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  • Open Access


    User Purchase Intention Prediction Based on Improved Deep Forest

    Yifan Zhang1, Qiancheng Yu1,2,*, Lisi Zhang1

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 661-677, 2024, DOI:10.32604/cmes.2023.044255

    Abstract Widely used deep neural networks currently face limitations in achieving optimal performance for purchase intention prediction due to constraints on data volume and hyperparameter selection. To address this issue, based on the deep forest algorithm and further integrating evolutionary ensemble learning methods, this paper proposes a novel Deep Adaptive Evolutionary Ensemble (DAEE) model. This model introduces model diversity into the cascade layer, allowing it to adaptively adjust its structure to accommodate complex and evolving purchasing behavior patterns. Moreover, this paper optimizes the methods of obtaining feature vectors, enhancement vectors, and prediction results within the deep forest algorithm to enhance the… More >

  • Open Access


    Defect Prediction Using Akaike and Bayesian Information Criterion

    Saleh Albahli1,*, Ghulam Nabi Ahmad Hassan Yar2

    Computer Systems Science and Engineering, Vol.41, No.3, pp. 1117-1127, 2022, DOI:10.32604/csse.2022.021750

    Abstract Data available in software engineering for many applications contains variability and it is not possible to say which variable helps in the process of the prediction. Most of the work present in software defect prediction is focused on the selection of best prediction techniques. For this purpose, deep learning and ensemble models have shown promising results. In contrast, there are very few researches that deals with cleaning the training data and selection of best parameter values from the data. Sometimes data available for training the models have high variability and this variability may cause a decrease in model accuracy. To… More >

  • Open Access


    A Weighted Combination Forecasting Model for Power Load Based on Forecasting Model Selection and Fuzzy Scale Joint Evaluation

    Bingbing Chen*, Zhengyi Zhu, Xuyan Wang, Can Zhang

    Energy Engineering, Vol.118, No.5, pp. 1499-1514, 2021, DOI:10.32604/EE.2021.015145

    Abstract To solve the medium and long term power load forecasting problem, the combination forecasting method is further expanded and a weighted combination forecasting model for power load is put forward. This model is divided into two stages which are forecasting model selection and weighted combination forecasting. Based on Markov chain conversion and cloud model, the forecasting model selection is implanted and several outstanding models are selected for the combination forecasting. For the weighted combination forecasting, a fuzzy scale joint evaluation method is proposed to determine the weight of selected forecasting model. The percentage error and mean absolute percentage error of… More >

  • Open Access


    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 applied to handwritten Arabic characters… More >

  • Open Access


    Establishment of Structure-Property Linkages Using a Bayesian Model Selection Method: Application to A Dual-Phase Metallic Composite System

    Hoheok Kim1, Tatsuki Yamamoto2, Yushi Sato1, Junya Inoue1,3,4,*

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.22, No.2, pp. 135-135, 2019, DOI:10.32604/icces.2019.05453

    Abstract The viability of establishing low-cost surrogate structure-property (S-P) linkages which applies a Bayesian model selection method to the Materials Knowledge System (MKS) homogenization framework is studied. The MKS framework employs the n-point correlation function, principal component analysis, and regression techniques for mapping between the structural factors and the property of a material. However, the framework chooses the factors not by their influence on the property but by their amount of inherent microstructural information. This also makes it difficult to find out which microstructural morphology affects the property. In the present work, we introduced a Bayesian model selection method to choose… More >

  • Open Access


    Universal Framework of Bayesian Creep Model Selection for Steel

    Yoh-ichi Mototake1, Hitoshi Izuno2, Kenji Nagata3,4, Masahiko Demura2 , Masato Okada1,2,*

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.22, No.2, pp. 129-130, 2019, DOI:10.32604/icces.2019.05389

    Abstract The creep deformation process is constructed by complex interactions of multiple factors, and the measurement of creep deformation requires enormous economic costs and a long experimental time, so there is a small amount of measurement data. In such a situation, multiple models are often proposed to explain the same experimental data. The coexistence of multiple models based on different physical assumptions makes it difficult to understand the creep deformation process.
    The purpose of this study is to construct a framework to compare and evaluate coexistence models based on measurement data using the Bayesian model selection framework. Basically, in the… More >

  • Open Access


    Creep Model Selection for Grade 91 Steel Using Data Scientific Method

    Hitoshi Izuno1, Masahiko Demura1,*, Masaaki Tabuchi2, Yohichi Mototake3, Masato Okada1,3

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.22, No.2, pp. 121-121, 2019, DOI:10.32604/icces.2019.05270

    Abstract An accurate creep deformation model is needed for detailed description of creep behavior of high temperature structural materials, e.g., Grade 91 steels used in boiler tubes of thermal power plants. Two types of creep constitutive equations are known, as follows: the one, e.g., modified theta method, assumes the existence of a steady state; and the other, e.g., theta method, does not. So far, both types have been selected on a case by case basis and there is no consensus on whether or not the steady state should be assumed even if limited in the Grade 91 steels. In this study,… More >

  • Open Access


    Determination of Working Pressure for Airport Runway Rubber Mark Cleaning Vehicle Based on Numeric Simulation

    Haojun Peng1,*, Zhongwei Wu1, Jinbing Xia1, Bolin Dong1, Yuntao Peng2, Linghe Wang3, Xingxing Ma3, Wei Shen3

    CMES-Computer Modeling in Engineering & Sciences, Vol.120, No.3, pp. 799-813, 2019, DOI:10.32604/cmes.2019.06950

    Abstract In this paper, numeric simulations are performed for three dimension models built according to actual surface cleaner in airport runway rubber mark cleaning vehicle using ANSYS FLUENT software on the basis of previous research finished by the authors. After analyzing the simulated flow fields under different standoff distances between nozzle outlet and runway surface and different discharge pressures at nozzle outlet, the relationships of normal stress and shear stress at striking point to outlet pressure and standoff distance are obtained. Finally, the most appropriate discharge pressure at nozzle outlet for the studied surface cleaner model is found, and this will… More >

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