Recently, convolutional neural networks (CNNs) have been utilized in medical imaging research field and have successfully shown their ability in image classification and detection. In this paper we used a CNN combined...Recently, convolutional neural networks (CNNs) have been utilized in medical imaging research field and have successfully shown their ability in image classification and detection. In this paper we used a CNN combined with a wavelet transform approach for classifying a dataset of 448 lung CT images into 4 categories, e.g. lung adenocarcinoma, lung squamous cell carcinoma, metastatic lung cancer, and normal. The key difference between the commonly-used CNNs and the presented method is that in this method, we adopt the use of redundant wavelet coefficients at level 1 as inputs to the CNN, instead of using original images. One of the main advantages of the proposed method is that it is not necessary to extract regions of interest from original images. The wavelet coefficients of the entire image are used as inputs to the CNN. We compare the classification performance of the proposed method to that of an existing CNN classifier and a CNN-based support vector machine classifier. The experimental results show that the proposed method outperforms the other two methods and achieve the highest overall accuracy of 91.9%. It demonstrates the potential for use in classification of lung diseases in CT images.展开更多
Computerized tomography (CT) scan is the only screening test recommended by doctors to look for lung cancer. Convolutional neural networks (CNNs) have recently proven their ability to successfully classify medical ima...Computerized tomography (CT) scan is the only screening test recommended by doctors to look for lung cancer. Convolutional neural networks (CNNs) have recently proven their ability to successfully classify medical images. Due to its strong compactness property, the Discrete Wavelet transform (DWT) has been commonly used in image feature extraction applications. This paper presents a novel technique for the classification of Lung cancer in Computerized Tomography (CT) scans using Wavelets to find discriminative features in the CT images and CNN to classify the extracted features. Experimental results prove that the proposed approach outperforms other commonly used methods and gives an overall accuracy of 99.5%.展开更多
The remaining useful life(RUL)estimation of bearings is critical for ensuring the reliability of mechanical systems.Owing to the rapid development of deep learning methods,a multitude of data-driven RUL estimation app...The remaining useful life(RUL)estimation of bearings is critical for ensuring the reliability of mechanical systems.Owing to the rapid development of deep learning methods,a multitude of data-driven RUL estimation approaches have been proposed recently.However,the following problems remain in existing methods:1)Most network models use raw data or statistical features as input,which renders it difficult to extract complex fault-related information hidden in signals;2)for current observations,the dependence between current states is emphasized,but their complex dependence on previous states is often disregarded;3)the output of neural networks is directly used as the estimated RUL in most studies,resulting in extremely volatile prediction results that lack robustness.Hence,a novel prognostics approach is proposed based on a time-frequency representation(TFR)subsequence,three-dimensional convolutional neural network(3DCNN),and Gaussian process regression(GPR).The approach primarily comprises two aspects:construction of a health indicator(HI)using the TFR-subsequence-3DCNN model,and RUL estimation based on the GPR model.The raw signals of the bearings are converted into TFR-subsequences by continuous wavelet transform and a dislocated overlapping strategy.Subsequently,the 3DCNN is applied to extract the hidden spatiotemporal features from the TFR-subsequences and construct HIs.Finally,the RUL of the bearings is estimated using the GPR model,which can also define the probability distribution of the potential function and prediction confidence.Experiments on the PRONOSTIA platform demonstrate the superiority of the proposed TFR-subsequence-3DCNN-GPR approach.The use of degradation-related spatiotemporal features in signals is proposed herein to achieve a highly accurate bearing RUL prediction with uncertainty quantification.展开更多
Noise reduction analysis of signals is essential for modern underwater acoustic detection systems.The traditional noise reduction techniques gradually lose efficacy because the target signal is masked by biological an...Noise reduction analysis of signals is essential for modern underwater acoustic detection systems.The traditional noise reduction techniques gradually lose efficacy because the target signal is masked by biological and natural noise in the marine environ-ment.The feature extraction method combining time-frequency spectrograms and deep learning can effectively achieve the separation of noise and target signals.A fully convolutional encoder-decoder neural network(FCEDN)is proposed to address the issue of noise reduc-tion in underwater acoustic signals.The time-domain waveform map of underwater acoustic signals is converted into a wavelet low-frequency analysis recording spectrogram during the denoising process to preserve as many underwater acoustic signal characteristics as possible.The FCEDN is built to learn the spectrogram mapping between noise and target signals that can be learned at each time level.The transposed convolution transforms are introduced,which can transform the spectrogram features of the signals into listenable audio files.After evaluating the systems on the ShipsEar Dataset,the proposed method can increase SNR and SI-SNR by 10.02 and 9.5dB,re-spectively.展开更多
Fluorescence microscopy is indispensable in life science research,yet denoising remains challenging due to varied biological samples and imaging conditions.We introduce a wavelet-enhanced transformer based on DnCNN th...Fluorescence microscopy is indispensable in life science research,yet denoising remains challenging due to varied biological samples and imaging conditions.We introduce a wavelet-enhanced transformer based on DnCNN that fuses wavelet preprocessing with a dual-branch transformer-convolutional neural network(CNN)architecture.Wavelet decomposition separates highand low-frequency components for targeted noise reduction;the CNN branch restores local details,whereas the transformer branch captures global context;and an adaptive loss balances quantitative fidelity with perceptual quality.On the fluorescence microscopy denoising benchmark,our method surpasses leading CNNand transformer-based approaches,improving peak signal-to-noise ratio by 2.34%and 0.88%and structural similarity index measure by 0.53%and 1.07%,respectively.This framework offers enhanced generalization and practical gains for fluorescence image denoising.展开更多
A wavelet-based local and global feature fusion network(LAGN)is proposed for low-light image enhancement,aiming to enhance image details and restore colors in dark areas.This study focuses on addressing three key issu...A wavelet-based local and global feature fusion network(LAGN)is proposed for low-light image enhancement,aiming to enhance image details and restore colors in dark areas.This study focuses on addressing three key issues in low-light image enhancement:Enhancing low-light images using LAGN to preserve image details and colors;extracting image edge information via wavelet transform to enhance image details;and extracting local and global features of images through convolutional neural networks and Transformer to improve image contrast.Comparisons with state-of-the-art methods on two datasets verify that LAGN achieves the best performance in terms of details,brightness,and contrast.展开更多
Convolutional neural networks(CNNs) have shown great potential for image super-resolution(SR).However,most existing CNNs only reconstruct images in the spatial domain,resulting in insufficient high-frequency details o...Convolutional neural networks(CNNs) have shown great potential for image super-resolution(SR).However,most existing CNNs only reconstruct images in the spatial domain,resulting in insufficient high-frequency details of reconstructed images.To address this issue,a channel attention based wavelet cascaded network for image super-resolution(CWSR) is proposed.Specifically,a second-order channel attention(SOCA) mechanism is incorporated into the network,and the covariance matrix normalization is utilized to explore interdependencies between channel-wise features.Then,to boost the quality of residual features,the non-local module is adopted to further improve the global information integration ability of the network.Finally,taking the image loss in the spatial and wavelet domains into account,a dual-constrained loss function is proposed to optimize the network.Experimental results illustrate that CWSR outperforms several state-of-the-art methods in terms of both visual quality and quantitative metrics.展开更多
Centrifugal Pumps(CPs)are critical machine components in many industries,and their efficient operation and reliable Fault Diagnosis(FD)are essential for minimizing downtime and maintenance costs.This paper introduces ...Centrifugal Pumps(CPs)are critical machine components in many industries,and their efficient operation and reliable Fault Diagnosis(FD)are essential for minimizing downtime and maintenance costs.This paper introduces a novel FD method to improve both the accuracy and reliability of detecting potential faults in such pumps.Theproposed method combinesWaveletCoherent Analysis(WCA)and Stockwell Transform(S-transform)scalograms with Sobel and non-local means filters,effectively capturing complex fault signatures from vibration signals.Using Convolutional Neural Network(CNN)for feature extraction,the method transforms these scalograms into image inputs,enabling the recognition of patterns that span both time and frequency domains.The CNN extracts essential discriminative features,which are then merged and passed into a Kolmogorov-Arnold Network(KAN)classifier,ensuring precise fault identification.The proposed approach was experimentally validated on diverse datasets collected under varying conditions,demonstrating its robustness and generalizability.Achieving classification accuracy of 100%,99.86%,and 99.92%across the datasets,this method significantly outperforms traditional fault detection approaches.These results underscore the potential to enhance CP FD,providing an effective solution for predictive maintenance and improving overall system reliability.展开更多
目的近年来,基于深度学习的水印方法得到了广泛研究。现有方法通常对特征图的低频和高频部分同等对待,忽视了不同频率成分之间的重要差异,导致模型在处理多样化攻击时缺乏灵活性,难以同时实现水印的高保真性和强鲁棒性。为此,本文提出...目的近年来,基于深度学习的水印方法得到了广泛研究。现有方法通常对特征图的低频和高频部分同等对待,忽视了不同频率成分之间的重要差异,导致模型在处理多样化攻击时缺乏灵活性,难以同时实现水印的高保真性和强鲁棒性。为此,本文提出一种频率感知驱动的深度鲁棒图像水印技术(deep robust image watermarking driven by frequency awareness,RIWFP)。方法通过差异化机制处理低频和高频成分,提升水印性能。具体而言,低频成分通过小波卷积神经网络进行建模,利用宽感受野卷积在粗粒度层面高效学习全局结构和上下文信息;高频成分则采用深度可分离卷积和注意力机制组成的特征蒸馏块进行精炼,强化图像细节,在细粒度层面高效捕捉高频信息。此外,本文使用多频率小波损失函数,引导模型聚焦于不同频带的特征分布,进一步提升生成图像的质量。结果实验结果表明,提出的频率感知驱动的深度鲁棒图像水印技术在多个数据集上均表现出优越性能。在COCO(common objects in context)数据集上,RIWFP在随机丢弃攻击下的准确率达到91.4%;在椒盐噪声和中值滤波攻击下,RIWFP分别以100%和99.5%的准确率达到了最高水平,展现了其对高频信息的高效学习能力。在Ima⁃geNet数据集上,RIWFP在裁剪攻击下的准确率为93.4%;在JPEG压缩攻击下的准确率为99.6%,均显著优于其他对比方法。综合来看,RIWFP在COCO和ImageNet数据集上的平均准确率分别为96.7%和96.9%,均高于其他对比方法。结论本文所提方法通过频率感知的粗到细处理策略,显著增强了水印的不可见性和鲁棒性,在处理多种攻击时表现出优越性能。展开更多
This research presents a Human Lower Limb Activity Recognition(HLLAR)system that identifies specific activities and predicts the angles of the knees simultaneously,based on the EMG signals.The HLLAR systems streamline...This research presents a Human Lower Limb Activity Recognition(HLLAR)system that identifies specific activities and predicts the angles of the knees simultaneously,based on the EMG signals.The HLLAR systems streamlines the research on the lower limb activities.The HILLAR model includes Discrete Hermite Wavelets Transform-based Synchrosqueezing(DHWTS),Deep Two-Layer Multiscale Convolutional Neural Network(DTLMCNN),and Generalized Regression Neural Network(GRNN)as feature extraction,activity recognition,and knee angle prediction respectively.Electromyography signal-based automatic lower limb activity detection is crucial to rehabilitation and human movement analysis.Yet several of these methods face issues in feature extraction in complex data,overlapping signals,extraction of crucial parameters,and adaptation constraints.This research aims classify lower limb activities and predict knee joint angles from electromy-ography signals using HILLAR model.The model is validated on two datasets,comprising 26 subjects performing three classes of activities:walking,standing,and sitting.The proposed model obtained a classification accuracy of 99.95%,along with significant achievements in precision(99.93%),recall(99.91%),and F1-score(99.93%).The generalized regression neural network predicted angles of the knee joint with a root mean squared error of 1.25%.Robustness is demonstrated through consistent results in five-fold cross-validation and statistical significance testing(p-value=0.004,McNemar's test).Additionally,the proposed model showed superior performance over baseline methods by reducing error rates by 18%and decreasing processing time to 0.98 s.展开更多
文摘Recently, convolutional neural networks (CNNs) have been utilized in medical imaging research field and have successfully shown their ability in image classification and detection. In this paper we used a CNN combined with a wavelet transform approach for classifying a dataset of 448 lung CT images into 4 categories, e.g. lung adenocarcinoma, lung squamous cell carcinoma, metastatic lung cancer, and normal. The key difference between the commonly-used CNNs and the presented method is that in this method, we adopt the use of redundant wavelet coefficients at level 1 as inputs to the CNN, instead of using original images. One of the main advantages of the proposed method is that it is not necessary to extract regions of interest from original images. The wavelet coefficients of the entire image are used as inputs to the CNN. We compare the classification performance of the proposed method to that of an existing CNN classifier and a CNN-based support vector machine classifier. The experimental results show that the proposed method outperforms the other two methods and achieve the highest overall accuracy of 91.9%. It demonstrates the potential for use in classification of lung diseases in CT images.
文摘Computerized tomography (CT) scan is the only screening test recommended by doctors to look for lung cancer. Convolutional neural networks (CNNs) have recently proven their ability to successfully classify medical images. Due to its strong compactness property, the Discrete Wavelet transform (DWT) has been commonly used in image feature extraction applications. This paper presents a novel technique for the classification of Lung cancer in Computerized Tomography (CT) scans using Wavelets to find discriminative features in the CT images and CNN to classify the extracted features. Experimental results prove that the proposed approach outperforms other commonly used methods and gives an overall accuracy of 99.5%.
基金Supported by National Key Research and Development Project of China(Grant No.2020YFB2007700)State Key Laboratory of Tribology Initiative Research Program(Grant No.SKLT2020D21)+2 种基金National Natural Science Foundation of China(Grant No.51975309)Shaanxi Provincial Natural Science Foundation of China(Grant No.2019JQ-712)Young Talent Fund of University Association for Science and Technology in Shaanxi(Grant No.20170511).
文摘The remaining useful life(RUL)estimation of bearings is critical for ensuring the reliability of mechanical systems.Owing to the rapid development of deep learning methods,a multitude of data-driven RUL estimation approaches have been proposed recently.However,the following problems remain in existing methods:1)Most network models use raw data or statistical features as input,which renders it difficult to extract complex fault-related information hidden in signals;2)for current observations,the dependence between current states is emphasized,but their complex dependence on previous states is often disregarded;3)the output of neural networks is directly used as the estimated RUL in most studies,resulting in extremely volatile prediction results that lack robustness.Hence,a novel prognostics approach is proposed based on a time-frequency representation(TFR)subsequence,three-dimensional convolutional neural network(3DCNN),and Gaussian process regression(GPR).The approach primarily comprises two aspects:construction of a health indicator(HI)using the TFR-subsequence-3DCNN model,and RUL estimation based on the GPR model.The raw signals of the bearings are converted into TFR-subsequences by continuous wavelet transform and a dislocated overlapping strategy.Subsequently,the 3DCNN is applied to extract the hidden spatiotemporal features from the TFR-subsequences and construct HIs.Finally,the RUL of the bearings is estimated using the GPR model,which can also define the probability distribution of the potential function and prediction confidence.Experiments on the PRONOSTIA platform demonstrate the superiority of the proposed TFR-subsequence-3DCNN-GPR approach.The use of degradation-related spatiotemporal features in signals is proposed herein to achieve a highly accurate bearing RUL prediction with uncertainty quantification.
基金supported by the National Natural Science Foundation of China(No.41906169)the PLA Academy of Military Sciences.
文摘Noise reduction analysis of signals is essential for modern underwater acoustic detection systems.The traditional noise reduction techniques gradually lose efficacy because the target signal is masked by biological and natural noise in the marine environ-ment.The feature extraction method combining time-frequency spectrograms and deep learning can effectively achieve the separation of noise and target signals.A fully convolutional encoder-decoder neural network(FCEDN)is proposed to address the issue of noise reduc-tion in underwater acoustic signals.The time-domain waveform map of underwater acoustic signals is converted into a wavelet low-frequency analysis recording spectrogram during the denoising process to preserve as many underwater acoustic signal characteristics as possible.The FCEDN is built to learn the spectrogram mapping between noise and target signals that can be learned at each time level.The transposed convolution transforms are introduced,which can transform the spectrogram features of the signals into listenable audio files.After evaluating the systems on the ShipsEar Dataset,the proposed method can increase SNR and SI-SNR by 10.02 and 9.5dB,re-spectively.
基金supported by the National Natural Science Foundation of China(Grant No.62275210)the National Leading Talent Program,the National Young Talent Program,the Key Research and Development Program of Shaanxi(Grant No.2024SF2-GJHX-25)+5 种基金the Scientific Research Program Funded by the Education Department of Shaanxi Provincial Government(Grant No.24JS016)the Xidian University Specially Funded Project for Interdisciplinary Exploration(Grant No.TZJHF202523)the Fundamental Research Funds for Central Universities(Grant No.YJSJ25014)the Guangdong Provincial General Colleges and Universities Young Innovative Talents Research Project(Grant No.2024KQNCX172)the Shenzhen Science and Technology Program(Grant No.GJHZ20210705141805015)the Key Research Areas Support Science and Technology Project of Shenzhen Institute of Information Technology(Grant No.SZIIT2024KJ056).
文摘Fluorescence microscopy is indispensable in life science research,yet denoising remains challenging due to varied biological samples and imaging conditions.We introduce a wavelet-enhanced transformer based on DnCNN that fuses wavelet preprocessing with a dual-branch transformer-convolutional neural network(CNN)architecture.Wavelet decomposition separates highand low-frequency components for targeted noise reduction;the CNN branch restores local details,whereas the transformer branch captures global context;and an adaptive loss balances quantitative fidelity with perceptual quality.On the fluorescence microscopy denoising benchmark,our method surpasses leading CNNand transformer-based approaches,improving peak signal-to-noise ratio by 2.34%and 0.88%and structural similarity index measure by 0.53%and 1.07%,respectively.This framework offers enhanced generalization and practical gains for fluorescence image denoising.
文摘A wavelet-based local and global feature fusion network(LAGN)is proposed for low-light image enhancement,aiming to enhance image details and restore colors in dark areas.This study focuses on addressing three key issues in low-light image enhancement:Enhancing low-light images using LAGN to preserve image details and colors;extracting image edge information via wavelet transform to enhance image details;and extracting local and global features of images through convolutional neural networks and Transformer to improve image contrast.Comparisons with state-of-the-art methods on two datasets verify that LAGN achieves the best performance in terms of details,brightness,and contrast.
基金Supported by the National Natural Science Foundation of China(No.61901183)Fundamental Research Funds for the Central Universities(No.ZQN921)+4 种基金Natural Science Foundation of Fujian Province Science and Technology Department(No.2021H6037)Key Project of Quanzhou Science and Technology Plan(No.2021C008R)Natural Science Foundation of Fujian Province(No.2019J01010561)Education and Scientific Research Project for Young and Middle-aged Teachers of Fujian Province 2019(No.JAT191080)Science and Technology Bureau of Quanzhou(No.2017G046)。
文摘Convolutional neural networks(CNNs) have shown great potential for image super-resolution(SR).However,most existing CNNs only reconstruct images in the spatial domain,resulting in insufficient high-frequency details of reconstructed images.To address this issue,a channel attention based wavelet cascaded network for image super-resolution(CWSR) is proposed.Specifically,a second-order channel attention(SOCA) mechanism is incorporated into the network,and the covariance matrix normalization is utilized to explore interdependencies between channel-wise features.Then,to boost the quality of residual features,the non-local module is adopted to further improve the global information integration ability of the network.Finally,taking the image loss in the spatial and wavelet domains into account,a dual-constrained loss function is proposed to optimize the network.Experimental results illustrate that CWSR outperforms several state-of-the-art methods in terms of both visual quality and quantitative metrics.
基金supported by the Technology Innovation Program(20023566,‘Development and Demonstration of Industrial IoT and AI-Based Process Facility Intelligence Support System in Small and Medium Manufacturing Sites’)funded by the Ministry of Trade,Industry,&Energy(MOTIE,Republic of Korea).
文摘Centrifugal Pumps(CPs)are critical machine components in many industries,and their efficient operation and reliable Fault Diagnosis(FD)are essential for minimizing downtime and maintenance costs.This paper introduces a novel FD method to improve both the accuracy and reliability of detecting potential faults in such pumps.Theproposed method combinesWaveletCoherent Analysis(WCA)and Stockwell Transform(S-transform)scalograms with Sobel and non-local means filters,effectively capturing complex fault signatures from vibration signals.Using Convolutional Neural Network(CNN)for feature extraction,the method transforms these scalograms into image inputs,enabling the recognition of patterns that span both time and frequency domains.The CNN extracts essential discriminative features,which are then merged and passed into a Kolmogorov-Arnold Network(KAN)classifier,ensuring precise fault identification.The proposed approach was experimentally validated on diverse datasets collected under varying conditions,demonstrating its robustness and generalizability.Achieving classification accuracy of 100%,99.86%,and 99.92%across the datasets,this method significantly outperforms traditional fault detection approaches.These results underscore the potential to enhance CP FD,providing an effective solution for predictive maintenance and improving overall system reliability.
文摘目的近年来,基于深度学习的水印方法得到了广泛研究。现有方法通常对特征图的低频和高频部分同等对待,忽视了不同频率成分之间的重要差异,导致模型在处理多样化攻击时缺乏灵活性,难以同时实现水印的高保真性和强鲁棒性。为此,本文提出一种频率感知驱动的深度鲁棒图像水印技术(deep robust image watermarking driven by frequency awareness,RIWFP)。方法通过差异化机制处理低频和高频成分,提升水印性能。具体而言,低频成分通过小波卷积神经网络进行建模,利用宽感受野卷积在粗粒度层面高效学习全局结构和上下文信息;高频成分则采用深度可分离卷积和注意力机制组成的特征蒸馏块进行精炼,强化图像细节,在细粒度层面高效捕捉高频信息。此外,本文使用多频率小波损失函数,引导模型聚焦于不同频带的特征分布,进一步提升生成图像的质量。结果实验结果表明,提出的频率感知驱动的深度鲁棒图像水印技术在多个数据集上均表现出优越性能。在COCO(common objects in context)数据集上,RIWFP在随机丢弃攻击下的准确率达到91.4%;在椒盐噪声和中值滤波攻击下,RIWFP分别以100%和99.5%的准确率达到了最高水平,展现了其对高频信息的高效学习能力。在Ima⁃geNet数据集上,RIWFP在裁剪攻击下的准确率为93.4%;在JPEG压缩攻击下的准确率为99.6%,均显著优于其他对比方法。综合来看,RIWFP在COCO和ImageNet数据集上的平均准确率分别为96.7%和96.9%,均高于其他对比方法。结论本文所提方法通过频率感知的粗到细处理策略,显著增强了水印的不可见性和鲁棒性,在处理多种攻击时表现出优越性能。
文摘针对传统滚动轴承故障诊断方法过度依赖人工提取与分析特征、模型泛化性差以及对时序和通道深层次特征读取不充分的问题,提出了一种基于时频图与改进的卷积神经网络(Convolutional Neural Network,CNN)相结合的滚动轴承故障诊断方法。首先,将滚动轴承的原始振动信号经过连续小波变换(Continuous Wavelet Transform,CWT)转化为二维时频图,再利用内嵌长短期记忆网络(Long Short Term Memory,LSTM)的二维卷积神经网络从变换后的时频图中充分提取图像的时序特征,然后,通过高效通道注意力机制(Efficient Channel Attention,ECA)获取通道的全局信息并自适应地对各通道权重值进行动态调整,建立通道间的联系,自适应提取深层次关键特征。最后,利用凯斯西储大学滚动轴承故障数据集进行实验验证。实验结果表明,相较于一些常见的滚动轴承故障诊断方法,该方法在诊断准确率方面有明显提高。
文摘This research presents a Human Lower Limb Activity Recognition(HLLAR)system that identifies specific activities and predicts the angles of the knees simultaneously,based on the EMG signals.The HLLAR systems streamlines the research on the lower limb activities.The HILLAR model includes Discrete Hermite Wavelets Transform-based Synchrosqueezing(DHWTS),Deep Two-Layer Multiscale Convolutional Neural Network(DTLMCNN),and Generalized Regression Neural Network(GRNN)as feature extraction,activity recognition,and knee angle prediction respectively.Electromyography signal-based automatic lower limb activity detection is crucial to rehabilitation and human movement analysis.Yet several of these methods face issues in feature extraction in complex data,overlapping signals,extraction of crucial parameters,and adaptation constraints.This research aims classify lower limb activities and predict knee joint angles from electromy-ography signals using HILLAR model.The model is validated on two datasets,comprising 26 subjects performing three classes of activities:walking,standing,and sitting.The proposed model obtained a classification accuracy of 99.95%,along with significant achievements in precision(99.93%),recall(99.91%),and F1-score(99.93%).The generalized regression neural network predicted angles of the knee joint with a root mean squared error of 1.25%.Robustness is demonstrated through consistent results in five-fold cross-validation and statistical significance testing(p-value=0.004,McNemar's test).Additionally,the proposed model showed superior performance over baseline methods by reducing error rates by 18%and decreasing processing time to 0.98 s.
