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  • Open Access


    A Novel Intrusion Detection Algorithm Based on Long Short Term Memory Network

    Xinda Hao1, Jianmin Zhou2,*, Xueqi Shen1, Yu Yang1

    Journal of Quantum Computing, Vol.2, No.2, pp. 97-104, 2020, DOI:10.32604/jqc.2020.010819

    Abstract In recent years, machine learning technology has been widely used for timely network attack detection and classification. However, due to the large number of network traffic and the complex and variable nature of malicious attacks, many challenges have arisen in the field of network intrusion detection. Aiming at the problem that massive and high-dimensional data in cloud computing networks will have a negative impact on anomaly detection, this paper proposes a Bi-LSTM method based on attention mechanism, which learns by transmitting IDS data to multiple hidden layers. Abstract information and high-dimensional feature representation in network data messages are used to… More >

  • Open Access


    Workload Allocation Based on User Mobility in Mobile Edge Computing

    Tengfei Yang1,2, Xiaojun Shi3, Yangyang Li1,*, Binbin Huang4, Haiyong Xie1,5, Yanting Shen4

    Journal on Big Data, Vol.2, No.3, pp. 105-115, 2020, DOI:10.32604/jbd.2020.010958

    Abstract Mobile Edge Computing (MEC) has become the most possible network architecture to realize the vision of interconnection of all things. By offloading compute-intensive or latency-sensitive applications to nearby small cell base stations (sBSs), the execution latency and device power consumption can be reduced on resource-constrained mobile devices. However, computation delay of Mobile Edge Network (MEN) tasks are neglected while the unloading decision-making is studied in depth. In this paper, we propose a workload allocation scheme which combines the task allocation optimization of mobile edge network with the actual user behavior activities to predict the task allocation of single user. We… More >

  • Open Access


    Short-Term Traffic Flow Prediction Based on LSTM-XGBoost Combination Model

    Xijun Zhang*, Qirui Zhang

    CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.1, pp. 95-109, 2020, DOI:10.32604/cmes.2020.011013

    Abstract According to the time series characteristics of the trajectory history data, we predicted and analyzed the traffic flow. This paper proposed a LSTMXGBoost model based urban road short-term traffic flow prediction in order to analyze and solve the problems of periodicity, stationary and abnormality of time series. It can improve the traffic flow prediction effect, achieve efficient traffic guidance and traffic control. The model combined the characteristics of LSTM (Long Short-Term Memory) network and XGBoost (Extreme Gradient Boosting) algorithms. First, we used the LSTM model that increases dropout layer to train the data set after preprocessing. Second, we replaced the… More >

  • Open Access


    Research on Prediction Methods of Energy Consumption Data

    Ning Chen1, Naernaer Xialihaer2,3, Weiliang Kong3, Jiping Ren2,3,*

    Journal of New Media, Vol.2, No.3, pp. 99-109, 2020, DOI:10.32604/jnm.2020.09889

    Abstract This paper analyzes the energy consumption situation in Beijing, based on the comparison of common energy consumption prediction methods. Here we use multiple linear regression analysis, grey prediction, BP neural net-work prediction, grey BP neural network prediction combined method, LSTM long-term and short-term memory network model prediction method. Firstly, before constructing the model, the whole model is explained theoretically. The advantages and disadvantages of each model are analyzed before the modeling, and the corresponding advantages and disadvantages of these models are pointed out. Finally, these models are used to construct the Beijing energy forecasting model, and some years are selected… More >

  • Open Access


    LSTM Neural Network for Beat Classification in ECG Identity Recognition

    Xin Liu1,*, Yujuan Si1,2, Di Wang1

    Intelligent Automation & Soft Computing, Vol.26, No.2, pp. 341-351, 2020, DOI:10.31209/2019.100000104

    Abstract As a biological signal existing in the human living body, the electrocardiogram (ECG) contains abundantly personal information and fulfils the basic characteristics of identity recognition. It has been widely used in the field of individual identification research in recent years. The common process of identity recognition includes three steps: ECG signals preprocessing, feature extraction and processing, beat classification recognition. However, the existing ECG classification models are sensitive to limitations of database type and extracted features dimension, which makes classification accuracy difficult to improve and cannot meet the needs of practical applications. To tackle the problem, this paper proposes to build… More >

  • Open Access


    Applying Stack Bidirectional LSTM Model to Intrusion Detection

    Ziyong Ran1, Desheng Zheng1, *, Yanling Lai1, Lulu Tian2

    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 309-320, 2020, DOI:10.32604/cmc.2020.010102

    Abstract Nowadays, Internet has become an indispensable part of daily life and is used in many fields. Due to the large amount of Internet traffic, computers are subject to various security threats, which may cause serious economic losses and even endanger national security. It is hoped that an effective security method can systematically classify intrusion data in order to avoid leakage of important data or misuse of data. As machine learning technology matures, deep learning is widely used in various industries. Combining deep learning with network security and intrusion detection is the current trend. In this paper, the problem of data… More >

  • Open Access


    Investigation of Granite Deformation Process under Axial Load Using LSTM-Based Architectures

    Yalei Wang1, Jinming Xu1,2,*, Mostafa Asadizadeh3, Chuanjiang Zhong4, Xuejie Tao1

    CMES-Computer Modeling in Engineering & Sciences, Vol.124, No.2, pp. 643-664, 2020, DOI:10.32604/cmes.2020.09866

    Abstract Granite is generally composed of quartz, biotite, feldspar, and cracks. The changes in digital parameters of these compositions reflect the detail of the deformation process of the rock. Therefore, the estimation of the changes in digital parameters of the compositions is much helpful to understand the deformation and failure stages of the rock. In the current study, after dividing the frames in the video images photographed during the axial compression test into four parts (or, the upper left, upper right, lower left, and lower right ones), the digital parameters of various compositions in each part were then extracted. Using these… More >

  • Open Access


    Sentiment Analysis Using Deep Learning Approach

    Peng Cen1, Kexin Zhang1, Desheng Zheng1, *

    Journal on Artificial Intelligence, Vol.2, No.1, pp. 17-27, 2020, DOI:10.32604/jai.2020.010132

    Abstract Deep learning has made a great breakthrough in the field of speech and image recognition. Mature deep learning neural network has completely changed the field of nat ural language processing (NLP). Due to the enormous amount of data and opinions being produced, shared and transferred everyday across the Internet and other media, sentiment analysis has become one of the most active research fields in natural language processing. This paper introduces three deep learning networks applied in IMDB movie reviews sent iment analysis. Dataset was divided to 50% positive reviews and 50% negative reviews. Recurrent Neural Network (RNN) and Long Short-Term… More >

  • Open Access


    Information Classification and Extraction on Official Web Pages of Organizations

    Jinlin Wang1, Xing Wang1, *, Hongli Zhang1, Binxing Fang1, Yuchen Yang1, Jianan Liu2

    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 2057-2073, 2020, DOI:10.32604/cmc.2020.011158

    Abstract As a real-time and authoritative source, the official Web pages of organizations contain a large amount of information. The diversity of Web content and format makes it essential for pre-processing to get the unified attributed data, which has the value of organizational analysis and mining. The existing research on dealing with multiple Web scenarios and accuracy performance is insufficient. This paper aims to propose a method to transform organizational official Web pages into the data with attributes. After locating the active blocks in the Web pages, the structural and content features are proposed to classify information with the specific model.… More >

  • Open Access


    An Improved Method for the Fitting and Prediction of the Number of COVID-19 Confirmed Cases Based on LSTM

    Bingjie Yan1, Jun Wang1, Zhen Zhang2, Xiangyan Tang1, *, Yize Zhou1, Guopeng Zheng1, Qi Zou1, Yao Lu1, Boyi Liu3, Wenxuan Tu4, Neal Xiong5

    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1473-1490, 2020, DOI:10.32604/cmc.2020.011317

    Abstract New coronavirus disease (COVID-19) has constituted a global pandemic and has spread to most countries and regions in the world. Through understanding the development trend of confirmed cases in a region, the government can control the pandemic by using the corresponding policies. However, the common traditional mathematical differential equations and population prediction models have limitations for time series population prediction, and even have large estimation errors. To address this issue, we propose an improved method for predicting confirmed cases based on LSTM (Long-Short Term Memory) neural network. This work compares the deviation between the experimental results of the improved LSTM… More >

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