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  • Open Access

    ARTICLE

    A Heuristic Radiomics Feature Selection Method Based on Frequency Iteration and Multi-Supervised Training Mode

    Zhigao Zeng1,2, Aoting Tang1,2, Shengqiu Yi1,2, Xinpan Yuan1,2, Yanhui Zhu1,2,*

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2277-2293, 2024, DOI:10.32604/cmc.2024.047989 - 15 May 2024

    Abstract Radiomics is a non-invasive method for extracting quantitative and higher-dimensional features from medical images for diagnosis. It has received great attention due to its huge application prospects in recent years. We can know that the number of features selected by the existing radiomics feature selection methods is basically about ten. In this paper, a heuristic feature selection method based on frequency iteration and multiple supervised training mode is proposed. Based on the combination between features, it decomposes all features layer by layer to select the optimal features for each layer, then fuses the optimal features More >

  • Open Access

    ARTICLE

    Weakly Supervised Abstractive Summarization with Enhancing Factual Consistency for Chinese Complaint Reports

    Ren Tao, Chen Shuang*

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6201-6217, 2023, DOI:10.32604/cmc.2023.036178 - 29 April 2023

    Abstract A large variety of complaint reports reflect subjective information expressed by citizens. A key challenge of text summarization for complaint reports is to ensure the factual consistency of generated summary. Therefore, in this paper, a simple and weakly supervised framework considering factual consistency is proposed to generate a summary of city-based complaint reports without pre-labeled sentences/words. Furthermore, it considers the importance of entity in complaint reports to ensure factual consistency of summary. Experimental results on the customer review datasets (Yelp and Amazon) and complaint report dataset (complaint reports of Shenyang in China) show that the More >

  • Open Access

    ARTICLE

    Deep Reinforcement Learning-Based Long Short-Term Memory for Satellite IoT Channel Allocation

    S. Lakshmi Durga1, Ch. Rajeshwari1, Khalid Hamed Allehaibi2, Nishu Gupta3,*, Nasser Nammas Albaqami4, Isha Bharti5, Ahmad Hoirul Basori6

    Intelligent Automation & Soft Computing, Vol.33, No.1, pp. 1-19, 2022, DOI:10.32604/iasc.2022.022536 - 05 January 2022

    Abstract In recent years, the demand for smart wireless communication technology has increased tremendously, and it urges to extend internet services globally with high reliability, less cost and minimal delay. In this connection, low earth orbit (LEO) satellites have played prominent role by reducing the terrestrial infrastructure facilities and providing global coverage all over the earth with the help of satellite internet of things (SIoT). LEO satellites provide wide coverage area to dynamically accessing network with limited resources. Presently, most resource allocation schemes are designed only for geostationary earth orbit (GEO) satellites. For LEO satellites, resource allocation… More >

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