Yifan Zhang¹, Qiancheng Yu^1,2,*, 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 >

Saleh Albahli^1,*, 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 >

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 >

Mamouni El Mamoun^1,*, 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 >

Hoheok Kim¹, Tatsuki Yamamoto², Yushi Sato¹, Junya Inoue^1,3,4,*

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 >

Yoh-ichi Mototake¹, Hitoshi Izuno², Kenji Nagata^3,4, Masahiko Demura² , Masato Okada^1,2,*

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 >

Hitoshi Izuno¹, Masahiko Demura^1,*, Masaaki Tabuchi², Yohichi Mototake³, Masato Okada^1,3

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 >

Haojun Peng^1,*, 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 >