Alzheimer’s Disease Stage Classification Using a Deep Transfer Learning and Sparse Auto Encoder Method

Alzheimer’s Disease(AD)is a progressive neurological disease.Early diagnosis of this illness using conventional methods is very challenging.Deep Learning(DL)is one of the finest solutions for improving diagnostic procedures’performance and forecast accuracy.The disease’s widespread distribution and elevated mortality rate demonstrate its significance in the older-onset and younger-onset age groups.In light of research investigations,it is vital to consider age as one of the key criteria when choosing the subjects.The younger subjects are more susceptible to the perishable side than the older onset.The proposed investigation concentrated on the younger onset.The research used deep learning models and neuroimages to diagnose and categorize the disease at its early stages automatically.The proposed work is executed in three steps.The 3D input images must first undergo image pre-processing using Weiner filtering and Contrast Limited Adaptive Histogram Equalization(CLAHE)methods.The Transfer Learning(TL)models extract features,which are subsequently compressed using cascaded Auto Encoders(AE).The final phase entails using a Deep Neural Network(DNN)to classify the phases of AD.The model was trained and tested to classify the five stages of AD.The ensemble ResNet-18 and sparse autoencoder with DNN model achieved an accuracy of 98.54%.The method is compared to state-of-the-art approaches to validate its efficacy and performance.

Pseudo Zernike Moment and Deep Stacked Sparse Autoencoder for COVID-19 Diagnosis

(Aim)COVID-19 is an ongoing infectious disease.It has caused more than 107.45 m confirmed cases and 2.35 m deaths till 11/Feb/2021.Traditional computer vision methods have achieved promising results on the automatic smart diagnosis.(Method)This study aims to propose a novel deep learning method that can obtain better performance.We use the pseudo-Zernike moment(PZM),derived from Zernike moment,as the extracted features.Two settings are introducing:(i)image plane over unit circle;and(ii)image plane inside the unit circle.Afterward,we use a deep-stacked sparse autoencoder(DSSAE)as the classifier.Besides,multiple-way data augmentation is chosen to overcome overfitting.The multiple-way data augmentation is based on Gaussian noise,salt-and-pepper noise,speckle noise,horizontal and vertical shear,rotation,Gamma correction,random translation and scaling.(Results)10 runs of 10-fold cross validation shows that our PZM-DSSAE method achieves a sensitivity of 92.06%±1.54%,a specificity of 92.56%±1.06%,a precision of 92.53%±1.03%,and an accuracy of 92.31%±1.08%.Its F1 score,MCC,and FMI arrive at 92.29%±1.10%,84.64%±2.15%,and 92.29%±1.10%,respectively.The AUC of our model is 0.9576.(Conclusion)We demonstrate“image plane over unit circle”can get better results than“image plane inside a unit circle.”Besides,this proposed PZM-DSSAE model is better than eight state-of-the-art approaches.

Anomaly-Based Intrusion DetectionModel Using Deep Learning for IoT Networks

The rapid growth of Internet of Things(IoT)devices has brought numerous benefits to the interconnected world.However,the ubiquitous nature of IoT networks exposes them to various security threats,including anomaly intrusion attacks.In addition,IoT devices generate a high volume of unstructured data.Traditional intrusion detection systems often struggle to cope with the unique characteristics of IoT networks,such as resource constraints and heterogeneous data sources.Given the unpredictable nature of network technologies and diverse intrusion methods,conventional machine-learning approaches seem to lack efficiency.Across numerous research domains,deep learning techniques have demonstrated their capability to precisely detect anomalies.This study designs and enhances a novel anomaly-based intrusion detection system(AIDS)for IoT networks.Firstly,a Sparse Autoencoder(SAE)is applied to reduce the high dimension and get a significant data representation by calculating the reconstructed error.Secondly,the Convolutional Neural Network(CNN)technique is employed to create a binary classification approach.The proposed SAE-CNN approach is validated using the Bot-IoT dataset.The proposed models exceed the performance of the existing deep learning approach in the literature with an accuracy of 99.9%,precision of 99.9%,recall of 100%,F1 of 99.9%,False Positive Rate(FPR)of 0.0003,and True Positive Rate(TPR)of 0.9992.In addition,alternative metrics,such as training and testing durations,indicated that SAE-CNN performs better.

Sparse Autoencoder-based Multi-head Deep Neural Networks for Machinery Fault Diagnostics with Detection of Novelties

Supervised fault diagnosis typically assumes that all the types of machinery failures are known.However,in practice unknown types of defect,i.e.,novelties,may occur,whose detection is a challenging task.In this paper,a novel fault diagnostic method is developed for both diagnostics and detection of novelties.To this end,a sparse autoencoder-based multi-head Deep Neural Network(DNN)is presented to jointly learn a shared encoding representation for both unsupervised reconstruction and supervised classification of the monitoring data.The detection of novelties is based on the reconstruction error.Moreover,the computational burden is reduced by directly training the multi-head DNN with rectified linear unit activation function,instead of performing the pre-training and fine-tuning phases required for classical DNNs.The addressed method is applied to a benchmark bearing case study and to experimental data acquired from a delta 3D printer.The results show that its performance is satisfactory both in detection of novelties and fault diagnosis,outperforming other state-of-the-art methods.This research proposes a novel fault diagnostics method which can not only diagnose the known type of defect,but also detect unknown types of defects.

大坝渗流安全监测数据异常检测的改进DSAE模型

针对现有大坝渗流安全监测数据异常检测方法存在检测效率和精度较低的不足,以及在异常阈值拟定过程中大多未能综合考虑监测数据随机性和模糊性的问题,提出大坝渗流安全监测数据异常检测的改进深度稀疏自编码器(deep sparse autoencoder,DSAE)模型。在以奇异谱分析方法提取监测数据残差分量的基础上,采用基于混沌初始化和非线性飞行速率改进的天鹰优化(improved Aquila optimization,IAO)算法对DSAE的超参数进行优化,建立IAO-DSAE模型,实现对监测数据残差分量的高精度重构;然后,在异常阈值的拟定过程中,将逆向云算法中的期望和熵值分别替代传统3σ法中的均值和标准差,以综合考虑监测数据的随机性和模糊性对异常阈值拟定的影响,提高异常检测结果的可靠性。工程案例研究表明,相比于基于统计模型法和3σ法的异常检测方法,根据所提方法处理后的渗流安全监测数据建立的预测模型,预测精度的平均提高幅度分别为5.56%和6.99%,验证了所提方法的有效性。

Optimizing Big Data Retrieval and Job Scheduling Using Deep Learning Approaches

Big data analytics in business intelligence do not provide effective data retrieval methods and job scheduling that will cause execution inefficiency and low system throughput.This paper aims to enhance the capability of data retrieval and job scheduling to speed up the operation of big data analytics to overcome inefficiency and low throughput problems.First,integrating stacked sparse autoencoder and Elasticsearch indexing explored fast data searching and distributed indexing,which reduces the search scope of the database and dramatically speeds up data searching.Next,exploiting a deep neural network to predict the approximate execution time of a job gives prioritized job scheduling based on the shortest job first,which reduces the average waiting time of job execution.As a result,the proposed data retrieval approach outperforms the previous method using a deep autoencoder and Solr indexing,significantly improving the speed of data retrieval up to 53%and increasing system throughput by 53%.On the other hand,the proposed job scheduling algorithmdefeats both first-in-first-out andmemory-sensitive heterogeneous early finish time scheduling algorithms,effectively shortening the average waiting time up to 5%and average weighted turnaround time by 19%,respectively.

一种SSAE+BPNN的变工况飞灰含碳量软测量方法

火电机组变工况运行使数据呈现多模态特征,导致基于浅层网络结构的回归软测量模型的预测精度下降。研究一种改进的BP神经网络(back propagation neural network,BPNN)软测量方法:首先利用堆叠稀疏自编码器(stacked sparse autoencoder,SSAE)强大的深度学习能力提取原始数据特征,然后再利用BPNN对提取特征进行回归分析。经实验验证,SSAE+BPNN软测量方法的均方误差为0.135 8×10–3,平方相关系数为0.983 2,其预测精度和泛化能力显著优于BPNN。将其应用于某台灵活调峰的超超临界660 MW发电机组飞灰含碳量软测量中,预测结果的平均相对误差为0.91%,总体相对误差控制在±5%以内,具有良好的工程应用价值。

基于MSSA+IESN+MFFN组合算法的齿轮箱早期故障智能诊断

齿轮箱故障诊断存在变速工况、样本数量偏少以及会形成强噪声情况,提出了一种通过多尺度特征融合网络(MFFN)实现故障诊断技术。对初始时域信号拓展形成多特征域,建立造多维堆栈稀疏自编码器(MSSA)对不同特征域进行故障采集,通过粒子群算法优化回声状态网络(IESN)进行信号处理。研究结果表明:样本充足条件下,MFFN模型诊断时,定速工况为99.15%,变速工况为98.46%,达到了更高准确率并降低了标准差。在样本不足条件下,深度特征融合网络(DEFN)和MFFN对于样本数量减少表现出了优异鲁棒性,MFFN达到了更优的性能。在噪声干扰场景下,采用MFFN依然能够达到85%的准确率。该算法具备更优抗干扰性能,采用多维特征提取能够更好地适应处于强噪声干扰环境。该研究为实现传动系统的稳定运行提供了理论参考。

镍基高温合金铣削刀具磨损预测

搭建镍基高温合金铣削实验测试平台,分析刀具磨损变化规律,提出了一种基于堆叠稀疏自动编码器和多传感器特征融合的新型深度学习方法,用于铣削刀具磨损预测。在时域、频域和时频域中提取信号特征,并通过相关性分析确定最优的多传感器特征,输入堆叠稀疏自动编码器进行深度特征学习。利用双向长短时记忆网络建立刀具磨损预测模型,应用不同的铣削磨损实验数据集来验证训练模型的预测性能。预测结果表明,所提模型均方根误差与传统模型相比至少减小了9.6%,证明了多传感器特征融合和深度学习方法的结合可以提高预测性能。

高光谱图像稀疏约束与自编码器特征提取相结合的异常检测方法

高光谱图像的异常检测在军事、农业、勘探、防火等领域具有重要的应用价值。传统的高光谱图像异常检测算法未能有效地挖掘图像光谱的深层特征,而深度学习方法具有良好的提取深层特征信息的能力。由于异常检测问题一般无法获取地物先验信息,因此无监督网络相比于监督网络要更为适用。而现有的基于自编码器的异常检测算法没有对局部信息进行有效利用,导致检测效果受限。针对这一问题,本文提出一种基于稀疏表示约束的自编码器深度特征提取方法。首先通过栈式自编码器得到深层次语义信息;然后利用稀疏表示作为约束与编码器进行有效结合,挖掘了潜在隐藏空间中的特征元素的局部表示特性;最后采用分数傅里叶变换,通过空间-频率表示获得原始光谱与其傅里叶变换的中间域中的特征,进一步增强了背景和异常的光谱区分度,且能有效去除噪声的影响。在Hymap、AVIRIS、ROSIS、HYDICE这4种光谱仪采集的5幅高光谱遥感影像上进行了性能验证,得到的曲线下覆盖面积(area under curve,AUC)分别为0.9905、0.9983、0.9990、0.9928和0.9110,相比于对比算法都有了不同程度的效果提升。结果表明本文方法具有更好的检测精度。

基于改进自编码网络的电力负荷识别

为提高电器设备识别的准确率,提出一种基于改进稀疏自编码网络和支持向量机算法相结合的电力负荷识别方法。以自编码网络的编码部分对输入数据的表征能力为基础,结合卷积神经网络特征提取的能力,采用卷积层替换自编码网络中的全连接层;通过在损失函数中加入惩罚项,进一步优化网络对负荷特征的提取能力;最后将特征放入粒子群优化后的支持向量机模型做识别。试验结果表明,方法能够有效地对电器进行多种类型的识别。相较传统自编码网络,改进后的模型泛化能力更强,识别率更高。

基于多特征提取自编码器的网络流量异常检测算法

随着网络攻击的复杂化、自动化和智能化水平的不断提高,网络中不断涌现出新的攻击类型,给基于特征码的网络攻击检测和及时响应带来极大挑战。为了更加有效准确的识别异常流量,提出一种基于多特征提取自编码器的网络流量异常检测算法。算法自定义基于多特征提取的自编码器模型,由5个不同的Encoder模块构成编码器,1个Decoder模块构成解码器,能够同时提取流量中的空间特征和时间特征,且能有效避免产生退化现象,有效检测异常流量。同时,自定义SMOTE-新样本过采样法来解决数据不均衡问题,使用方差分析进行特征选择,优化数据,降低模型复杂度,大大缩短检测时间,提高算法的检测实时性。实验结果表明,提出的算法在网络流量异常检测的准确率方面比当前同类最优算法提升了1%,对百万条流量数据的检测时间减少了4.22 s。

基于改进深层网络的人脸识别算法

目前的人脸识别算法在其特征提取过程中采用手工设计(hand-crafted)特征或利用深度学习自动提取特征.本文提出一种基于改进深层网络自动提取特征的人脸识别算法,可以更准确地提取出目标的鉴别性特征.算法首先对图像进行ZCA(Zero-mean Component Analysis)白化等预处理,减小特征相关性,降低网络训练复杂度.然后,基于卷积、池化、多层稀疏自动编码器构建深层网络特征提取器.所使用的卷积核是通过单独的无监督学习获得的.此改进的深层网络通过预训练和微调,得到一个自动的深层特征提取器.最后,利用Softmax回归模型对提取的特征进行分类.本文算法在多个常用人脸库上进行了实验,表明了其在性能上比传统方法和普通深度学习方法都有所提高.

一种深度神经网络SAR遮挡目标识别方法

提出了一种利用深度神经网络的合成孔径雷达图像部分遮挡目标的特征提取和目标识别新方法.该方法首先对合成孔径雷达图像进行预处理,然后提取预处理后合成孔径雷达目标的小波域低频子带图像作为训练数据,最后利用深层稀疏编码模型进一步提取合成孔径雷达遮挡目标的有效特征向量作为目标的特征以完成目标识别.采用MSTAR数据库中的3类目标进行目标遮挡模拟及识别实验.结果表明,新方法可以综合利用遮挡目标的局部和整体结构信息以提高目标的正确识别率,是一种有效的合成孔径雷达遮挡目标特征提取和目标识别方法.

基于小波变换和深层稀疏编码的SAR目标识别

针对SAR图像预处理算法自适应能力差、带标签图像不足、目标特征提取困难等问题,提出了一种基于小波变换和深层稀疏编码的SAR图像目标自动识别算法。首先利用灰度值和尺度缩放获得大量的无标签SAR目标,并采用离散小波变换对图像进行高效的降维,再结合深层稀疏编码提取目标的深层抽象特征并完成识别任务。采用MSTAR数据库中3类军事目标进行算法仿真与验证。实验结果表明,在没有预处理的情况下,该算法能够有效地完成多目标SAR图像分类,且具有较高的识别率和鲁棒性。

基于堆叠稀疏自编码的滚动轴承故障诊断

针对机械设备故障数据大容量、多样性的特点,提出一种基于堆叠稀疏自编码(SSAE)的滚动轴承故障智能诊断方法。使用自动编码器(AE)逐层训练网络,从海量数据中自适应地学习各类故障的特征表达,再通过有监督的反向传播算法优化整个网络,最终将特征输入softmax分类器实现滚动轴承健康状况精确诊断。在动力传动故障诊断试验台采集了5类轴承故障数据进行测试。试验结果表明:SSAE算法能够有效地提取故障特征,且故障诊断效果优于传统智能诊断方法。

基于堆叠稀疏自编码的模糊C-均值聚类算法

针对模糊C-均值聚类算法对孤立点、随机初始化的聚类中心比较敏感的问题,将堆叠稀疏自编码与传统模糊C-均值聚类算法相结合,对传统模糊C-均值聚类算法进行了改进。由于堆叠稀疏自编码可以提取原始数据集从低层到高层的特征,而高层的特征通常比原始数据集更能反映待聚类样本的本质特征,用其代替原始数据集进行聚类,有助于提高聚类的效果。利用改进后的算法在UCI的几个标准数据集上进行实验,结果表明改进后的算法是有效可行的。

基于稀疏降噪自编码器的深度置信网络

传统的深度置信网络(DBN)采用随机初始化受限玻尔兹曼机(RBM)的权值和偏置的方法初始化网络。虽然这在一定程度上克服了由BP算法带来的易陷入局部最优和训练时间长的问题,但随机初始化仍然会导致网络重构和原始输入的较大差别,这使得网络无论在准确率还是学习效率上都无法得到进一步提升。针对以上问题,提出一种基于稀疏降噪自编码器(SDAE)的深度网络模型,其核心是稀疏降噪自编码器对数据的特征提取。首先,训练稀疏降噪自编码;然后,用训练后得到的权值和偏置来初始化深度置信网络;最后,训练深度置信网络。在Poker Hand纸牌游戏数据集和MNIST、USPS手写数据集上测试模型性能,在Poker Hand数据集下,方法的误差率比传统的深度置信网络降低46.4%,准确率和召回率依次提升15.56%和14.12%。实验结果表明,所提方法能有效地改善模型性能。

基于栈式降噪稀疏自动编码器的雷达目标识别方法

雷达目标识别中特征提取是关键步骤,所提取特征的好坏决定着识别效果的优劣,但传统特征提取方法很难发掘目标数据深层次本质特征。深度学习理论中的自动编码器模型能够用数据去学习特征,获得数据不同层次的特征表达。同时为消除噪声影响,该文提出一种基于栈式降噪稀疏自动编码器的雷达目标识别方法,通过设置不同隐藏层数和迭代次数,从雷达数据中直接高效地提取识别所需的各层次特征。暗室仿真数据实验结果验证了该方法较K近邻分类方法及传统栈式自编码器有更好的识别效果。

基于深度神经网络的液压泵泄漏状态识别

针对液压信号的高度复杂性以及难以识别的特点,构建了一种基于堆栈稀疏自编码器和Softmax的深度神经网络来对液压泵泄漏状态进行识别。利用小波变换和希尔伯特-黄变换提取液压信号的低维特征,并输入深度神经网络。通过堆栈稀疏自编码器的逐层学习对特征进行优化并提取出高维特征,然后使用Softmax进行识别。实验结果表明,堆栈稀疏自编码器能够有效地提取液压泵泄漏状态的高维特征,构建的深度神经网络可有效地识别液压泵泄漏状态,识别精度达到了97.6%。此外与支持向量机、极限学习机、卷积神经网络以及长短期记忆网络相比,深度神经网络具有更好的识别效果。