Ning Chen¹, Naernaer Xialihaer^2,3, Weiliang Kong³, Jiping Ren^2,3,*

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 >

Xin Liu^1,*, Yujuan Si^1,2, 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 >

Ziyong Ran¹, Desheng Zheng^{1, *}, 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 >

Yalei Wang¹, Jinming Xu^1,2,*, 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 >

Peng Cen¹, Kexin Zhang¹, Desheng Zheng^{1, *}

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 >

Jinlin Wang¹, Xing Wang^{1, *}, 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 >

Bingjie Yan¹, Jun Wang¹, Zhen Zhang², Xiangyan Tang^{1, *}, 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 >

Yongjin Hu¹, Yuanbo Guo¹, Junxiu Liu², Han Zhang^{3, *}

CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 1297-1315, 2020, DOI:10.32604/cmc.2020.010855

Abstract In the field of information security, a gap exists in the study of coreference resolution of entities. A hybrid method is proposed to solve the problem of coreference resolution in information security. The work consists of two parts: the first extracts all candidates (including noun phrases, pronouns, entities, and nested phrases) from a given document and classifies them; the second is coreference resolution of the selected candidates. In the first part, a method combining rules with a deep learning model (Dictionary BiLSTM-Attention-CRF, or DBAC) is proposed to extract all candidates in the text and classify them. In the DBAC model,… More >

Wei Fang^{1, 2}, Feihong Zhang^{1, *}, Yewen Ding¹, Jack Sheng³

CMC-Computers, Materials & Continua, Vol.64, No.1, pp. 217-231, 2020, DOI:10.32604/cmc.2020.06395

Abstract Image recognition technology is an important field of artificial intelligence. Combined with the development of machine learning technology in recent years, it has great researches value and commercial value. As a matter of fact, a single recognition function can no longer meet people’s needs, and accurate image prediction is the trend that people pursue. This paper is based on Long Short-Term Memory (LSTM) and Deep Convolution Generative Adversarial Networks (DCGAN), studies and implements a prediction model by using radar image data. We adopt a stack cascading strategy in designing network connection which can control of parameter convergence better. This new… More >

Dongjie Zhu¹, Haiwen Du⁶, Yundong Sun¹, Xiaofang Li², Rongning Qu², Hao Hu¹, Shuangshuang Dong¹, Helen Min Zhou³, Ning Cao^{4, 5, *},

CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 979-993, 2020, DOI:10.32604/cmc.2020.06478

Abstract In distributed storage systems, file access efficiency has an important impact on the real-time nature of information forensics. As a popular approach to improve file accessing efficiency, prefetching model can fetches data before it is needed according to the file access pattern, which can reduce the I/O waiting time and increase the system concurrency. However, prefetching model needs to mine the degree of association between files to ensure the accuracy of prefetching. In the massive small file situation, the sheer volume of files poses a challenge to the efficiency and accuracy of relevance mining. In this paper, we propose a… More >