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  • Open Access

    ARTICLE

    Research on the Influencing Rules of Gas Hydrate Emission Dissipation Coefficient Based on Subspace Spectrum Clustering

    Geng Guo1,*, Leiwen Chen1, Ji Li2, Shu Yan3, Wenxiang Wu4, Lingxu Li5, Hongda Li6

    Energy Engineering, Vol.117, No.2, pp. 79-88, 2020, DOI:10.32604/EE.2020.010529 - 23 April 2020

    Abstract Featured by high energy density, low combustion pollution and large quantity, natural gas hydrate has become one of the research hotspots in Sanlutian Field of Muri Coalfield since 2008, when China first drilled natural gas hydrate samples in the permafrost area of Qilian Mountains, Qinghai-Tibet Plateau. However, the study on the controlling factors of gas hydrate accumulation is still shallow, which hinders the exploration and development of natural gas hydrate resources. The controlling factors of gas hydrate accumulation mainly include temperature and pressure conditions, gas source conditions, sedimentary conditions and structural conditions, among which structural More >

  • Open Access

    ARTICLE

    A Security Sensitive Function Mining Approach Based on Precondition Pattern Analysis

    Zhongxu Yin1, *, Yiran Song2, Huiqin Chen3, Yan Cao4

    CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 1013-1029, 2020, DOI:10.32604/cmc.2020.09345 - 01 May 2020

    Abstract Security-sensitive functions are the basis for building a taint-style vulnerability model. Current approaches for extracting security-sensitive functions either don’t analyze data flow accurately, or not conducting pattern analyzing of conditions, resulting in higher false positive rate or false negative rate, which increased manual confirmation workload. In this paper, we propose a security sensitive function mining approach based on preconditon pattern analyzing. Firstly, we propose an enhanced system dependency graph analysis algorithm for precisely extracting the conditional statements which check the function parameters and conducting statistical analysis of the conditional statements for selecting candidate security sensitive More >

  • Open Access

    ARTICLE

    Hybrid Clustering Algorithms with GRASP to Construct an Initial Solution for the MVPPDP

    Abeer I. Alhujaylan1, 2, *, Manar I. Hosny1

    CMC-Computers, Materials & Continua, Vol.62, No.3, pp. 1025-1051, 2020, DOI:10.32604/cmc.2020.08742

    Abstract Mobile commerce (m-commerce) contributes to increasing the popularity of electronic commerce (e-commerce), allowing anybody to sell or buy goods using a mobile device or tablet anywhere and at any time. As demand for e-commerce increases tremendously, the pressure on delivery companies increases to organise their transportation plans to achieve profits and customer satisfaction. One important planning problem in this domain is the multi-vehicle profitable pickup and delivery problem (MVPPDP), where a selected set of pickup and delivery customers need to be served within certain allowed trip time. In this paper, we proposed hybrid clustering algorithms More >

  • Open Access

    ARTICLE

    Agile Satellite Mission Planning via Task Clustering and Double-Layer Tabu Algorithm

    Yanbin Zhao1, *, Bin Du2, Shuang Li2

    CMES-Computer Modeling in Engineering & Sciences, Vol.122, No.1, pp. 235-257, 2020, DOI:10.32604/cmes.2020.08070 - 01 January 2020

    Abstract Satellite observation schedule is investigated in this paper. A mission planning algorithm of task clustering is proposed to improve the observation efficiency of agile satellite. The newly developed method can make the satellite observe more targets and therefore save observation resources. First, for the densely distributed target points, a pre-processing scheme based on task clustering is proposed. The target points are clustered according to the distance condition. Second, the local observation path is generated by Tabu algorithm in the inner layer of cluster regions. Third, considering the scatter and cluster sets, the global observation path More >

  • Open Access

    ARTICLE

    News Text Topic Clustering Optimized Method Based on TF-IDF Algorithm on Spark

    Zhuo Zhou1, Jiaohua Qin1,*, Xuyu Xiang1, Yun Tan1, Qiang Liu1, Neal N. Xiong2

    CMC-Computers, Materials & Continua, Vol.62, No.1, pp. 217-231, 2020, DOI:10.32604/cmc.2020.06431

    Abstract Due to the slow processing speed of text topic clustering in stand-alone architecture under the background of big data, this paper takes news text as the research object and proposes LDA text topic clustering algorithm based on Spark big data platform. Since the TF-IDF (term frequency-inverse document frequency) algorithm under Spark is irreversible to word mapping, the mapped words indexes cannot be traced back to the original words. In this paper, an optimized method is proposed that TF-IDF under Spark to ensure the text words can be restored. Firstly, the text feature is extracted by More >

  • Open Access

    ARTICLE

    A Survey and Systematic Categorization of Parallel K-Means and Fuzzy-C-Means Algorithms

    Ahmed A. M. Jamel1,∗, Bahriye Akay2,†

    Computer Systems Science and Engineering, Vol.34, No.5, pp. 259-281, 2019, DOI:10.32604/csse.2019.34.259

    Abstract Parallel processing has turned into one of the emerging fields of machine learning due to providing consistent work by performing several tasks simultaneously, enhancing reliability (the presence of more than one device ensures the workflow even if some devices disrupted), saving processing time and introducing low cost and high-performance computation units. This research study presents a survey of parallel K-means and Fuzzy-c-means clustering algorithms based on their implementations in parallel environments such as Hadoop, MapReduce, Graphical Processing Units, and multi-core systems. Additionally, the enhancement in parallel clustering algorithms is investigated as hybrid approaches in which More >

  • Open Access

    ARTICLE

    The Analysis of China’s Integrity Situation Based on Big Data

    Wangdong Jiang1, Taian Yang1, *, Guang Sun1, 3, Yucai Li1, Yixuan Tang2, Hongzhang Lv1, Wenqian Xiang1

    Journal on Big Data, Vol.1, No.3, pp. 117-134, 2019, DOI:10.32604/jbd.2019.08454

    Abstract In order to study deeply the prominent problems faced by China’s clean government work, and put forward effective coping strategies, this article analyzes the network information of anti-corruption related news events, which is based on big data technology. In this study, we take the news report from the website of the Communist Party of China (CPC) Central Commission for Discipline Inspection (CCDI) as the source of data. Firstly, the obtained text data is converted to word segmentation and stop words under preprocessing, and then the pre-processed data is improved by vectorization and text clustering, finally,… More >

  • Open Access

    ARTICLE

    Genetic-Frog-Leaping Algorithm for Text Document Clustering

    Lubna Alhenak1, Manar Hosny1,*

    CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 1045-1074, 2019, DOI:10.32604/cmc.2019.08355

    Abstract In recent years, the volume of information in digital form has increased tremendously owing to the increased popularity of the World Wide Web. As a result, the use of techniques for extracting useful information from large collections of data, and particularly documents, has become more necessary and challenging. Text clustering is such a technique; it consists in dividing a set of text documents into clusters (groups), so that documents within the same cluster are closely related, whereas documents in different clusters are as different as possible. Clustering depends on measuring the content (i.e., words) of… More >

  • Open Access

    ARTICLE

    Novel Ensemble Modeling Method for Enhancing Subset Diversity Using Clustering Indicator Vector Based on Stacked Autoencoder

    Yanzhen Wang1, Xuefeng Yan1,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.121, No.1, pp. 123-144, 2019, DOI:10.32604/cmes.2019.07052

    Abstract A single model cannot satisfy the high-precision prediction requirements given the high nonlinearity between variables. By contrast, ensemble models can effectively solve this problem. Three key factors for improving the accuracy of ensemble models are namely the high accuracy of a submodel, the diversity between subsample sets and the optimal ensemble method. This study presents an improved ensemble modeling method to improve the prediction precision and generalization capability of the model. Our proposed method first uses a bagging algorithm to generate multiple subsample sets. Second, an indicator vector is defined to describe these subsample sets. More >

  • Open Access

    ARTICLE

    A New Time-Aware Collaborative Filtering Intelligent Recommendation System

    Weijin Jiang1,2,3, Jiahui Chen1,*, Yirong Jiang4,*, Yuhui Xu1, Yang Wang1, Lina Tan1, Guo Liang5

    CMC-Computers, Materials & Continua, Vol.61, No.2, pp. 849-859, 2019, DOI:10.32604/cmc.2019.05932

    Abstract Aiming at the problem that the traditional collaborative filtering recommendation algorithm does not fully consider the influence of correlation between projects on recommendation accuracy, this paper introduces project attribute fuzzy matrix, measures the project relevance through fuzzy clustering method, and classifies all project attributes. Then, the weight of the project relevance is introduced in the user similarity calculation, so that the nearest neighbor search is more accurate. In the prediction scoring section, considering the change of user interest with time, it is proposed to use the time weighting function to improve the influence of the More >

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