基于一维卷积神经网络的房颤智能诊断方法研究

针对“大数据”时代如何利用数据对房颤进行智能、高效的诊断问题,提出了基于一维卷积神经网络的智能诊断方法,以避免传统算法依赖人工特征提取和先验知识的问题。首先,分别构建一维LeNet-5和AlexNet神经网络模型,合理设置网络结构参数;然后,在采集的实验数据基础上针对心电信号的特点进行一系列的数据处理,随机构建训练样本和测试样本;最后,将训练样本分别输入上述2个神经网络模型中训练学习,再将训练好的模型用于房颤的诊断。实验结果表明:一维LeNet-5网络模型存在“过拟合”现象,而一维AlexNet网络模型在避免了上述现象的同时,诊断精度达到了95.34%,较传统方法有了较大提升,为房颤诊断提供了有效的手段。     

Deep Transfer Learning Based Detection and Classification of Citrus Plant Diseases

Citrus fruit crops are among the world’s most important agricultural products, but pests and diseases impact their cultivation, resulting in yield and quality losses. Computer vision and machine learning have been widely used to detect and classify plant diseases over the last decade, allowing for early disease detection and improving agricultural production. This paper presented an automatic system for the early detection and classification of citrus plant diseases based on a deep learning (DL) model, which improved accuracy while decreasing computational complexity. The most recent transfer learning-based models were applied to the Citrus Plant Dataset to improve classification accuracy. Using transfer learning, this study successfully proposed a Convolutional Neural Network (CNN)-based pre-trained model (EfficientNetB3, ResNet50, MobiNetV2, and InceptionV3) for the identification and categorization of citrus plant diseases. To evaluate the architecture’s performance, this study discovered that transferring an EfficientNetb3 model resulted in the highest training, validating, and testing accuracies, which were 99.43%, 99.48%, and 99.58%, respectively. In identifying and categorizing citrus plant diseases, the proposed CNN model outperforms other cutting-edge CNN model architectures developed previously in the literature.     

A serialized classification method for pulmonary nodules based on lightweight cascaded convolutional neural network-long short-term memory

Computer Assisted Diagnosis (CAD) is an effective method to detect lung cancer from computed tomography (CT) scans. The development of artificial neural network makes CAD more accurate in detecting pathological changes. Due to the complexity of the lung environment, the existing neural network training still requires large datasets, excessive time, and memory space. To meet the challenge, we analysis 3D volumes as serialized 2D slices and present a new neural network structure lightweight convolutional neural network (CNN)-long short-term memory (LSTM) for lung nodule classification. Our network contains two main components: (a) optimized lightweight CNN layers with tiny parameter space for extracting visual features of serialized 2D images, and (b) LSTM network for learning relevant information among 2D images. In all experiments, we compared the training results of several models and our model achieved an accuracy of 91.78% for lung nodule classification with an AUC of 93%. We used fewer samples and memory space to train the model, and we achieved faster convergence. Finally, we analyzed and discussed the feasibility of migrating this framework to mobile devices. The framework can also be applied to cope with the small amount of training data and the development of mobile health device in future.     

An Efficient Internet Traffic Classification System Using Deep Learning for IoT

Internet of Things (IoT) defines a network of devices connected to the internet and sharing a massive amount of data between each other and a central location. These IoT devices are connected to a network therefore prone to attacks. Various management tasks and network operations such as security, intrusion detection, Quality-of-Service provisioning, performance monitoring, resource provisioning, and traffic engineering require traffic classification. Due to the ineffectiveness of traditional classification schemes, such as port-based and payload-based methods, researchers proposed machine learning-based traffic classification systems based on shallow neural networks. Furthermore, machine learning-based models incline to misclassify internet traffic due to improper feature selection. In this research, an efficient multilayer deep learning based classification system is presented to overcome these challenges that can classify internet traffic. To examine the performance of the proposed technique, Moore-dataset is used for training the classifier. The proposed scheme takes the pre-processed data and extracts the flow features using a deep neural network (DNN). In particular, the maximum entropy classifier is used to classify the internet traffic. The experimental results show that the proposed hybrid deep learning algorithm is effective and achieved high accuracy for internet traffic classification, i.e., 99.23%. Furthermore, the proposed algorithm achieved the highest accuracy compared to the support vector machine (SVM) based classification technique and k-nearest neighbours (KNNs) based classification technique.     

基于CNN-MSLSTM的航空发动机滑油监测方法研究

针对传统的数据特征提取方法难以提取ECT滑油监测数据有效特征的缺陷,提出了一种基于卷积神经网络(convolutional neural network,CNN)和多尺度长短期记忆(multi-scales long short-term memory,MSLSTM)神经网络的双通道网络模型CNN-MSLSTM.将多尺...     

An Optimized Approach to Vehicle-Type Classification Using a Convolutional Neural Network

Vehicle type classification is considered a central part of an intelligent traffic system. In recent years, deep learning had a vital role in object detection in many computer vision tasks. To learn high-level deep features and semantics, deep learning offers powerful tools to address problems in traditional architectures of handcrafted feature-extraction techniques. Unlike other algorithms using handcrated visual features, convolutional neural network is able to automatically learn good features of vehicle type classification. This study develops an optimized automatic surveillance and auditing system to detect and classify vehicles of different categories. Transfer learning is used to quickly learn the features by recording a small number of training images from vehicle frontal view images. The proposed system employs extensive data-augmentation techniques for effective training while avoiding the problem of data shortage. In order to capture rich and discriminative information of vehicles, the convolutional neural network is fine-tuned for the classification of vehicle types using the augmented data. The network extracts the feature maps from the entire dataset and generates a label for each object (vehicle) in an image, which can help in vehicle-type detection and classification. Experimental results on a public dataset and our own dataset demonstrated that the proposed method is quite effective in detection and classification of different types of vehicles. The experimental results show that the proposed model achieves 96.04% accuracy on vehicle type classification.     

基于深度学习的短纤维增强聚氨酯复合材料性能预测基于深度学习的短纤维增强聚氨酯复合材料性能预测

结合深度学习在图像识别领域的优势,将卷积神经网络（CNN）应用于有限元代理模型,预测了平面随机分布短纤维增强聚氨酯复合材料的有效弹性参数,并针对训练过程出现的过拟合,提出了一种数据增强的方法。为验证该代理模型的有效性,比较了其与传统代理模型在预测有效杨氏模量和剪切模量上的精度差异。在此基础上结合蒙特卡洛法利用卷积神经网络代理模型研究了材料微几何参数不确定性的误差正向传递。结果表明:相对于传统代理模型,卷积神经网络模型能更好地学习图像样本的内部特征,得到更加精确的预测结果,并在训练样本空间外的一定范围内可以保持较好的鲁棒性;随着纤维长宽比的增大,微几何参数的不确定性对材料有效性能预测结果会传递较大的误差。      

Improved VGG Model for Road Traffic Sign Recognition

Road traffic sign recognition is an important task in intelligent transportation system. Convolutional neural networks (CNNs) have achieved a breakthrough in computer vision tasks and made great success in traffic sign classification. In this paper, it presents a road traffic sign recognition algorithm based on a convolutional neural network. In natural scenes, traffic signs are disturbed by factors such as illumination, occlusion, missing and deformation, and the accuracy of recognition decreases, this paper proposes a model called Improved VGG (IVGG) inspired by VGG model. The IVGG model includes 9 layers, compared with the original VGG model, it is added max-pooling operation and dropout operation after multiple convolutional layers, to catch the main features and save the training time. The paper proposes the method which adds dropout and Batch Normalization (BN) operations after each fully-connected layer, to further accelerate the model convergence, and then it can get better classification effect. It uses the German Traffic Sign Recognition Benchmark (GTSRB) dataset in the experiment. The IVGG model enhances the recognition rate of traffic signs and robustness by using the data augmentation and transfer learning, and the spent time is also reduced greatly.     

Visualization for Explanation of Deep Learning-Based Defect Detection Model Using Class Activation Map

Recently, convolutional neural network (CNN)-based visual inspection has been developed to detect defects on building surfaces automatically. The CNN model demonstrates remarkable accuracy in image data analysis; however, the predicted results have uncertainty in providing accurate information to users because of the “black box” problem in the deep learning model. Therefore, this study proposes a visual explanation method to overcome the uncertainty limitation of CNN-based defect identification. The visual representative gradient-weights class activation mapping (Grad-CAM) method is adopted to provide visually explainable information. A visualizing evaluation index is proposed to quantitatively analyze visual representations; this index reflects a rough estimate of the concordance rate between the visualized heat map and intended defects. In addition, an ablation study, adopting three-branch combinations with the VGG16, is implemented to identify performance variations by visualizing predicted results. Experiments reveal that the proposed model, combined with hybrid pooling, batch normalization, and multi-attention modules, achieves the best performance with an accuracy of 97.77%, corresponding to an improvement of 2.49% compared with the baseline model. Consequently, this study demonstrates that reliable results from an automatic defect classification model can be provided to an inspector through the visual representation of the predicted results using CNN models.     

A Hybrid Model Using Bio-Inspired Metaheuristic Algorithms for Network Intrusion Detection System

Network Intrusion Detection System (IDS) aims to maintain computer network security by detecting several forms of attacks and unauthorized uses of applications which often can not be detected by firewalls. The features selection approach plays an important role in constructing effective network IDS. Various bio-inspired metaheuristic algorithms used to reduce features to classify network traffic as abnormal or normal traffic within a shorter duration and showing more accuracy. Therefore, this paper aims to propose a hybrid model for network IDS based on hybridization bio-inspired metaheuristic algorithms to detect the generic attack. The proposed model has two objectives; The first one is to reduce the number of selected features for Network IDS. This objective was met through the hybridization of bio-inspired metaheuristic algorithms with each other in a hybrid model. The algorithms used in this paper are particle swarm optimization (PSO), multi-verse optimizer (MVO), grey wolf optimizer (GWO), moth-flame optimization (MFO), whale optimization algorithm (WOA), firefly algorithm (FFA), and bat algorithm (BAT). The second objective is to detect the generic attack using machine learning classifiers. This objective was met through employing the support vector machine (SVM), C4.5 (J48) decision tree, and random forest (RF) classifiers. UNSW-NB15 dataset used for assessing the effectiveness of the proposed hybrid model. UNSW-NB15 dataset has nine attacks type. The generic attack is the highest among them. Therefore, the proposed model aims to identify generic attacks. My data showed that J48 is the best classifier compared to SVM and RF for the time needed to build the model. In terms of features reduction for the classification, my data show that the MFO-WOA and FFA-GWO models reduce the features to 15 features with close accuracy, sensitivity and F-measure of all features, whereas MVO-BAT model reduces features to 24 features with the same accuracy, sensitivity and F-measure of all features for all classifiers.     

A deep learning model integrating convolution neural network and multiple kernel K means clustering for segmenting brain tumor in magnetic resonance images

In medical imaging, segmenting brain tumor becomes a vital task, and it provides a way for early diagnosis and treatment. Manual segmentation of brain tumor in magnetic resonance (MR) images is a time‐consuming and challenging task. Hence, there is a need for a computer‐aided brain tumor segmentation approach. Using deep learning algorithms, a robust brain tumor segmentation approach is implemented by integrating convolution neural network (CNN) and multiple kernel K means clustering (MKKMC). In this proposed CNN‐MKKMC approach, classification of MR images into normal and abnormal is performed by CNN algorithm. At next, MKKMC algorithm is employed to segment the brain tumor from the abnormal brain image. The proposed CNN‐MKKMC algorithm is evaluated both visually and objectively in terms of accuracy, sensitivity, and specificity with the existing segmentation methods. The experimental results demonstrate that the proposed CNN‐MKKMC approach yields better accuracy in segmenting brain tumor with less time cost.     

A hybrid convolutional neural network model to detect COVID-19 and pneumonia using chest X-ray images

A hybrid convolutional neural network (CNN)-based model is proposed in the article for accurate detection of COVID-19, pneumonia, and normal patients using chest X-ray images. The input images are first pre-processed to tackle problems associated with the formation of the dataset from different sources, image quality issues, and imbalances in the dataset. The literature suggests that several abnormalities can be found with limited medical image datasets by using transfer learning. Hence, various pre-trained CNN models: VGG-19, InceptionV3, MobileNetV2, and DenseNet are adopted in the present work. Finally, with the help of these models, four hybrid models: VID (VGG-19, Inception, and DenseNet), VMI(VGG-19, MobileNet, and Inception), VMD (VGG-19, MobileNet, and DenseNet), and IMD(Inception, MobileNet, and DenseNet) are proposed. The model outcome is also tested using five-fold cross-validation. The best-performing hybrid model is the VMD model with an overall testing accuracy of 97.3%. Thus, a new hybrid model architecture is presented in the work that combines three individual base CNN models in a parallel configuration to counterbalance the shortcomings of individual models. The experimentation result reveals that the proposed hybrid model outperforms most of the previously suggested models. This model can also be used in the identification of diseases, especially in rural areas where limited laboratory facilities are available.     

User Behavior Traffic Analysis Using a Simplified Memory-Prediction Framework

As nearly half of the incidents in enterprise security have been triggered by insiders, it is important to deploy a more intelligent defense system to assist enterprises in pinpointing and resolving the incidents caused by insiders or malicious software (malware) in real-time. Failing to do so may cause a serious loss of reputation as well as business. At the same time, modern network traffic has dynamic patterns, high complexity, and large volumes that make it more difficult to detect malware early. The ability to learn tasks sequentially is crucial to the development of artificial intelligence. Existing neurogenetic computation models with deep-learning techniques are able to detect complex patterns; however, the models have limitations, including catastrophic forgetfulness, and require intensive computational resources. As defense systems using deep-learning models require more time to learn new traffic patterns, they cannot perform fully online (on-the-fly) learning. Hence, an intelligent attack/malware detection system with on-the-fly learning capability is required. For this paper, a memory-prediction framework was adopted, and a simplified single cell assembled sequential hierarchical memory (s.SCASHM) model instead of the hierarchical temporal memory (HTM) model is proposed to speed up learning convergence to achieve on-the-fly learning. The s.SCASHM consists of a Single Neuronal Cell (SNC) model and a simplified Sequential Hierarchical Superset (SHS) platform. The s.SCASHM is implemented as the prediction engine of a user behavior analysis tool to detect insider attacks/anomalies. The experimental results show that the proposed memory model can predict users’ traffic behavior with accuracy level ranging from 72% to 83% while performing on-the-fly learning.     

利用拼音特征的深度学习文本分类模型

针对人-机器人语音交互中经过语音识别的文本指令,提出了一种利用汉语拼音中声韵母作为特征的深度学习文本分类模型。首先,以无人驾驶车语音导航控制为人机交互的应用背景,分析其文本指令结构并分别构建单一意图与复杂意图语料库;其次,在以字符作为文本分类特征的基础上,结合汉语拼音与英文单词的区别,提出了一种利用拼音声韵母字符作为中文文本分类的特征表示方法;然后,用门控递归单元(GRU)代替传统递归神经网络单元以解决其难以捕获长时间维度特征的不足,为提取信息的高阶特征、缩短特征序列长度并加快模型收敛速度,建立了一种结合卷积神经网络及GRU递归神经网络的深度学习文本分类模型。最后,为验证模型在处理长、短序列任务上的表现,在上述两个语料库上对提出的模型分别进行十折交叉测试,并与其他分类方法进行比较与分析,结果表明该模型显著地提高了分类准确率。     

Optimized Convolutional Neural Network Models for Skin Lesion Classification

Skin cancer is one of the most severe diseases, and medical imaging is among the main tools for cancer diagnosis. The images provide information on the evolutionary stage, size, and location of tumor lesions. This paper focuses on the classification of skin lesion images considering a framework of four experiments to analyze the classification performance of Convolutional Neural Networks (CNNs) in distinguishing different skin lesions. The CNNs are based on transfer learning, taking advantage of ImageNet weights. Accordingly, in each experiment, different workflow stages are tested, including data augmentation and fine-tuning optimization. Three CNN models based on DenseNet-201, Inception-ResNet-V2, and Inception-V3 are proposed and compared using the HAM10000 dataset. The results obtained by the three models demonstrate accuracies of 98%, 97%, and 96%, respectively. Finally, the best model is tested on the ISIC 2019 dataset showing an accuracy of 93%. The proposed methodology using CNN represents a helpful tool to accurately diagnose skin cancer disease.     

Traffic Sign Recognition Method Integrating Multi-Layer Features and Kernel Extreme Learning Machine Classifier

Traffic sign recognition (TSR), as a critical task to automated driving and driver assistance systems, is challenging due to the color fading, motion blur, and occlusion. Traditional methods based on convolutional neural network (CNN) only use an end-layer feature as the input to TSR that requires massive data for network training. The computation-intensive network training process results in an inaccurate or delayed classification. Thereby, the current state-of-the-art methods have limited applications. This paper proposes a new TSR method integrating multi-layer feature and kernel extreme learning machine (ELM) classifier. The proposed method applies CNN to extract the multi-layer features of traffic signs, which can present sufficient details and semantically abstract information of multi-layer feature maps. The extraction of multi-scale features of traffic signs is effective against object scale variation by applying a new multi-scale pooling operation. Further, the extracted features are combined into a multi-scale multi-attribute vector, which can enhance the feature presentation ability for TSR. To efficiently handle nonlinear sampling problems in TSR, the kernel ELM classifier is adopted for efficient TSR. The kernel ELM has a more powerful function approximation capability, which can achieve an optimal and generalized solution for multiclass TSR. Experimental results demonstrate that the proposed method can improve the recognition accuracy, efficiency, and adaptivity to complex travel environments in TSR.     

红外光谱结合主成分分析对纸质快递文件袋的分类研究

目的 建立一种快速无损的检验纸质快递文件袋的分析方法。方法 利用傅里叶变换红外光谱对63个纸质快递文件袋样品进行检验，分析样品的红外光谱吸收峰的峰位，结合主成分分析对光谱数据进行了降维处理并分类。利用费歇尔判别对快递文件袋的分类结果进行分析和验证。同时建立多层感知器神经网络和径向基函数神经网络2种分类模型，进行分析和验证。结果 63个纸质快递文件袋样品可被分成四大类，利用费歇尔分类模型进行验证，准确率为100%；多层感知器神经网络分类模型准确率为95.23%，径向基函数神经网络分类模型准确率为92.06%。通过比较发现，费歇尔判别可以实现对纸质快递文件袋更加有效地分类。结论 该方法简单快速，样品用量少且无损样品，可为快递文件袋类的物证鉴定提供科学依据。     

Acoustic Emission Recognition Based on a Two-Streams Convolutional Neural Network

The Convolutional Neural Network (CNN) is a widely used deep neural network. Compared with the shallow neural network, the CNN network has better performance and faster computing in some image recognition tasks. It can effectively avoid the problem that network training falls into local extremes. At present, CNN has been applied in many different fields, including fault diagnosis, and it has improved the level and efficiency of fault diagnosis. In this paper, a two-streams convolutional neural network (TCNN) model is proposed. Based on the short-time Fourier transform (STFT) spectral and Mel Frequency Cepstrum Coefficient (MFCC) input characteristics of two-streams acoustic emission (AE) signals, an AE signal processing and classification system is constructed and compared with the traditional recognition methods of AE signals and traditional CNN networks. The experimental results illustrate the effectiveness of the proposed model. Compared with single-stream convolutional neural network and a simple Long Short-Term Memory (LSTM) network, the performance of TCNN which combines spatial and temporal features is greatly improved, and the accuracy rate can reach 100% on the current database, which is 12% higher than that of single-stream neural network.     

Development of automatic glioma brain tumor detection system using deep convolutional neural networks

We have developed six convolutional neural network (CNN) models for finding optimal brain tumor detection system on high-grade glioma and low-grade glioma lesions from voluminous magnetic resonance imaging human brain scans. Glioma is the most common form of brain tumor. The models are constructed based on the different combinations and settings of hyperparameters with conventional CNN architecture. The six models are two layers with five epochs, five layers with dropout, five layers with stopping criteria (FLSC), FLSC and dropout (FLSCD), FLSC and batch normalization (FLSCBN), and FLSCBN and dropout. The models were trained and tested with BraTS2013 and whole brain atlas data sets. Among them, FLSCBN model yielded the best classification results for brain tumor detection. Experimental results revealed that our deep learning approach was better than the conventional state-of-art methods.     

Neural Networks in Applied Statistics

Artificial neural networks are computer algorithms or computer programs derived in part from attempts to model the activity of nerve cells. They have been applied to pattern recognition, classification, and optimization problems in the physical and chemical sciences, as well as in other fields. We introduce the principles of the multilayer feedforward network that is among the most commonly used neural networks in practical problems. The relevance of neural network models for the applied statistician is considered using a time series prediction problem as an example. The multilayer feedforward neural network uses a nonlinear function of the predictors to obtain predictions for future time series values. We illustrate the considerations involved in specifying a neural network model and evaluate the accuracy of neural network models relative to the accuracy obtained using other computer-intensive, nonmodel-based techniques.