estimation of the field size. In addition, it has its own advantage, that is, a higher reduction in memory use. The simulation and the example show the effectiveness of the proposed algorithm. 展开更多
关键词 convolutional network coding adaptive network coding algorithm random coding
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Domain adaptation method inspired by quantum convolutional neural network 认领 引用
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作者 Chunhui Wu Junhao Pei +2 位作者 Yihua Wu Anqi Zhang Shengmei Zhao 《Chinese Physics B》 SCIE EI CAS CSCD 2025年第7期185-195,共11页
Quantum machine learning is an important application of quantum computing in the era of noisy intermediate-scale quantum devices.Domain adaptation(DA)is an effective method for addressing the distribution discrepancy ... Quantum machine learning is an important application of quantum computing in the era of noisy intermediate-scale quantum devices.Domain adaptation(DA)is an effective method for addressing the distribution discrepancy problem between the training data and the real data when the neural network model is deployed.In this paper,we propose a variational quantum domain adaptation method inspired by the quantum convolutional neural network,named variational quantum domain adaptation(VQDA).The data are first uploaded by a‘quantum coding module',then the feature information is extracted by several‘quantum convolution layers'and‘quantum pooling layers',which is named‘Feature Extractor'.Subsequently,the labels and the domains of the samples are obtained by the‘quantum fully connected layer'.With a gradient reversal module,the trained‘Feature Extractor'can extract the features that cannot be distinguished from the source and target domains.The simulations on the local computer and IBM Quantum Experience(IBM Q)platform by Qiskit show the effectiveness of the proposed method.The results show that VQDA(with 8 quantum bits)has 91.46%average classification accuracy for DA task between MNIST→USPS(USPS→MNIST),achieves 91.16%average classification accuracy for gray-scale and color images(with 10 quantum bits),and has 69.25%average classification accuracy on the DA task for color images(also with 10 quantum bits).VQDA achieves a 9.14%improvement in average classification accuracy compared to its corresponding classical domain adaptation method with the same parameter scale for different DA tasks.Simultaneously,the parameters scale is reduced to 43%by using VQDA when both quantum and classical DA methods have similar classification accuracies. 展开更多
关键词 quantum image processing domain adaptation quantum convolutional neural network IBM quantum experience
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LKAW: A Robust Watermarking Method Based on Large Kernel Convolution and Adaptive Weight Assignment 认领 引用
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作者 Xiaorui Zhang Rui Jiang +3 位作者 Wei Sun Aiguo Song Xindong Wei Ruohan Meng 《Computers, Materials & Continua》 SCIE EI 2023年第4期1-17,共17页
Robust watermarking requires finding invariant features under multiple attacks to ensure correct extraction.Deep learning has extremely powerful in extracting features,and watermarking algorithms based on deep learnin... Robust watermarking requires finding invariant features under multiple attacks to ensure correct extraction.Deep learning has extremely powerful in extracting features,and watermarking algorithms based on deep learning have attracted widespread attention.Most existing methods use 3×3 small kernel convolution to extract image features and embed the watermarking.However,the effective perception fields for small kernel convolution are extremely confined,so the pixels that each watermarking can affect are restricted,thus limiting the performance of the watermarking.To address these problems,we propose a watermarking network based on large kernel convolution and adaptive weight assignment for loss functions.It uses large-kernel depth-wise convolution to extract features for learning large-scale image information and subsequently projects the watermarking into a highdimensional space by 1×1 convolution to achieve adaptability in the channel dimension.Subsequently,the modification of the embedded watermarking on the cover image is extended to more pixels.Because the magnitude and convergence rates of each loss function are different,an adaptive loss weight assignment strategy is proposed to make theweights participate in the network training together and adjust theweight dynamically.Further,a high-frequency wavelet loss is proposed,by which the watermarking is restricted to only the low-frequency wavelet sub-bands,thereby enhancing the robustness of watermarking against image compression.The experimental results show that the peak signal-to-noise ratio(PSNR)of the encoded image reaches 40.12,the structural similarity(SSIM)reaches 0.9721,and the watermarking has good robustness against various types of noise. 展开更多
关键词 Robust watermarking large kernel convolution adaptive loss weights high-frequency wavelet loss deep learning
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APSO-CNN-SE:An Adaptive Convolutional Neural Network Approach for IoT Intrusion Detection 认领 引用
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作者 Yunfei Ban Damin Zhang +1 位作者 Qing He Qianwen Shen 《Computers, Materials & Continua》 SCIE EI 2024年第10期567-601,共35页
The surge in connected devices and massive data aggregation has expanded the scale of the Internet of Things(IoT)networks.The proliferation of unknown attacks and related risks,such as zero-day attacks and Distributed... The surge in connected devices and massive data aggregation has expanded the scale of the Internet of Things(IoT)networks.The proliferation of unknown attacks and related risks,such as zero-day attacks and Distributed Denial of Service(DDoS)attacks triggered by botnets,have resulted in information leakage and property damage.Therefore,developing an efficient and realistic intrusion detection system(IDS)is critical for ensuring IoT network security.In recent years,traditional machine learning techniques have struggled to learn the complex associations between multidimensional features in network traffic,and the excellent performance of deep learning techniques,as an advanced version of machine learning,has led to their widespread application in intrusion detection.In this paper,we propose an Adaptive Particle Swarm Optimization Convolutional Neural Network Squeeze-andExcitation(APSO-CNN-SE)model for implementing IoT network intrusion detection.A 2D CNN backbone is initially constructed to extract spatial features from network traffic.Subsequently,a squeeze-and-excitation channel attention mechanism is introduced and embedded into the CNN to focus on critical feature channels.Lastly,the weights and biases in the CNN-SE are extracted to initialize the population individuals of the APSO.As the number of iterations increases,the population’s position vector is continuously updated,and the cross-entropy loss function value is minimized to produce the ideal network architecture.We evaluated the models experimentally using binary and multiclassification on the UNSW-NB15 and NSL-KDD datasets,comparing and analyzing the evaluation metrics derived from each model.Compared to the base CNN model,the results demonstrate that APSO-CNNSE enhances the binary classification detection accuracy by 1.84%and 3.53%and the multiclassification detection accuracy by 1.56%and 2.73%on the two datasets,respectively.Additionally,the model outperforms the existing models like DT,KNN,LR,SVM,LSTM,etc.,in terms of accuracy and fitting performance.This means that the model can identify potential attacks or anomalies more precisely,improving the overall security and stability of the IoT environment. 展开更多
关键词 Intrusion detection system internet of things convolutional neural network channel attention mechanism adaptive particle swarm optimization
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A Spectral Convolutional Neural Network Model Based on Adaptive Fick’s Law for Hyperspectral Image Classification 认领 引用
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作者 Tsu-Yang Wu Haonan Li +1 位作者 Saru Kumari Chien-Ming Chen 《Computers, Materials & Continua》 SCIE EI 2024年第4期19-46,共28页
Hyperspectral image classification stands as a pivotal task within the field of remote sensing,yet achieving highprecision classification remains a significant challenge.In response to this challenge,a Spectral Convol... Hyperspectral image classification stands as a pivotal task within the field of remote sensing,yet achieving highprecision classification remains a significant challenge.In response to this challenge,a Spectral Convolutional Neural Network model based on Adaptive Fick’s Law Algorithm(AFLA-SCNN)is proposed.The Adaptive Fick’s Law Algorithm(AFLA)constitutes a novel metaheuristic algorithm introduced herein,encompassing three new strategies:Adaptive weight factor,Gaussian mutation,and probability update policy.With adaptive weight factor,the algorithmcan adjust theweights according to the change in the number of iterations to improve the performance of the algorithm.Gaussianmutation helps the algorithm avoid falling into local optimal solutions and improves the searchability of the algorithm.The probability update strategy helps to improve the exploitability and adaptability of the algorithm.Within the AFLA-SCNN model,AFLA is employed to optimize two hyperparameters in the SCNN model,namely,“numEpochs”and“miniBatchSize”,to attain their optimal values.AFLA’s performance is initially validated across 28 functions in 10D,30D,and 50D for CEC2013 and 29 functions in 10D,30D,and 50D for CEC2017.Experimental results indicate AFLA’s marked performance superiority over nine other prominent optimization algorithms.Subsequently,the AFLA-SCNN model was compared with the Spectral Convolutional Neural Network model based on Fick’s Law Algorithm(FLA-SCNN),Spectral Convolutional Neural Network model based on Harris Hawks Optimization(HHO-SCNN),Spectral Convolutional Neural Network model based onDifferential Evolution(DE-SCNN),SpectralConvolutionalNeuralNetwork(SCNN)model,and SupportVector Machines(SVM)model using the Indian Pines dataset and PaviaUniversity dataset.The experimental results show that the AFLA-SCNN model outperforms other models in terms of Accuracy,Precision,Recall,and F1-score on Indian Pines and Pavia University.Among them,the Accuracy of the AFLA-SCNN model on Indian Pines reached 99.875%,and the Accuracy on PaviaUniversity reached 98.022%.In conclusion,our proposed AFLA-SCNN model is deemed to significantly enhance the precision of hyperspectral image classification. 展开更多
关键词 Adaptive Fick’s law algorithm spectral convolutional neural network metaheuristic algorithm intelligent optimization algorithm hyperspectral image classification
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Adaptive Graph Convolutional Recurrent Neural Networks for System-Level Mobile Traffic Forecasting 认领 引用 被引量：1
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作者 Yi Zhang Min Zhang +4 位作者 Yihan Gui Yu Wang Hong Zhu Wenbin Chen Danshi Wang 《China Communications》 SCIE CSCD 2023年第10期200-211,共12页
Accurate traffic pattern prediction in largescale networks is of great importance for intelligent system management and automatic resource allocation.System-level mobile traffic forecasting has significant challenges ... Accurate traffic pattern prediction in largescale networks is of great importance for intelligent system management and automatic resource allocation.System-level mobile traffic forecasting has significant challenges due to the tremendous temporal and spatial dynamics introduced by diverse Internet user behaviors and frequent traffic migration.Spatialtemporal graph modeling is an efficient approach for analyzing the spatial relations and temporal trends of mobile traffic in a large system.Previous research may not reflect the optimal dependency by ignoring inter-base station dependency or pre-determining the explicit geological distance as the interrelationship of base stations.To overcome the limitations of graph structure,this study proposes an adaptive graph convolutional network(AGCN)that captures the latent spatial dependency by developing self-adaptive dependency matrices and acquires temporal dependency using recurrent neural networks.Evaluated on two mobile network datasets,the experimental results demonstrate that this method outperforms other baselines and reduces the mean absolute error by 3.7%and 5.6%compared to time-series based approaches. 展开更多
关键词 adaptive graph convolutional network mobile traffic prediction spatial-temporal dependence
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Importance of Adaptive Photometric Augmentation for Different Convolutional Neural Network 认领 引用
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作者 Saraswathi Sivamani Sun Il Chon +2 位作者 Do Yeon Choi Dong Hoon Lee Ji Hwan Park 《Computers, Materials & Continua》 SCIE EI 2022年第9期4433-4452,共20页
Existing segmentation and augmentation techniques on convolutional neural network(CNN)has produced remarkable progress in object detection.However,the nominal accuracy and performance might be downturned with the phot... Existing segmentation and augmentation techniques on convolutional neural network(CNN)has produced remarkable progress in object detection.However,the nominal accuracy and performance might be downturned with the photometric variation of images that are directly ignored in the training process,along with the context of the individual CNN algorithm.In this paper,we investigate the effect of a photometric variation like brightness and sharpness on different CNN.We observe that random augmentation of images weakens the performance unless the augmentation combines the weak limits of photometric variation.Our approach has been justified by the experimental result obtained from the PASCAL VOC 2007 dataset,with object detection CNN algorithms such as YOLOv3(You Only Look Once),Faster R-CNN(Region-based CNN),and SSD(Single Shot Multibox Detector).Each CNN model shows performance loss for varying sharpness and brightness,ranging between−80%to 80%.It was further shown that compared to random augmentation,the augmented dataset with weak photometric changes delivered high performance,but the photometric augmentation range differs for each model.Concurrently,we discuss some research questions that benefit the direction of the study.The results prove the importance of adaptive augmentation for individual CNN model,subjecting towards the robustness of object detection. 展开更多
关键词 Object detection photometric variation adaptive augmentation convolutional neural network
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Method of Multi-Mode Sensor Data Fusion with an Adaptive Deep Coupling Convolutional Auto-Encoder 认领 引用
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作者 Xiaoxiong Feng Jianhua Liu 《Journal of Sensor Technology》 2023年第4期69-85,共17页
To address the difficulties in fusing multi-mode sensor data for complex industrial machinery, an adaptive deep coupling convolutional auto-encoder (ADCCAE) fusion method was proposed. First, the multi-mode features e... To address the difficulties in fusing multi-mode sensor data for complex industrial machinery, an adaptive deep coupling convolutional auto-encoder (ADCCAE) fusion method was proposed. First, the multi-mode features extracted synchronously by the CCAE were stacked and fed to the multi-channel convolution layers for fusion. Then, the fused data was passed to all connection layers for compression and fed to the Softmax module for classification. Finally, the coupling loss function coefficients and the network parameters were optimized through an adaptive approach using the gray wolf optimization (GWO) algorithm. Experimental comparisons showed that the proposed ADCCAE fusion model was superior to existing models for multi-mode data fusion. 展开更多
关键词 Multi-Mode Data Fusion Coupling Convolutional Auto-Encoder Adaptive Optimization Deep Learning
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Wind Power Prediction Model based on Integrated Osprey and Adaptive T-distribution Dung Beetle Optimization Algorithm 认领 引用 被引量：1
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作者 Yanyan Wu Ying Xu Xudong Huang 《Journal of Bionic Engineering》 SCIE EI CSCD 2025年第5期2678-2699,共22页
Accurate forecasting of wind power is crucial for ensuring the reliable operation of the electrical grid.Due to the impact of various factors,wind power forecasting presents a significant challenge.This paper presents... Accurate forecasting of wind power is crucial for ensuring the reliable operation of the electrical grid.Due to the impact of various factors,wind power forecasting presents a significant challenge.This paper presents the model that integrates Osprey and adaptive T-distribution dung beetle algorithm for optimizing a convolutional neural network.The CNN-BiLSTM-Attention model combines bidirectional long short-term memory neural networks with an attention mechanism,thereby improving the accuracy of wind power generation predictions.The original data is subjected to Variational Mode Decomposition(VMD)for analysis,taking into account the fluctuations in wind power across different periods.The BiLSTM network with short-term memory processes time-series wind power data,yielding an optimal predictive performance.The integration of the osprey algorithm and adaptive T-distribution within the Dung Beetle Optimization Algorithm was utilized to optimize the hyperparameters of the CNN-BiLSTM-Attention model,thereby enhancing its predictive performance.To assess the efficacy of the CNN-BiLSTM-Attention algorithm,enhanced by Ospreys and adaptive T-distributed dung beetle algorithm,we conducted experiments using the CEC2021 benchmark function.The integrated Osprey and adaptive T-distribution Dung Beetle algorithm has excellent global optimization performance when dealing with complex optimization problems.The fusion of Osprey and the adaptive T-distribution Dung beetle algorithm optimized the CNN-BiLSTM-Attention algorithm as well as other optimization algorithms for ablation experiments.The results show that the improved algorithm performs well in predicting wind power.The experimental findings suggest that the model’s predictive efficiency has enhanced by a minimum of 17.74%. 展开更多
关键词 Convolutional neural network Bidirectional long term memory Dung beetle optimization IntegratedOsprey and adaptive T-distribution
A Real-Time Deep Learning Approach for Electrocardiogram-Based Cardiovascular Disease Prediction with Adaptive Drift Detection and Generative Feature Replay 认领 引用
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作者 Soumia Zertal Asma Saighi +2 位作者 Sofia Kouah Souham Meshoul Zakaria Laboudi 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第9期3737-3782,共46页
Cardiovascular diseases(CVDs)continue to present a leading cause ofmortalityworldwide,emphasizing the importance of early and accurate prediction.Electrocardiogram(ECG)signals,central to cardiac monitoring,have increa... Cardiovascular diseases(CVDs)continue to present a leading cause ofmortalityworldwide,emphasizing the importance of early and accurate prediction.Electrocardiogram(ECG)signals,central to cardiac monitoring,have increasingly been integratedwithDeep Learning(DL)for real-time prediction of CVDs.However,DL models are prone to performance degradation due to concept drift and to catastrophic forgetting.To address this issue,we propose a realtime CVDs prediction approach,referred to as ADWIN-GFR that combines Convolutional Neural Network(CNN)layers,for spatial feature extraction,with Gated Recurrent Units(GRU),for temporal modeling,alongside adaptive drift detection and mitigation mechanisms.The proposed approach integratesAdaptiveWindowing(ADWIN)for realtime concept drift detection,a fine-tuning strategy based on Generative Features Replay(GFR)to preserve previously acquired knowledge,and a dynamic replay buffer ensuring variance,diversity,and data distribution coverage.Extensive experiments conducted on the MIT-BIH arrhythmia dataset demonstrate that ADWIN-GFR outperforms standard fine-tuning techniques,achieving an average post-drift accuracy of 95.4%,amacro F1-score of 93.9%,and a remarkably low forgetting score of 0.9%.It also exhibits an average drift detection delay of 12 steps and achieves an adaptation gain of 17.2%.These findings underscore the potential of ADWIN-GFR for deployment in real-world cardiac monitoring systems,including wearable ECG devices and hospital-based patient monitoring platforms. 展开更多
关键词 Real-time cardiovascular disease prediction concept drift detection catastrophic forgetting fine-tuning electrocardiogram convolutional neural networks gated recurrent units adaptive windowing generative feature replay
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Integration of contrast-adaptive colour correction and convolutional neural network for cryptocaryon fish disease detection 认领 引用
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作者 Nor Hazlyna Harun Siti Naquiah Md Pauzi +3 位作者 Mohamad Ghozali Hassan Nooraini Yusoff Beng Chu Kua Neil Gordon 《Aquaculture and Fisheries》 CAS CSCD 2026年第4期684-696,共13页
Integrating Contrast Colour Correction(CACC)and Convolutional Neural Networks(CNN)can help fish breeders in earlier classification and identification of Cryptocaryon fish disease(protozoan white spot disease).Disease ... Integrating Contrast Colour Correction(CACC)and Convolutional Neural Networks(CNN)can help fish breeders in earlier classification and identification of Cryptocaryon fish disease(protozoan white spot disease).Disease identification accuracy is enhanced through such method by adaptive colour changes and CNN feature extracting ability,thereby boosting underwater image clarity.Unlike traditional rule-based systems that relies on expert knowledge despite being error-prone,existing methods focus on visual quality without classifying impact influence.Early disease identification is hampered in terms of efficiency due to machine learning methods reliant on abundant human expertise other than efficient feature extracting.An artificial intelligence(AI)-oriented computer model is introduced for existing research in overcoming limitations and eliminating subjectivity.The model employs a proprietary method of diagnosing fish disease through underwater images examination that yields objective outcome.Several CNN structures such as GoogleNet,ResNet-101,AlexNet,ResNet-50 as well as VGG-16 are tested on its performance.The current study shows integration of CACC with CNN through a set of 15000 images boosting up model performance in Cryptocaryon fish disease detection.The introduced novel method significantly enhances performance with 99.53%accuracy,99.08%precision along with 100.00%recall.This efficient,accurate approach can significantly reduce the workload of experts and fish farmers while promoting sustainable aquaculture and healthier aquatic ecosystems. 展开更多
关键词 Fish disease detecting Contrast adaptive colour correction Convolutional neural networks Underwater images Image enhancement Artificial intelligence
An adaptive physics-informed deep learning method for pore pressure prediction using seismic data 认领 引用 被引量：13
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作者 Xin Zhang Yun-Hu Lu +2 位作者 Yan Jin Mian Chen Bo Zhou 《Petroleum Science》 SCIE EI CAS CSCD 2024年第2期885-902,共18页
Accurate prediction of formation pore pressure is essential to predict fluid flow and manage hydrocarbon production in petroleum engineering.Recent deep learning technique has been receiving more interest due to the g... Accurate prediction of formation pore pressure is essential to predict fluid flow and manage hydrocarbon production in petroleum engineering.Recent deep learning technique has been receiving more interest due to the great potential to deal with pore pressure prediction.However,most of the traditional deep learning models are less efficient to address generalization problems.To fill this technical gap,in this work,we developed a new adaptive physics-informed deep learning model with high generalization capability to predict pore pressure values directly from seismic data.Specifically,the new model,named CGP-NN,consists of a novel parametric features extraction approach(1DCPP),a stacked multilayer gated recurrent model(multilayer GRU),and an adaptive physics-informed loss function.Through machine training,the developed model can automatically select the optimal physical model to constrain the results for each pore pressure prediction.The CGP-NN model has the best generalization when the physicsrelated metricλ=0.5.A hybrid approach combining Eaton and Bowers methods is also proposed to build machine-learnable labels for solving the problem of few labels.To validate the developed model and methodology,a case study on a complex reservoir in Tarim Basin was further performed to demonstrate the high accuracy on the pore pressure prediction of new wells along with the strong generalization ability.The adaptive physics-informed deep learning approach presented here has potential application in the prediction of pore pressures coupled with multiple genesis mechanisms using seismic data. 展开更多
关键词 Pore pressure prediction Seismic data 1D convolution pyramid pooling Adaptive physics-informed loss function High generalization capability
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A Novel Forgery Detection in Image Frames of the Videos Using Enhanced Convolutional Neural Network in Face Images 认领 引用 被引量：2
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作者 S.Velliangiri J.Premalatha 《Computer Modeling in Engineering & Sciences》 SCIE EI 2020年第11期625-645,共21页
Different devices in the recent era generated a vast amount of digital video.Generally,it has been seen in recent years that people are forging the video to use it as proof of evidence in the court of justice.Many kin... Different devices in the recent era generated a vast amount of digital video.Generally,it has been seen in recent years that people are forging the video to use it as proof of evidence in the court of justice.Many kinds of researches on forensic detection have been presented,and it provides less accuracy.This paper proposed a novel forgery detection technique in image frames of the videos using enhanced Convolutional Neural Network(CNN).In the initial stage,the input video is taken as of the dataset and then converts the videos into image frames.Next,perform pre-sampling using the Adaptive Rood Pattern Search(ARPS)algorithm intended for reducing the useless frames.In the next stage,perform preprocessing for enhancing the image frames.Then,face detection is done as of the image utilizing the Viola-Jones algorithm.Finally,the improved Crow Search Algorithm(ICSA)has been used to select the extorted features and inputted to the Enhanced Convolutional Neural Network(ECNN)classifier for detecting the forged image frames.The experimental outcome of the proposed system has achieved 97.21%accuracy compared to other existing methods. 展开更多
关键词 Adaptive Rood Pattern Search(ARPS) Improved Crow Search Algorithm(ICSA) Enhanced Convolutional Neural Network(ECNN) Viola Jones algorithm Speeded Up Robust Feature(SURF)
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Enhancing Human Action Recognition with Adaptive Hybrid Deep Attentive Networks and Archerfish Optimization 认领 引用
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作者 Ahmad Yahiya Ahmad Bani Ahmad Jafar Alzubi +3 位作者 Sophers James Vincent Omollo Nyangaresi Chanthirasekaran Kutralakani Anguraju Krishnan 《Computers, Materials & Continua》 SCIE EI 2024年第9期4791-4812,共22页
In recent years,wearable devices-based Human Activity Recognition(HAR)models have received significant attention.Previously developed HAR models use hand-crafted features to recognize human activities,leading to the e... In recent years,wearable devices-based Human Activity Recognition(HAR)models have received significant attention.Previously developed HAR models use hand-crafted features to recognize human activities,leading to the extraction of basic features.The images captured by wearable sensors contain advanced features,allowing them to be analyzed by deep learning algorithms to enhance the detection and recognition of human actions.Poor lighting and limited sensor capabilities can impact data quality,making the recognition of human actions a challenging task.The unimodal-based HAR approaches are not suitable in a real-time environment.Therefore,an updated HAR model is developed using multiple types of data and an advanced deep-learning approach.Firstly,the required signals and sensor data are accumulated from the standard databases.From these signals,the wave features are retrieved.Then the extracted wave features and sensor data are given as the input to recognize the human activity.An Adaptive Hybrid Deep Attentive Network(AHDAN)is developed by incorporating a“1D Convolutional Neural Network(1DCNN)”with a“Gated Recurrent Unit(GRU)”for the human activity recognition process.Additionally,the Enhanced Archerfish Hunting Optimizer(EAHO)is suggested to fine-tune the network parameters for enhancing the recognition process.An experimental evaluation is performed on various deep learning networks and heuristic algorithms to confirm the effectiveness of the proposed HAR model.The EAHO-based HAR model outperforms traditional deep learning networks with an accuracy of 95.36,95.25 for recall,95.48 for specificity,and 95.47 for precision,respectively.The result proved that the developed model is effective in recognizing human action by taking less time.Additionally,it reduces the computation complexity and overfitting issue through using an optimization approach. 展开更多
关键词 Human action recognition multi-modal sensor data and signals adaptive hybrid deep attentive network enhanced archerfish hunting optimizer 1D convolutional neural network gated recurrent units
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A transfer learning framework using spatiotemporal graph convolutional network and adversarial domain adaptation for cross-district building group energy prediction 认领 引用
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作者 Yingjun Ruan Yamei Ma +5 位作者 Hua Meng Tingting Xu Yuting Yao Fanyue Qian Chaoliang Wang Wei Liu 《Building Simulation》 SCIE EI CSCD 2026年第1期235-255,共21页
Accurate and efficient prediction of energy consumption in district building groups is a critical foundation for effective energy management and achieving building energy-saving and emission reduction goals.In recent ... Accurate and efficient prediction of energy consumption in district building groups is a critical foundation for effective energy management and achieving building energy-saving and emission reduction goals.In recent years,deep learning methods have been widely applied to building energy consumption prediction.However,most existing approaches rely heavily on abundant historical data for model training.In practice,data scarcity,due to factors such as data privacy concerns or newly constructed buildings,poses a significant challenge.Additionally,many methods fail to account for spatial dependencies between buildings.To address these limitations,this paper proposes a novel prediction framework based on a spatiotemporal graph convolutional network integrated with adversarial domain adaptation,specifically designed for energy consumption prediction in district building groups under data-scarce conditions.The approach combines a graph convolutional network(GCN)based on an adaptive adjacency matrix with a graph attention network based on a dynamic time warping(DTW)correlation matrix to capture the spatiotemporal features of energy consumption,across the building group.The proposed model is first pre-trained on a data-rich source domain.Then,adversarial domain adaptation is employed to extract domain-invariant spatiotemporal features from both the source and target domains.This enables a“multi-buildings to multi-buildings”transfer of predictive knowledge from the source domain to the target domain.Extensive experiments were conducted using historical energy consumption data from two real-world district building groups to validate the effectiveness and robustness of the proposed method.Results demonstrate that adversarial domain adaptation can successfully leverage domain-invariant knowledge to improve prediction accuracy in data-scarce scenarios.Compared to models trained directly on the target domain without transfer learning,the proposed method reduces prediction error by 2.36%to 16.08%.Furthermore,when benchmarked against various mainstream transfer learning methods,the adversarial domain adaptation approach more effectively bridges the domain gap,yielding superior predictive performance.Additionally,to simulate real-world data collection scenarios,the impact of varying training data ratios on the proposed model performance was also evaluated. 展开更多
关键词 data scarcity district building energy consumption prediction cross-district transfer transfer learning spatiotemporal graph convolutional network adversarial domain adaptation
Deep Bi-Directional Adaptive Gating Graph Convolutional Networks for Spatio-Temporal Traffic Forecasting 认领 引用
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作者 Xin Wang Jianhui Lv +5 位作者 Madini O.Alassafi Fawaz E.Alsaadi B.D.Parameshachari Longhao Zou Gang Feng Zhonghua Liu 《Tsinghua Science and Technology》 SCIE EI CAS CSCD 2025年第5期2060-2080,共21页
With the advent of deep learning,various deep neural network architectures have been proposed to capture the complex spatio-temporal dependencies in traffic data.This paper introduces a novel Deep Bi-directional Adapt... With the advent of deep learning,various deep neural network architectures have been proposed to capture the complex spatio-temporal dependencies in traffic data.This paper introduces a novel Deep Bi-directional Adaptive Gating Graph Convolutional Network(DBAG-GCN)model for spatio-temporal traffic forecasting.The proposed model leverages the power of graph convolutional networks to capture the spatial dependencies in the road network topology and incorporates bi-directional gating mechanisms to control the information flow adaptively.Furthermore,we introduce a multi-scale temporal convolution module to capture multi-scale temporal dynamics and a contextual attention mechanism to integrate external factors such as weather conditions and event information.Extensive experiments on real-world traffic datasets demonstrate the superior performance of DBAG-GCN compared to state-of-the-art baselines,achieving significant improvements in prediction accuracy and computational efficiency.The DBAG-GCN model provides a powerful and flexible framework for spatio-temporal traffic forecasting,paving the way for intelligent transportation management and urban planning. 展开更多
关键词 traffic forecasting spatio-temporal modeling Graph Convolutional Networks(GCNs) adaptive gating
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Adaptive edge-aware graph convolutional with multi-task learning for simultaneous prediction of material properties 认领 引用
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作者 Yunhua Lu Mingyue Chen +4 位作者 Qingwei Zhang Junan Zhang Chao Zhang Shiai Xu Qiuyan Bi 《npj Computational Materials》 SCIE EI CSCD 2025年第1期4642-4655,共14页
The targeted design of functional materials often requires the concurrent optimization of multiple interdependent properties.For boron-doped graphene(BDG),both the band gap and work function critically influence perfo... The targeted design of functional materials often requires the concurrent optimization of multiple interdependent properties.For boron-doped graphene(BDG),both the band gap and work function critically influence performance in electronic and catalytic applications,yet existing machine learning(ML)approaches typically focus on single-property prediction and rely on hand-crafted features,limiting their generality.Here we present an adaptive edge-aware graph convolutional neural network with multi-task learning(AEGCNN-MTL)for simultaneous prediction of multiple material properties.On a DFT-computed BDG dataset of 2613 structures,AEGCNN-MTL achieved high accuracy(R2=0.9905 for band gap and 0.9778 for work function),and under identical training budgets,outperformed representative single-task GNN baselines.When transferred to the QM9 benchmark,the framework delivered competitive performance across 12 diverse quantum chemical properties,demonstrating strong generalization capability.These results highlight the potential of AEGCNN-MTL as a scalable and accurate tool for high-throughput,multi-property screening and the data-driven discovery of multifunctional materials. 展开更多
关键词 targeted design functional materials adaptive edge aware graph convolutional neural network machine learning ml approaches optimization multiple interdependent propertiesfor material properties functional materials electronic catalytic applicationsyet multi task learning
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