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Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The sample balance is crucial for machine learning. The importance of certain classes may be higher than its number on the imbalanced datasets. This paper studies the imbalanced datasets for speech emotion recognition. Firstly, the imbalanced baseline datasets EMODB and IEMOCAP are augmented with different signal-to-noise ratios, and the datasets EMODBM and IEMOCAPM are constructed. Secondly, six techniques namely SMOTE, RandomOverSampler, SMOTEENN, ADASYN, TomekLinks and SMOTETomek are adopted to resample the baseline datasets, and the augmented datasets are constructed to achieve the category balance. Thirdly, 21-dimensional low-level descriptor features are extracted from the baseline datasets and the augmented datasets. Finally, a novel model MA-CapsNet is proposed to validate the effectiveness of the resampling techniques. The results show that all types of emotion samples are basically balanced after resampling, which makes the learning of the model MA-CapsNet fairer. In addition, the model MA-CapsNet has better robustness on the resampling datasets. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With its strong adversary learning ability, generative adversarial networks (GAN) is favored by more and more researchers in many fields. This paper expounds the development background, framework and objective function of GAN, analyzes the causes of pattern collapse and gradient disappearance in the training process, and introduces in detail the GAN derived model proposed through the change of architecture and the modification of objective function. Then, it summarizes some standards used to evaluate the quality and diversity of generated images, and summarizes the wide application of GAN in different fields, Finally, this paper summarizes and puts forward some prospects for the future research direction in this field. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As an important branch of intelligent transportation system, road traffic flow prediction plays an important role in congestion prediction, path planning. The spatiotemporal polymorphism and complex correlation of road traffic flow data force the transformation and upgrading of road traffic flow prediction methods in the era of big data. In order to mine the time-space characteristics of traffic flow, scholars have proposed various methods, including model fusion, model algorithm improvement, data definition conversion, etc, in order to improve the prediction accuracy of the model. In order to reasonably summarize all kinds of traffic flow prediction methods, they are divided into three categories according to the types of methods used: statistics based methods, machine learning based methods, and depth learning based methods. This paper summarizes and analyzes the new models and algorithms in recent years by summarizing various traffic flow prediction methods, aiming to provide research ideas for relevant researchers. Finally, the methods of traffic flow prediction are summarized and prospected, and the exploration direction of the future traffic flow prediction field is given. [ABSTRACT FROM AUTHOR]

In order to further improve the accuracy of text generated by image description, this paper proposes an image description method that combines global-local features and attention mechanism. This method is an improvement on the traditional encoder-decoder model. From an overall perspective, the encoder stage uses the residual network ResNet101 to extract the global and local features of the image to avoid object loss or object prediction errors. In the processor stage, a two-way GRU embedded with an improved attention mechanism is used to generate text sequences. From a local point of view, the attention mechanism proposed by this model is an independent loop structure. The attention weight is obtained by calculating the similarity between the image local feature vector and the semantic vector, and the mapping relationship between image features and semantic information is enhanced. The experimental results on the MSCOCO dataset show that the algorithm in this paper has achieved varying degrees of improvement in evaluation indicators such as BLEU, CIDEr, and METEOR, indicating that the description text generated by this model is highly accurate and rich in details. [ABSTRACT FROM AUTHOR]

High precision segmentation of organs or lesions in medical image is a vital challenge issue for intelligent analysis of medical image, it has important clinical application value for auxiliary diagnosis and treatment of diseases. Recently, in solving challenging problems such as medical image information representation and accurate modeling of non-Euclidean spatial physiological tissue structures, the graph deep learning based medical image segmentation technology has made important breakthroughs, and it has shown significant information feature extraction and characterization advantages. The merged technology also can obtain more accurate segmentation results, which has become an emerging research hotspot in this field. In order to better promote the research and development of the deep learning segmentation algorithm for medical image graphs, this paper makes a systematic summary of the technological progress and application status in this field. The paper introduces the definition of graphs and the basic structure of graph convolutional networks, and elaborates on spectral graph convolution and spatial graph convolution operations. Then, according to the three technical structure modes of GCN combined with residual module, attention mechanism module and learning module, the research progress in medical image segmentation has been encapsulated. The application and development of graph deep learning algorithms based medical image segmentation are summarized and prospected to provide references and guiding principles for the technical development of related researches. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Microsatellites are short strings of repeated sequences scattered throughout the human genome. Microsatellite Instability (MSI) is a phenomenon in which the length of microsatellites changes due to the insertion or deletion of repeated units in tumor tissues. MSI type gastric cancer often has unique molecular phenotypes and clinicopathological characteris- tics, and the instability of microsatellites determines whether gastric cancer patients respond well to immunotherapy. Therefore, preoperative detection of MSI status is of great significance for the formulation of treatment plans for gastric cancer patients. Traditional MSI detection methods require immunohistochemistry and genetic analysis, which not only require additional costs, but also are difficult to be extended to every patient in clinical practice. In this paper, image feature extraction technology and machine learning algorithm are applied to quantitative analysis of high-resolution histopathological images of gastric cancer patients to predict the MSI status of gastric cancer patients. The original data of 279 cases are obtained from the TCGA database. After pre-processing and up-sampling, 442 samples are obtained, and 445 quantitative image features are extracted from the histopathological images of each sample, including the first-order statistics, texture features and small wave characteristics of the images. Lasso regression is used to screen features and construct predictive labels (Risk-score) of gastric cancer MSI status, and the performance of predictive labels is verified through logistics classification model. Then, multivariate analysis is carried out in combination with the clinical characteristics of each patient, and personalized train diagram is constructed for MSI status prediction. The experimental results show that the prediction performance AUC value of the prediction label based on histone image texture features is 0.74, and the AUC value of the existing MSI prediction model based on histone image texture features is 0.73. Based on all samples, the AUC value of the MSI prediction model constructed by combining clinical features and Risk- score is 0.802, while the AUC value of the existing MSI prediction model combining clinical features and image features is only 0.752, compared with the existing methods, the MSI prediction model proposed in this paper has better prediction performance and can provide more valuable reference information for the clinical decision-making of gastric cancer patients. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Text semantic representation is one of the most difficulty problems in natural language processing and machine learning. To solve the problem of semantic loss in text representation, this paper proposes a new text semantic representation method named Sem2vec (semantic to vector) model which is based on the LDA topic model and the Word2vec model. The topic similarity is calculated according to the word topic distribution obtained by the LDA model. Then the topic semantic word vectors are inputted into the Sem2vec model instead of the one-hot vector. Constrained by maximizing log-likelihood objective function, the parameters of the Sem2vec model are optimized. Finally, the semantic word vectors are learned by the Sem2vec model and the semantic representation of the text is further obtained. The experimental results on different datasets show that compared with the other classic models, the Sem2vec model is more accurate in calculating semantic similarity between words. Moreover, in different classification algorithms, the text semantic vectors generated by the Sem2vec model can improve the text classification results by 0.58%~3.5% and promote the time performance compared with the other classic models. [ABSTRACT FROM AUTHOR]

The prediction of stock price has a great significance for the regulators to find out the operation of the financial market and for the investors to avoid the risk of stock market. This paper proposes a method based on gated recurrent unit (GRU) neural network and bagging, and applies it to the research of forecasts of stock indexes. The model uses Bagging method to process the training data set, introduces randomness in the model construction process, and combines the GRU model to predict stock prices, which can ultimately reduce prediction errors and improve prediction accuracy. Through the comparison of the experimental results of the fourdata sets, it is found that: (1) The GRU model can better predict stock index data. Compared with the other two single models, it has smaller forecast errors in most cases. (2) The Bagging-GRU model has a strong predictive ability. Compared with the three benchmark models (GRU, ELM, BP), it has smaller prediction errors and higher prediction stability. [ABSTRACT FROM AUTHOR]

Visual attention model has been applied to image recognition tasks which autolocate discriminative local part of fine-grained image to capture different features, but input image size is fixed and the size of discriminative part is uncertain, so model cannot capture all features of image precisely and the classification accuracy is reduced. This paper proposes a variable size recurrent attention network (VSRAM), different from previous fixed input size, recurrent attention network (RAM), the VSRAM optimizes attention policy and size sampling policy to learn the position and size for next input image by itself, reduces total input image areas and increases processing speed. Experimental results show that, dynamically adjusting the size of input image can achieve the same recognition accuracy as RAM, but efficiently reduce the total input image area and increase speed. [ABSTRACT FROM AUTHOR]

With the advent of the 5G era and the increasing public awareness of the Internet, the public has paid more and more attention to the protection of personal privacy. Due to malicious communication in the process of data encryption, to ensure data security and safeguard social and national interests, the research work on encrypted traffic identification is particularly important. Therefore, this paper describes the TLS traffic in detail and analyzes the improved technology of early identification method, including common traffic detection technology, DPI detection technology, proxy technology, and certificate detection technology. It also introduces machine learning models for selecting different TLS encrypted traffic characteristics, as well as many recent research results of deep learning models without feature selection. The deficiencies of the related research work are summarized, and the future research work and development trend of the technology have been prospected. [ABSTRACT FROM AUTHOR]

The high rate of infection has led to the rapid spread of COVID-19 across the globe, and the commonly used reverse transcription-polymerase reaction (RT-PCR) test has drawbacks such as time consuming, high false-negative rates, and inadequate medical equipment. Therefore, the development of efficient, accurate, and low-cost test techniques is critical for the diagnosis and treatment of COVID-19. With the successful application of Artificial intelligence in medicine, deep learning technology has become an effective method to assist detection and identification of COVID-19. This paper studies and summarizes the emerging deep learning diagnostic methods for COVID-19 in recent years. Two COVID-19 data sets used by deep learning methods are introduced. Six deep learning diagnostic methods based on VGGNet, Inception, ResNet, DenseNet, EfficientNet and CapsNet models are introduced. Three diagnostic methods combining deep learning with other machine learning methods are introduced. The research trend of COVID-19 diagnosis based on deep learning is prospected. [ABSTRACT FROM AUTHOR]