Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (185)
  • 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 >

  • Open Access


    A Hybrid Method of Coreference Resolution in Information Security

    Yongjin Hu1, Yuanbo Guo1, Junxiu Liu2, Han Zhang3, *

    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 >

  • Open Access


    A New Sequential Image Prediction Method Based on LSTM and DCGAN

    Wei Fang1, 2, Feihong Zhang1, *, 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 >

  • Open Access


    Massive Files Prefetching Model Based on LSTM Neural Network with Cache Transaction Strategy

    Dongjie Zhu1, Haiwen Du6, Yundong Sun1, Xiaofang Li2, Rongning Qu2, Hao Hu1, Shuangshuang Dong1, Helen Min Zhou3, Ning Cao4, 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 >

  • Open Access


    TdBrnn: An Approach to Learning Users’ Intention to Legal Consultation with Normalized Tensor Decomposition and Bi-LSTM

    Xiaoding Guo1, Hongli Zhang1, *, Lin Ye1, Shang Li1

    CMC-Computers, Materials & Continua, Vol.63, No.1, pp. 315-336, 2020, DOI:10.32604/cmc.2020.07506

    Abstract With the development of Internet technology and the enhancement of people’s concept of the rule of law, online legal consultation has become an important means for the general public to conduct legal consultation. However, different people have different language expressions and legal professional backgrounds. This phenomenon may lead to the phenomenon of different descriptions of the same legal consultation. How to accurately understand the true intentions behind different users’ legal consulting statements is an important issue that needs to be solved urgently in the field of legal consulting services. Traditional intent understanding algorithms rely heavily on the lexical and semantic… More >

  • Open Access


    A Novel Bidirectional LSTM and Attention Mechanism Based Neural Network for Answer Selection in Community Question Answering

    Bo Zhang1, Haowen Wang1, #, Longquan Jiang1, Shuhan Yuan2, Meizi Li1, *

    CMC-Computers, Materials & Continua, Vol.62, No.3, pp. 1273-1288, 2020, DOI:10.32604/cmc.2020.07269

    Abstract Deep learning models have been shown to have great advantages in answer selection tasks. The existing models, which employ encoder-decoder recurrent neural network (RNN), have been demonstrated to be effective. However, the traditional RNN-based models still suffer from limitations such as 1) high-dimensional data representation in natural language processing and 2) biased attentive weights for subsequent words in traditional time series models. In this study, a new answer selection model is proposed based on the Bidirectional Long Short-Term Memory (Bi-LSTM) and attention mechanism. The proposed model is able to generate the more effective question-answer pair representation. Experiments on a question… More >

  • Open Access


    Neural Dialogue Model with Retrieval Attention for Personalized Response Generation

    Cong Xu1, 2, Zhenqi Sun2, 3, Qi Jia2, 3, Dezheng Zhang2, 3, Yonghong Xie2, 3,*, Alan Yang4

    CMC-Computers, Materials & Continua, Vol.62, No.1, pp. 113-122, 2020, DOI:10.32604/cmc.2020.05239

    Abstract With the success of new speech-based human-computer interfaces, there is a great need for effective and friendly dialogue agents that can communicate with people naturally and continuously. However, the lack of personality and consistency is one of critical problems in neural dialogue systems. In this paper, we aim to generate consistent response with fixed profile and background information for building a realistic dialogue system. Based on the encoder-decoder model, we propose a retrieval mechanism to deliver natural and fluent response with proper information from a profile database. Moreover, in order to improve the efficiency of training the dataset related to… More >

  • Open Access


    Detecting Domain Generation Algorithms with Bi-LSTM

    Liang Ding1,*, Lunjie Li1, Jianghong Han1, Yuqi Fan2,*, Donghui Hu1

    CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 1285-1304, 2019, DOI:10.32604/cmc.2019.06160

    Abstract Botnets often use domain generation algorithms (DGA) to connect to a command and control (C2) server, which enables the compromised hosts connect to the C2 server for accessing many domains. The detection of DGA domains is critical for blocking the C2 server, and for identifying the compromised hosts as well. However, the detection is difficult, because some DGA domain names look normal. Much of the previous work based on statistical analysis of machine learning relies on manual features and contextual information, which causes long response time and cannot be used for real-time detection. In addition, when a new family of… More >

  • Open Access


    Hashtag Recommendation Using LSTM Networks with Self-Attention

    Yatian Shen1, Yan Li1, Jun Sun1,*, Wenke Ding1, Xianjin Shi1, Lei Zhang1, Xiajiong Shen1, Jing He2

    CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 1261-1269, 2019, DOI:10.32604/cmc.2019.06104

    Abstract On Twitter, people often use hashtags to mark the subject of a tweet. Tweets have specific themes or content that are easy for people to manage. With the increase in the number of tweets, how to automatically recommend hashtags for tweets has received wide attention. The previous hashtag recommendation methods were to convert the task into a multi-class classification problem. However, these methods can only recommend hashtags that appeared in historical information, and cannot recommend the new ones. In this work, we extend the self-attention mechanism to turn the hashtag recommendation task into a sequence labeling task. To train and… More >

Displaying 171-180 on page 18 of 185. Per Page