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Search Results (7)
  • Open Access


    Big Data Bot with a Special Reference to Bioinformatics

    Ahmad M. Al-Omari1,*, Shefa M. Tawalbeh1, Yazan H. Akkam2, Mohammad Al-Tawalbeh3, Shima’a Younis1, Abdullah A. Mustafa4, Jonathan Arnold5

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4155-4173, 2023, DOI:10.32604/cmc.2023.036956

    Abstract There are quintillions of data on deoxyribonucleic acid (DNA) and protein in publicly accessible data banks, and that number is expanding at an exponential rate. Many scientific fields, such as bioinformatics and drug discovery, rely on such data; nevertheless, gathering and extracting data from these resources is a tough undertaking. This data should go through several processes, including mining, data processing, analysis, and classification. This study proposes software that extracts data from big data repositories automatically and with the particular ability to repeat data extraction phases as many times as needed without human intervention. This software simulates the extraction of… More >

  • Open Access


    Recommendation Algorithm Integrating CNN and Attention System in Data Extraction

    Yang Li, Fei Yin, Xianghui Hui*

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4047-4063, 2023, DOI:10.32604/cmc.2023.036945

    Abstract With the rapid development of the Internet globally since the 21st century, the amount of data information has increased exponentially. Data helps improve people’s livelihood and working conditions, as well as learning efficiency. Therefore, data extraction, analysis, and processing have become a hot issue for people from all walks of life. Traditional recommendation algorithm still has some problems, such as inaccuracy, less diversity, and low performance. To solve these problems and improve the accuracy and variety of the recommendation algorithms, the research combines the convolutional neural networks (CNN) and the attention model to design a recommendation algorithm based on the… More >

  • Open Access


    LAME: Layout-Aware Metadata Extraction Approach for Research Articles

    Jongyun Choi1, Hyesoo Kong2, Hwamook Yoon2, Heungseon Oh3, Yuchul Jung1,*

    CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 4019-4037, 2022, DOI:10.32604/cmc.2022.025711

    Abstract The volume of academic literature, such as academic conference papers and journals, has increased rapidly worldwide, and research on metadata extraction is ongoing. However, high-performing metadata extraction is still challenging due to diverse layout formats according to journal publishers. To accommodate the diversity of the layouts of academic journals, we propose a novel LAyout-aware Metadata Extraction (LAME) framework equipped with the three characteristics (e.g., design of automatic layout analysis, construction of a large meta-data training set, and implementation of metadata extractor). In the framework, we designed an automatic layout analysis using PDFMiner. Based on the layout analysis, a large volume… More >

  • Open Access


    Complex Network Formation and Analysis of Online Social Media Systems

    Hafiz Abid Mahmood Malik*

    CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.3, pp. 1737-1750, 2022, DOI:10.32604/cmes.2022.018015

    Abstract To discover and identify the influential nodes in any complex network has been an important issue. It is a significant factor in order to control over the network. Through control on a network, any information can be spread and stopped in a short span of time. Both targets can be achieved, since network of information can be extended and as well destroyed. So, information spread and community formation have become one of the most crucial issues in the world of SNA (Social Network Analysis). In this work, the complex network of twitter social network has been formalized and results are… More >

  • Open Access


    An Efficient Mechanism for Product Data Extraction from E-Commerce Websites

    Malik Javed Akhtar1, Zahur Ahmad1, Rashid Amin1, *, Sultan H. Almotiri2, Mohammed A. Al Ghamdi2, Hamza Aldabbas3

    CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2639-2663, 2020, DOI:10.32604/cmc.2020.011485

    Abstract A large amount of data is present on the web which can be used for useful purposes like a product recommendation, price comparison and demand forecasting for a particular product. Websites are designed for human understanding and not for machines. Therefore, to make data machine-readable, it requires techniques to grab data from web pages. Researchers have addressed the problem using two approaches, i.e., knowledge engineering and machine learning. State of the art knowledge engineering approaches use the structure of documents, visual cues, clustering of attributes of data records and text processing techniques to identify data records on a web page.… More >

  • Open Access


    Research on Data Extraction and Analysis of Software Defect in IoT Communication Software

    Wenbin Bi1, Fang Yu2, Ning Cao3, Wei Huo3, Guangsheng Cao4, *, Xiuli Han5, Lili Sun6, Russell Higgs7

    CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1837-1854, 2020, DOI:10.32604/cmc.2020.010420

    Abstract Software defect feature selection has problems of feature space dimensionality reduction and large search space. This research proposes a defect prediction feature selection framework based on improved shuffled frog leaping algorithm (ISFLA).Using the two-level structure of the framework and the improved hybrid leapfrog algorithm's own advantages, the feature values are sorted, and some features with high correlation are selected to avoid other heuristic algorithms in the defect prediction that are easy to produce local The case where the convergence rate of the optimal or parameter optimization process is relatively slow. The framework improves generalization of predictions of unknown data samples… 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 >

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