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  • Open Access

    ARTICLE

    An Early Stopping-Based Artificial Neural Network Model for Atmospheric Corrosion Prediction of Carbon Steel

    Phyu Hnin Thike1, 2, Zhaoyang Zhao1, Peng Liu1, Feihu Bao1, Ying Jin1, Peng Shi1, *

    CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2091-2109, 2020, DOI:10.32604/cmc.2020.011608 - 16 September 2020

    Abstract The optimization of network topologies to retain the generalization ability by deciding when to stop overtraining an artificial neural network (ANN) is an existing vital challenge in ANN prediction works. The larger the dataset the ANN is trained with, the better generalization the prediction can give. In this paper, a large dataset of atmospheric corrosion data of carbon steel compiled from several resources is used to train and test a multilayer backpropagation ANN model as well as two conventional corrosion prediction models (linear and Klinesmith models). Unlike previous related works, a grid searchbased hyperparameter tuning… More >

  • Open Access

    ARTICLE

    SVM Model Selection Using PSO for Learning Handwritten Arabic Characters

    Mamouni El Mamoun1,*, Zennaki Mahmoud1, Sadouni Kaddour1

    CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 995-1008, 2019, DOI:10.32604/cmc.2019.08081

    Abstract Using Support Vector Machine (SVM) requires the selection of several parameters such as multi-class strategy type (one-against-all or one-against-one), the regularization parameter C, kernel function and their parameters. The choice of these parameters has a great influence on the performance of the final classifier. This paper considers the grid search method and the particle swarm optimization (PSO) technique that have allowed to quickly select and scan a large space of SVM parameters. A comparative study of the SVM models is also presented to examine the convergence speed and the results of each model. SVM is More >

  • Open Access

    ARTICLE

    Determination of the Thermodynamic Properties of Water and Steam in the p-T and p-S Planes via Different Grid Search Computer Algorithms

    Dugang Guo1, *

    FDMP-Fluid Dynamics & Materials Processing, Vol.15, No.4, pp. 419-430, 2019, DOI:10.32604/fdmp.2019.07831

    Abstract The role of different grid search computer algorithms for the determination of the thermodynamic properties of water and steam in the p-T and P-S planes has been investigated via experimental and analytical methods. The results show that the spline interpolation grid search algorithm and the power grid search algorithm are more efficient, stable and clear than other algorithms. More >

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