Malicious Code Detection under 5G HetNets Based on a Multi-Objective RBM Model

With the rapid growth of the number of Web services on the Internet, how to classify Web services correctly and efficiently become particularly important in service management tasks, such as service discovery, service selection, service ranking, and service recommendation. Existing functionality-based service classification techniques have some drawbacks: (1) the keyword order and context information are not considered; (2) the embedding features of keywords are taken as equal importance to learn the classification model; (3) the topic number is hard to determine manually. Due to these drawbacks, the accuracy of service classification needs to be improved further. At present, deep learning techniques show the strong power in modeling complex and nonlinear function relationship. Thus, to address the problems above, this paper exploits attention mechanism to combine the local implicit state vector of Bidirectional Long Short-Term Memory Network (BiLSTM) and the global hierarchical Dirichlet process (HDP) topic vector, and proposes a Web service classification approach with topical attention-based BiLSTM. Specifically, BiLSTM is used to automatically learn the keyword feature representations of Web services. Then, the topic vectors of Web service documents are obtained with HDP by offline training, and topic attention mechanism is adopted to strengthen the feature representation by discriminating the importance or weight of different keywords in Web service documents. Finally, the enhanced Web service feature representation is used as the input of a softmax neural network layer to perform the classification prediction for Web services. Extensive experiments are conducted to validate the effectiveness of the proposed approach.

Section: Related Workmentioning

confidence: 99%

Tatt‐BiLSTM: Web service classification with topical attention‐based BiLSTM

Kang

Xiao

Liu

et al. 2021

“…Many‐objective particle swarm optimization methods are proposed for the green coal production problem 11,12 . Multiobjective restricted Boltzmann machine network is designed for malicious code detection under 5G HetNets 13 . Cui et al 14 deploy subspace clustering strategy for data analysis on edge servers.…”

Section: Introductionmentioning

confidence: 99%

“…11,12 Multiobjective restricted Boltzmann machine network is designed for malicious code detection under 5G HetNets. 13 Cui et al 14 deploy subspace clustering strategy for data analysis on edge servers. In stock market, machine learning methods are also applied to model the relationship between the features from technical analysis and future stock trend, 15 such as random forest, artificial neural networks (ANNs), support vector machine (SVM), 16,17 logistic regression, and Naïve Bayes.…”

mentioning

confidence: 99%

Unsupervised feature extraction with convolutional autoencoder with application to daily stock market prediction

Xie

2021

Due to the volatility and noise of the stock market, accurately obtaining the trend of the stock market is a challenging problem, and gets the attention of many researchers and speculators. Recently, convolutional neural network (CNN) has been used to automatically learn effective features and predict stock market trends. In CNN-based methods reported so far, less focus has been paid to time series information of the stock, but is very crucial for stock forecasting. In this study, an unsupervised feature extraction method with convolutional autoencoder (CAE) with application to daily stock market prediction is proposed, which has a higher prediction than traditional models.The proposed method mainly consists of the data processing part, unsupervised feature learning part, and the support vector machine model part. Data processing part includes time series data transform into two-dimensional data and data normalization. CAE network-based unsupervised feature learning is designed by fusing convolution and autoencoder. In order to verify the performance of the model, various initial financial and economic variables of stock indices are chosen for prediction experiments. The experimental results on different stock indices demonstrate a significant improvement in prediction's performance compared with the baseline methods. K E Y W O R D S convolutional autoencoder network, convolutional neural network, stock market prediction, support vector machine INTRODUCTIONThe stock market has become an important component for listed companies to raise funds from investors. However, the law of stock price change is difficult to accurately grasp, and most investors usually depend on subjective judgment to conduct stock trading. 1,2 The stock market has always been affected by political factors, the industry specific factors, the world economic situation, and the price trends are highly nonlinear and nonstationary. [3][4][5] Therefore, effective and efficient predicting the stock is an extremely challenging task for both investors and researchers. And thus, it is significant to research how to build a universal and effective model to forecast future movements of the stock market.There are a number of advanced methods that are applied for prediction of the stock market. Depending on the relationship between the historical behavior and future trend of the stock price is processed, forecasting methods can be divided into three categories. [6][7][8] Among statistical methodologies, due to the interpretability, linear regression, autoregression, and moving average are helpful in financial time series forecasting. Assume that the stock price trend is a result of historical behaviors, extracting effective features from the original stock data is the key of the prediction process. However, features are subjectively designed, and the accurate prediction of models mostly depended on the A preliminary version of this article has been published by the seventh International Conference on Information Science and Control Engineering.

“…Along with the development of deep learning-based methods, [16][17][18][19] the general object detection methods also achieved remarkable developments in both detection accuracy and efficiency, such as the Faster R-CNN, 20 SSD, 21 and YOLO. In this study, we regard the recognition of tooth-marked tongue and cracked tongue as an object detection task, which simultaneously identifies the tooth-marked tongue and cracked tongue and locates the position of the tooth-marks and cracks in the tongue images.…”

Section: Introductionmentioning

confidence: 99%

A weakly supervised tooth‐mark and crack detection method in tongue image

Hui

Lei

et al. 2021

Tongue diagnosis is one of the primary clinical diagnostic methods in Traditional Chinese Medicine. Recognizing the tooth-marked tongue and the crackled tongue plays an essential role in evaluating the status of patients. Previous methods mainly focus on identifying whether a tongue image is a tooth-marked tongue (cracked tongue)or not, while cannot provide more details. In this study, we propose a weakly supervised method for training the tooth-mark and crack detection model by leveraging fully bounding-box level annotated and coarse image-level annotated tongue images. The proposed model is extended from the YOLO object detection model, and we add several classification branches for recognizing the tooth-marked tongue and cracked tongue.The classification branch aims to predict the coarse label for both coarse-labeled data and fully annotated data. The detection branch is used to locate the position of tooth marks and cracks from the fully annotated data. Finally, we utilize a multitask loss function for training the model. Experimental results on a challenging tongue image dataset demonstrate the effectiveness of our proposed weakly supervised method.