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  • Open Access

    ARTICLE

    A Novel Method of Heart Failure Prediction Based on DPCNNXGBOOST Model

    Yuwen Chen1, 2, 3, *, Xiaolin Qin1, 3, Lige Zhang1, 3, Bin Yi4

    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 495-510, 2020, DOI:10.32604/cmc.2020.011278 - 23 July 2020

    Abstract The occurrence of perioperative heart failure will affect the quality of medical services and threaten the safety of patients. Existing methods depend on the judgment of doctors, the results are affected by many factors such as doctors’ knowledge and experience. The accuracy is difficult to guarantee and has a serious lag. In this paper, a mixture prediction model is proposed for perioperative adverse events of heart failure, which combined with the advantages of the Deep Pyramid Convolutional Neural Networks (DPCNN) and Extreme Gradient Boosting (XGBOOST). The DPCNN was used to automatically extract features from patient’s More >

  • Open Access

    ARTICLE

    A Haze Feature Extraction and Pollution Level Identification Pre-Warning Algorithm

    Yongmei Zhang1, *, Jianzhe Ma2, Lei Hu3, Keming Yu4, Lihua Song1, 5, Huini Chen1

    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1929-1944, 2020, DOI:10.32604/cmc.2020.010556 - 30 June 2020

    Abstract The prediction of particles less than 2.5 micrometers in diameter (PM2.5) in fog and haze has been paid more and more attention, but the prediction accuracy of the results is not ideal. Haze prediction algorithms based on traditional numerical and statistical prediction have poor effects on nonlinear data prediction of haze. In order to improve the effects of prediction, this paper proposes a haze feature extraction and pollution level identification pre-warning algorithm based on feature selection and integrated learning. Minimum Redundancy Maximum Relevance method is used to extract low-level features of haze, and deep confidence More >

  • Open Access

    ARTICLE

    Simulation of Daily Diffuse Solar Radiation Based on Three Machine Learning Models

    Jianhua Dong1, Lifeng Wu2, Xiaogang Liu1, *, Cheng Fan1, Menghui Leng3, Qiliang Yang1

    CMES-Computer Modeling in Engineering & Sciences, Vol.123, No.1, pp. 49-73, 2020, DOI:10.32604/cmes.2020.09014 - 01 April 2020

    Abstract Solar radiation is an important parameter in the fields of computer modeling, engineering technology and energy development. This paper evaluated the ability of three machine learning models, i.e., Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM) and Multivariate Adaptive Regression Splines (MARS), to estimate the daily diffuse solar radiation (Rd). The regular meteorological data of 1966-2015 at five stations in China were taken as the input parameters (including mean average temperature (Ta), theoretical sunshine duration (N), actual sunshine duration (n), daily average air relative humidity (RH), and extra-terrestrial solar radiation (Ra)). And their estimation accuracies were subjected to… More >

  • Open Access

    ARTICLE

    Coal Rock Condition Detection Model Using Acoustic Emission and Light Gradient Boosting Machine

    Jing Li1, Yong Yang2, *, Hongmei Ge1, Li Zhao3, Ruxue Guo3, 4

    CMC-Computers, Materials & Continua, Vol.63, No.1, pp. 151-162, 2020, DOI:10.32604/cmc.2020.05649 - 30 March 2020

    Abstract Coal rock mass instability fracture may result in serious hazards to underground coal mining. Acoustic emissions (AE) stimulated by internal structure fracture should carry lots of favorable information about health condition of rock mass. AE as a sensitive non-destructive test method is gradually utilized to detect anomaly conditions of coal rock. This paper proposes an improved multi-resolution feature to extract AE waveform at different frequency resolutions using Coilflet Wavelet Transform method (CWT). It is further adopt an efficient Light Gradient Boosting Machine (LightGBM) by several cascaded sub weak classifier models to merge AE features at More >

  • Open Access

    ARTICLE

    A Recommendation System Based on Fusing Boosting Model and DNN Model

    Aziguli Wulam1,2, Yingshuai Wang1,2, Dezheng Zhang1,2,*, Jingyue Sang3, Alan Yang4

    CMC-Computers, Materials & Continua, Vol.60, No.3, pp. 1003-1013, 2019, DOI:10.32604/cmc.2019.07704

    Abstract In recent years, the models combining traditional machine learning with the deep learning are applied in many commodity recommendation practices. It has been proved better performance by the means of the neural network. Feature engineering has been the key to the success of many click rate estimation model. As we know, neural networks are able to extract high-order features automatically, and traditional linear models are able to extract low-order features. However, they are not necessarily efficient in learning all types of features. In traditional machine learning, gradient boosting decision tree is a typical representative of More >

  • Open Access

    ARTICLE

    AdaBoosting Neural Network for Short-Term Wind Speed Forecasting Based on Seasonal Characteristics Analysis and Lag Space Estimation

    Haijian Shao1, 2, Xing Deng1, 2, *

    CMES-Computer Modeling in Engineering & Sciences, Vol.114, No.3, pp. 277-293, 2018, DOI:10.3970/cmes.2018.114.277

    Abstract High accurary in wind speed forcasting remains hard to achieve due to wind’s random distribution nature and its seasonal characteristics. Randomness, intermittent and nonstationary usually cause the portion problem of the wind speed forecasting. Seasonal characteristics of wind speed means that its feature distribution is inconsistent. This typically results that the persistence of excitation for modeling can not be guaranteed, and may severely reduce the possibilities of high precise forecasting model. In this paper, we proposed two effective solutions to solve the problems caused by the randomness and seasonal characteristics of the wind speed. (1)… More >

  • Open Access

    ARTICLE

    Boosting forage yield and quality of maize (Zea mays L.) with multi-species bacterial inoculation in Pakistan

    Iqbal A1, MA Iqbal1, A Iqbal1, Z Aslam1, M Maqsood1, Z Ahmad2, N Akbar1, HZ Khan1, RN Abbas1, RD Khan1, G Abbas1, M Faisal1

    Phyton-International Journal of Experimental Botany, Vol.86, pp. 84-88, 2017, DOI:10.32604/phyton.2017.86.084

    Abstract Seed inoculation with bacterial species has the potential to increase yield and agro-qualitative attributes of forage crops. This study determined the response of forage maize to three plant growth promoting rhizobacteria [PGPR1 (Azotobacter chroococcum), PGPR2 (Pseudomonas flourescens) and PGPR3 (Bacillus megaterium)] inoculated individually and in different combinations (PGPR1+2, PGPR1+3, PGPR2+3 and PGPR1+2+3). A non-inoculated treatment was kept as a control. We used a completely randomized block design with four replicates. The PGPR1+2+3 treatment showed an outstanding performance by improving yield attributes, green forage yield, dry matter biomass, crude protein and total ash. The same treatment gave the lowest More >

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