Stacking Ensemble Learning-Based Convolutional Gated Recurrent Neural Network for Diabetes Miletus

Diabetes mellitus is a metabolic disease in which blood glucose levels rise as a result of pancreatic insulin production failure.It causes hyperglycemia and chronic multiorgan dysfunction,including blindness,renal failure,and cardi-ovascular disease,if left untreated.One of the essential checks that are needed to be performed frequently in Type 1 Diabetes Mellitus is a blood test,this procedure involves extracting blood quite frequently,which leads to subject discomfort increasing the possibility of infection when the procedure is often recurring.Exist-ing methods used for diabetes classification have less classification accuracy and suffer from vanishing gradient problems,to overcome these issues,we proposed stacking ensemble learning-based convolutional gated recurrent neural network(CGRNN)Metamodel algorithm.Our proposed method initially performs outlier detection to remove outlier data,using the Gaussian distribution method,and the Box-cox method is used to correctly order the dataset.After the outliers’detec-tion,the missing values are replaced by the data’s mean rather than their elimina-tion.In the stacking ensemble base model,multiple machine learning algorithms like Naïve Bayes,Bagging with random forest,and Adaboost Decision tree have been employed.CGRNN Meta model uses two hidden layers Long-Short-Time Memory(LSTM)and Gated Recurrent Unit(GRU)to calculate the weight matrix for diabetes prediction.Finally,the calculated weight matrix is passed to the soft-max function in the output layer to produce the diabetes prediction results.By using LSTM-based CG-RNN,the mean square error(MSE)value is 0.016 and the obtained accuracy is 91.33%.

Mapping Network-Coordinated Stacked Gated Recurrent Units for Turbulence Prediction

Accurately predicting fluid forces acting on the sur-face of a structure is crucial in engineering design.However,this task becomes particularly challenging in turbulent flow,due to the complex and irregular changes in the flow field.In this study,we propose a novel deep learning method,named mapping net-work-coordinated stacked gated recurrent units(MSU),for pre-dicting pressure on a circular cylinder from velocity data.Specifi-cally,our coordinated learning strategy is designed to extract the most critical velocity point for prediction,a process that has not been explored before.In our experiments,MSU extracts one point from a velocity field containing 121 points and utilizes this point to accurately predict 100 pressure points on the cylinder.This method significantly reduces the workload of data measure-ment in practical engineering applications.Our experimental results demonstrate that MSU predictions are highly similar to the real turbulent data in both spatio-temporal and individual aspects.Furthermore,the comparison results show that MSU predicts more precise results,even outperforming models that use all velocity field points.Compared with state-of-the-art methods,MSU has an average improvement of more than 45%in various indicators such as root mean square error(RMSE).Through comprehensive and authoritative physical verification,we estab-lished that MSU’s prediction results closely align with pressure field data obtained in real turbulence fields.This confirmation underscores the considerable potential of MSU for practical applications in real engineering scenarios.The code is available at https://github.com/zhangzm0128/MSU.

Fake News Classification Using a Fuzzy Convolutional Recurrent Neural Network

In recent years,social media platforms have gained immense popularity.As a result,there has been a tremendous increase in content on social media platforms.This content can be related to an individual’s sentiments,thoughts,stories,advertisements,and news,among many other content types.With the recent increase in online content,the importance of identifying fake and real news has increased.Although,there is a lot of work present to detect fake news,a study on Fuzzy CRNN was not explored into this direction.In this work,a system is designed to classify fake and real news using fuzzy logic.The initial feature extraction process is done using a convolutional recurrent neural network(CRNN).After the extraction of features,word indexing is done with high dimensionality.Then,based on the indexing measures,the ranking process identifies whether news is fake or real.The fuzzy CRNN model is trained to yield outstanding resultswith 99.99±0.01%accuracy.This work utilizes three different datasets(LIAR,LIAR-PLUS,and ISOT)to find the most accurate model.

Recurrent Convolutional Neural Network MSER-Based Approach for Payable Document Processing

A tremendous amount of vendor invoices is generated in the corporate sector.To automate the manual data entry in payable documents,highly accurate Optical Character Recognition(OCR)is required.This paper proposes an end-to-end OCR system that does both localization and recognition and serves as a single unit to automate payable document processing such as cheques and cash disbursement.For text localization,the maximally stable extremal region is used,which extracts a word or digit chunk from an invoice.This chunk is later passed to the deep learning model,which performs text recognition.The deep learning model utilizes both convolution neural networks and long short-term memory(LSTM).The convolution layer is used for extracting features,which are fed to the LSTM.The model integrates feature extraction,modeling sequence,and transcription into a unified network.It handles the sequences of unconstrained lengths,independent of the character segmentation or horizontal scale normalization.Furthermore,it applies to both the lexicon-free and lexicon-based text recognition,and finally,it produces a comparatively smaller model,which can be implemented in practical applications.The overall superior performance in the experimental evaluation demonstrates the usefulness of the proposed model.The model is thus generic and can be used for other similar recognition scenarios.

多尺度特征和极化自注意力的Faster-RCNN水漂垃圾识别

针对小目标水漂垃圾形态多变、分辨率低且信息有限,导致检测效果不理想的问题,提出一种改进的Faster-RCNN(Faster Regions with Convolutional Neural Network)水漂垃圾检测算法MP-Faster-RCNN(Faster-RCNN with Multi-scale feature and Polarized self-attention)。首先,建立黄河兰州段小目标水漂垃圾数据集,将空洞卷积结合ResNet-50代替原来的VGG-16(Visual Geometry Group 16)作为主干特征提取网络,扩大感受野以提取更多小目标特征;其次,在区域生成网络(RPN)利用多尺度特征,设置3×3和1×1的两层卷积,补偿单一滑动窗口造成的特征丢失;最后,在RPN前加入极化自注意力,进一步利用多尺度和通道特征提取更细粒度的多尺度空间信息和通道间依赖关系,生成具有全局特征的特征图,实现更精确的目标框定位。实验结果表明,MP-Faster-RCNN能有效提高水漂垃圾检测精度,与原始Faster-RCNN相比,平均精度均值(mAP)提高了6.37个百分点,模型大小从521 MB降到了108 MB,且在同一训练批次下收敛更快。

Lightweight and highly robust memristor-based hybrid neural networks for electroencephalogram signal processing

Memristor-based neuromorphic computing shows great potential for high-speed and high-throughput signal processing applications,such as electroencephalogram(EEG)signal processing.Nonetheless,the size of one-transistor one-resistor(1T1R)memristor arrays is limited by the non-ideality of the devices,which prevents the hardware implementation of large and complex networks.In this work,we propose the depthwise separable convolution and bidirectional gate recurrent unit(DSC-BiGRU)network,a lightweight and highly robust hybrid neural network based on 1T1R arrays that enables efficient processing of EEG signals in the temporal,frequency and spatial domains by hybridizing DSC and BiGRU blocks.The network size is reduced and the network robustness is improved while ensuring the network classification accuracy.In the simulation,the measured non-idealities of the 1T1R array are brought into the network through statistical analysis.Compared with traditional convolutional networks,the network parameters are reduced by 95%and the network classification accuracy is improved by 21%at a 95%array yield rate and 5%tolerable error.This work demonstrates that lightweight and highly robust networks based on memristor arrays hold great promise for applications that rely on low consumption and high efficiency.

Multi-Scale Convolutional Gated Recurrent Unit Networks for Tool Wear Prediction in Smart Manufacturing

As an integrated application of modern information technologies and artificial intelligence,Prognostic and Health Management(PHM)is important for machine health monitoring.Prediction of tool wear is one of the symbolic applications of PHM technology in modern manufacturing systems and industry.In this paper,a multi-scale Convolutional Gated Recurrent Unit network(MCGRU)is proposed to address raw sensory data for tool wear prediction.At the bottom of MCGRU,six parallel and independent branches with different kernel sizes are designed to form a multi-scale convolutional neural network,which augments the adaptability to features of different time scales.These features of different scales extracted from raw data are then fed into a Deep Gated Recurrent Unit network to capture long-term dependencies and learn significant representations.At the top of the MCGRU,a fully connected layer and a regression layer are built for cutting tool wear prediction.Two case studies are performed to verify the capability and effectiveness of the proposed MCGRU network and results show that MCGRU outperforms several state-of-the-art baseline models.

FinBERT-RCNN-ATTACK:金融文本情感分析模型

金融文本包含投资者的情绪及公众对相关事件的态度。近年来,自然语言处理已广泛应用于金融领域,对金融文本数据进行情感分析可以得到丰富的投资价值和监管参考价值。然而由于金融词汇具有专业性和特殊性,现有的通用情感分析模型不适合金融领域情感分析任务,精确度有待提高,且现有模型易受到对抗样本的干扰导致模型结果出错。为了解决这些问题,提出了一个FinBERT-RCNN-ATTACK模型。利用在金融语料库预训练的FinBERT模型进行词嵌入处理,提取语义特征,将提取到的特征引入RCNN模型进一步挖掘上下文的关键特征,并且在模型中引入对抗训练,即在嵌入阶段添加扰动,提高模型的鲁棒性和泛化性。实验结果表明,在金融领域数据集上,提出的模型优于其他情感分析模型,精准度提升了3%~35%。

基于改进Faster RCNN的PCB表面缺陷检测研究

印刷电路板(PCB)在制造过程中不可避免地存在焊点缺焊、短路、毛刺、缺口、开路、余铜等微小缺陷。传统的基于机器视觉检测的缺陷检测方法存在检测速度慢、误检率和漏检率高、抗干扰能力弱等问题。为解决上述问题,提出一种基于改进快速区域卷积神经网络(Faster RCNN)的PCB表面缺陷检测方法。首先,在传统Faster RCNN框架的基础上,融入扩展特征金字塔网络(EFPN)以实现特征提取与融合,并进行多尺度检测,从而尽可能保留图像细节信息以提高检测性能。其次,利用K-means算法结合交并比(IoU)优化区域建议网络(RPN)结构中的锚框参数,使得生成的锚框方案更有针对性。试验结果表明,改进Faster RCNN在PCB缺陷数据集上的全类平均正确率(mAP)值达到93.4%、检测速度达到每秒21.79帧。所提方法可推广应用至芯片、光学器件表面微小缺陷在线检测,从而提升工业生产效率。

基于Transformer改进的Faster RCNN在复杂环境下的车辆检测

在监控视角中目标车辆较小、遮挡较为严重,导致检测精度低。通过探讨卷积神经网络和Transformer模型的互相借鉴和联系,并结合损失函数等常规改进,提出了新的Faster RCNN模型。通过借鉴Transformer模型的思想,对原有的特征提取网络进行了改进,将原block比例3∶4∶6∶3改为3∶3∶27∶3、卷积核由3×3改为7×7,增大其感受野,能够更好捕捉图像中的全局特征,使用DW卷积来减少参数量并略微提高性能,使用Channel shuffle解决通道间信息不交流的问题。将原先交并比IoU改为CIoU,与改进后的特征提取网络结合,进一步提高小目标和遮挡目标的检测效果。在UA-DETRAC数据集上,改进后的模型在mAP@0.5:0.95方面比原算法提高了20.20%,并在大、中、小目标下分别提高了15.8%、23%和45.8%,相较于其他模型,如YO⁃LOv7、YOLOv5和Cascade RCNN,mAP@0.5:0.95分别提高了3.3%、5%和6.69%。

利用改进RCNN卷积核的复合绝缘子缺陷识别方法

变电站复合绝缘子缺陷的检测仍然依赖于运行人员的巡检,巡检工作量大,易因视觉疲劳导致漏检。为减少计算资源消耗和缩短训练时间,通过重新组织卷积核改进了区域卷积神经网络(RCNN),提出了一种针对绝缘子裂纹形状特征的检测方法。该方法满足在训练样本数据不足的前提下,也能得到较好的卷积神经网络(CNN)训练效果,最终实现准确的裂纹识别。训练阶段采用RGB三通道分解方法,扩充训练数据集;利用中值滤波方法去除噪声;采用改进后的卷积核训练CNN。试验阶段将图片进行RGB三通道分解,并输入CNN得到确切的裂纹中心坐标、长度;采用非极大值抑制(NMS)算法去重,得到最终的裂纹识别结果。通过实例分析表明,在训练样本不足前提下,所提方法依然能达到较好的识别准确率,并能准确识别出裂纹的具体位置。

Deep Convolutional Neural Network Based Churn Prediction for Telecommunication Industry

Currently,mobile communication is one of the widely used means of communication.Nevertheless,it is quite challenging for a telecommunication company to attract new customers.The recent concept of mobile number portability has also aggravated the problem of customer churn.Companies need to identify beforehand the customers,who could potentially churn out to the competitors.In the telecommunication industry,such identification could be done based on call detail records.This research presents an extensive experimental study based on various deep learning models,such as the 1D convolutional neural network(CNN)model along with the recurrent neural network(RNN)and deep neural network(DNN)for churn prediction.We use the mobile telephony churn prediction dataset obtained from customers-dna.com,containing the data for around 100,000 individuals,out of which 86,000 are non-churners,whereas 14,000 are churned customers.The imbalanced data are handled using undersampling and oversampling.The accuracy for CNN,RNN,and DNN is 91%,93%,and 96%,respectively.Furthermore,DNN got 99%for ROC.

基于RoBERTa-RCNN和注意力池化的新闻主题文本分类

针对中文新闻主题因缺乏上下文信息而造成语义模糊和用词规范性不高的问题,提出一种基于RoBERTa-RCNN和多头注意力池化机制的新闻主题文本分类方法。利用数据增强技术对部分训练数据进行回译处理,再通过自编码预训练模型和RCNN对文本进行初步和深度的特征提取,并结合多头注意力思想改进最大池化层。该方法采用融合机制,改善了RCNN中最大池化策略单一和无法进行动态优化的缺陷。在三个新闻主题数据集上进行实验,使用更适用于新闻主题分类的Mish函数代替ReLU函数,并利用标签平滑来解决过拟合问题。结果表明,所提方法相比传统分类方法效果突出,并通过消融实验验证了模型在分类任务上的可行性。

基于改进的Faster RCNN的仪表自动识别方法

在环境复杂的工业场景中,仪表盘存在类别多、相似性高等问题,导致检测的识别效果较差、准确率不高。针对这一问题,提出了一种基于改进的更快速的区域卷积神经网络(Faster RCNN)的仪表自动识别方法。首先,采用残差网络(Resnet)101代替视觉几何群网络(VGG)16,进行了网络结构简化;然后,引入了特征金字塔网络(FPN),并将其改进为递归特征金字塔网络后进行了迭代融合,输出了特征图;接着,引入了注意力机制模块,根据特征的重要程度,完成了对输出通道权值的重新分配,增强了Faster RCNN对目标的运算能力;提出了改进非极大值抑制算法(Softer-NMS),通过降低置信度来确定准确的目标候选框;最后,采用Mosaic数据增强技术对可视对象类(VOC)2007数据集进行了扩充,对改进后的Faster RCNN模型进行了仪表自动识别的实验。研究结果表明:在相同工业环境下,与传统的Faster RCNN算法模型相比,改进后的Faster RCNN模型准确率为93.5%,较原模型提高了3.8%,mAP值为92.6%,较原模型提高了3.7%,可见该方法在实际生产中具有较强的鲁棒性与泛化能力,可满足工业上对智能检测的要求。

基于AE-RCNN的洪水分级智能预报方法研究

复杂产汇流特性地区使用洪水分级预报方法可提高预报精度,本文提出一种基于自编码器(Autoencoder,AE)和残差卷积神经网络(Residual Convolutional Neural Network,RCNN)的洪水分级智能预报方法,使用自编码器和K均值聚类算法实现对原始水文数据的特征提取和洪水分级,通过RCNN模型提升卷积神经网络的有效训练深度,以山东省小清河流域黄台桥水文站为例开展洪水分级智能预报研究。结果表明应用降维数据聚类的AE-RCNN模型MAE指标、RMSE指标、NSE指标分别为5.04、7.91、0.92,优于CNN模型、RCNN模型和降雨聚类RCNN模型。该方法能够有效提取水文数据特征、提高洪水预报精度。

基于Mask-RCNN与SFM的单目视觉长方体三维测量方法

为解决基于运动结构恢复(Structure from motion,SFM)多视角拍摄的局限性,以实现自动化三维测量效果,本文提出了一种可用于长方体三维测量的基于Mask-区域卷积神经网络(Mask-region convolutional neural networks,Mask-RCNN)和SFM的单目视觉测量方法。以箱体三维测量为例,该方法包括测量点提取、转换矩阵计算和三维映射测量三个部分,仅需一次标定获取内部参数,利用深度学习技术实现了单视角自动化三维测量,避免复杂重建的同时降低了视觉测量方法的应用要求。实验结果表明,该方法在棋盘格标志物下获得测量结果的相对标准不确定度在6%以内,在箱体自带标志物下获得测量结果的相对标准不确定度在8%以内。

Deep learning neural networks for spatially explicit prediction of flash flood probability

Flood probability maps are essential for a range of applications,including land use planning and developing mitigation strategies and early warning systems.This study describes the potential application of two architectures of deep learning neural networks,namely convolutional neural networks(CNN)and recurrent neural networks(RNN),for spatially explicit prediction and mapping of flash flood probability.To develop and validate the predictive models,a geospatial database that contained records for the historical flood events and geo-environmental characteristics of the Golestan Province in northern Iran was constructed.The step-wise weight assessment ratio analysis(SWARA)was employed to investigate the spatial interplay between floods and different influencing factors.The CNN and RNN models were trained using the SWARA weights and validated using the receiver operating characteristics technique.The results showed that the CNN model(AUC=0.832,RMSE=0.144)performed slightly better than the RNN model(AUC=0.814,RMSE=0.181)in predicting future floods.Further,these models demonstrated an improved prediction of floods compared to previous studies that used different models in the same study area.This study showed that the spatially explicit deep learning neural network models are successful in capturing the heterogeneity of spatial patterns of flood probability in the Golestan Province,and the resulting probability maps can be used for the development of mitigation plans in response to the future floods.The general policy implication of our study suggests that design,implementation,and verification of flood early warning systems should be directed to approximately 40%of the land area characterized by high and very susceptibility to flooding.

基于RCNN-ABiLSTM的机械设备剩余寿命预测方法

针对机械设备的关键退化信息易淹没在非线性、多维度、长时间、大规模监测数据中的问题,提出了一种基于残差卷积神经网络和注意力双向长短时记忆网络融合(residual convolutional neural network-attentional bidirectional long short-term memory network,RCNN-ABiLSTM)的机械设备剩余寿命预测方法。首先通过训练RCNN提取监测数据的深度空间特征;然后通过引入注意力机制,优化双向长短时记忆网络提取时间相关特征的权重参数,加强关键退化信息对剩余寿命预测的表达;最后通过航空发动机数据集验证了方法的有效性。分析结果表明,对于运行条件复杂和故障模式多变的多维监测数据,所提方法能够准确寻找退化时间点,有效提高长时间运行设备的剩余寿命预测准确度。

Deep Neural Network-Based Chinese Semantic Role Labeling

A recent trend in machine learning is to use deep architectures to discover multiple levels of features from data,which has achieved impressive results on various natural language processing(NLP)tasks.We propose a deep neural network-based solution to Chinese semantic role labeling(SRL)with its application on message analysis.The solution adopts a six-step strategy:text normalization,named entity recognition(NER),Chinese word segmentation and part-of-speech(POS)tagging,theme classification,SRL,and slot filling.For each step,a novel deep neural network-based model is designed and optimized,particularly for smart phone applications.Experiment results on all the NLP sub-tasks of the solution show that the proposed neural networks achieve state-of-the-art performance with the minimal computational cost.The speed advantage of deep neural networks makes them more competitive for large-scale applications or applications requiring real-time response,highlighting the potential of the proposed solution for practical NLP systems.

基于Rcnn+Char_SegNet的藏文乌梅长文本识别

藏文文字识别在藏文古籍文献、藏文办公自动化以及藏汉双语教育等领域具有非常重要的应用价值。作为两种常见的藏文字体之一,乌梅字体中笔画粘连和交错现象严重,导致识别难度较大。为此,该文提出了基于Rcnn+Char_SegNet的藏文乌梅长文本识别。首先,在CNN的每个卷积层中添加循环连接,增强CNN提取乌梅字粘连片段的特征和集成上下文信息的能力;其次,对提取的图像文本特征序列采用BiLSTM进行建模;最后,采用字丁切分模块增强CTC对图像序列和标签对齐的监督能力。在自行构建的Cursive Script-C517测试数据集上,该模型的最高准确率和平均准确率分别达到了99.80%和91.43%,分别比基线提高了1.45和48.47个百分点。此外,通过字符级词典库训练,使模型的训练时间减少了13.63%。实验表明,该方法有效解决了乌梅字体中笔画粘连和交错现象严重导致的识别错误问题,显著提升了印刷体藏文乌梅识别精度,减少了训练时间,且具有较好的鲁棒性。