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Search Results (4)
  • Open Access

    ARTICLE

    RBMDO Using Gaussian Mixture Model-Based Second-Order Mean-Value Saddlepoint Approximation

    Debiao Meng1,2,3, Shiyuan Yang1, Tao Lin4,5,*, Jiapeng Wang1, Hengfei Yang1, Zhiyuan Lv1

    CMES-Computer Modeling in Engineering & Sciences, Vol.132, No.2, pp. 553-568, 2022, DOI:10.32604/cmes.2022.020756

    Abstract Actual engineering systems will be inevitably affected by uncertain factors. Thus, the Reliability-Based Multidisciplinary Design Optimization (RBMDO) has become a hotspot for recent research and application in complex engineering system design. The Second-Order/First-Order Mean-Value Saddlepoint Approximate (SOMVSA/FOMVSA) are two popular reliability analysis strategies that are widely used in RBMDO. However, the SOMVSA method can only be used efficiently when the distribution of input variables is Gaussian distribution, which significantly limits its application. In this study, the Gaussian Mixture Model-based Second-Order Mean-Value Saddlepoint Approximation (GMM-SOMVSA) is introduced to tackle above problem. It is integrated with the Collaborative Optimization (CO) method to… More >

  • Open Access

    ARTICLE

    Virtual Nursing Using Deep Belief Networks for Elderly People (DBN-EP)

    S. Rajasekaran1,*, G. Kousalya2

    Computer Systems Science and Engineering, Vol.42, No.3, pp. 985-1000, 2022, DOI:10.32604/csse.2022.022234

    Abstract The demand for better health services has resulted in the advancement of remote monitoring health, i.e., virtual nursing systems, to watch and support the elderly with innovative concepts such as being patient-centric, easier to use, and having smarter interactions and more accurate conclusions. While virtual nursing services attempt to provide consumers and medical practitioners with continuous medical and health monitoring services, access to allied healthcare experts such as nurses remains a challenge. In this research, we present Virtual Nursing Using Deep Belief Networks for Elderly People (DBN-EP), a new framework that provides a virtual nurse agent deployed on a senior… More >

  • Open Access

    ARTICLE

    Effect of RBM10 on regulating the proliferation and metastasis activity of human hepatocellular carcinoma cells by affecting the stability of miR-21

    LI JIANG1,*, KE ZHANG1, CHONG HU1, HONGWEI ZHANG1, DANPU WANG1, WEIWEI ZHAI2

    BIOCELL, Vol.46, No.4, pp. 969-978, 2022, DOI:10.32604/biocell.2022.017765

    Abstract In recent decades, RNA binding motif (RBM) proteins have been widespread concerned by researchers. Among them, RBM5 is considered as a potential tumor suppressor gene in HCC. RBM10, also belonging to the RBM family, have similar structure and high homology with RBM5, indicating its potential as potential tumor suppressor genes. However, the role of RBM10 in tumors is controversial. The purpose of this study was to analyze the expression correlation and functional relationship of miR-21 and RBM10 in human hepatocellular carcinoma (HCC) tissues and corresponding tumor cells. Bioinformatics analysis showed that miR-21 and RBM10 were both highly expressed in HCC;… More >

  • Open Access

    ARTICLE

    A Study of Unmanned Path Planning Based on a Double-Twin RBM-BP Deep Neural Network

    Xuan Chen1,*, Zhiping Wan1, Jiatong Wang2

    Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1531-1548, 2020, DOI:10.32604/iasc.2020.011723

    Abstract Addressing the shortcomings of unmanned path planning, such as significant error and low precision, a path-planning algorithm based on the whale optimization algorithm (WOA)-optimized double-blinking restricted Boltzmann machine-back propagation (RBM-BP) deep neural network model is proposed. The model consists mainly of two twin RBMs and one BP neural network. One twin RBM is used for feature extraction of the unmanned path location, and the other RBM is used for the path similarity calculation. The model uses the WOA algorithm to optimize parameters, which reduces the number of training sessions, shortens the training time, and reduces the training errors of the… More >

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