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  • Open Access

    ARTICLE

    Evaluating the Potentials of PLSR and SVR Models for Soil Properties Prediction Using Field Imaging, Laboratory VNIR Spectroscopy and Their Combination

    Emna Karray1, Hela Elmannai2,*, Elyes Toumi1, Mohamed Hedi Gharbia3, Souham Meshoul2, Hamouda Aichi4, Zouhaier Ben Rabah1

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.2, pp. 1399-1425, 2023, DOI:10.32604/cmes.2023.023164

    Abstract Pedo-spectroscopy has the potential to provide valuable information about soil physical, chemical, and biological properties. Nowadays, we may predict soil properties using VNIR field imaging spectra (IS) such as Prisma satellite data or laboratory spectra (LS). The primary goal of this study is to investigate machine learning models namely Partial Least Squares Regression (PLSR) and Support Vector Regression (SVR) for the prediction of several soil properties, including clay, sand, silt, organic matter, nitrate NO3-, and calcium carbonate CaCO3, using five VNIR spectra dataset combinations (% IS, % LS) as follows: C1 (0% IS, 100% LS), C2 (20% IS, 80% LS),… More > Graphic Abstract

    Evaluating the Potentials of PLSR and SVR Models for Soil Properties Prediction Using Field Imaging, Laboratory VNIR Spectroscopy and Their Combination

  • Open Access

    ARTICLE

    A Hybrid BPNN-GARF-SVR Prediction Model Based on EEMD for Ship Motion

    Hao Han, Wei Wang*

    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 1353-1370, 2023, DOI:10.32604/cmes.2022.021494

    Abstract Accurate prediction of ship motion is very important for ensuring marine safety, weapon control, and aircraft carrier landing, etc. Ship motion is a complex time-varying nonlinear process which is affected by many factors. Time series analysis method and many machine learning methods such as neural networks, support vector machines regression (SVR) have been widely used in ship motion predictions. However, these single models have certain limitations, so this paper adopts a multi-model prediction method. First, ensemble empirical mode decomposition (EEMD) is used to remove noise in ship motion data. Then the random forest (RF) prediction model optimized by genetic algorithm… More >

  • Open Access

    ARTICLE

    N-SVRG: Stochastic Variance Reduction Gradient with Noise Reduction Ability for Small Batch Samples

    Haijie Pan, Lirong Zheng*

    CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.1, pp. 493-512, 2022, DOI:10.32604/cmes.2022.019069

    Abstract The machine learning model converges slowly and has unstable training since large variance by random using a sample estimate gradient in SGD. To this end, we propose a noise reduction method for Stochastic Variance Reduction gradient (SVRG), called N-SVRG, which uses small batches samples instead of all samples for the average gradient calculation, while performing an incremental update of the average gradient. In each round of iteration, a small batch of samples is randomly selected for the average gradient calculation, while the average gradient is updated by rounding of the past model gradients during internal iterations. By suitably reducing the… More >

  • Open Access

    ARTICLE

    Gaussian Kernel Based SVR Model for Short-Term Photovoltaic MPP Power Prediction

    Yasemin Onal*

    Computer Systems Science and Engineering, Vol.41, No.1, pp. 141-156, 2022, DOI:10.32604/csse.2022.020367

    Abstract Predicting the power obtained at the output of the photovoltaic (PV) system is fundamental for the optimum use of the PV system. However, it varies at different times of the day depending on intermittent and nonlinear environmental conditions including solar irradiation, temperature and the wind speed, Short-term power prediction is vital in PV systems to reconcile generation and demand in terms of the cost and capacity of the reserve. In this study, a Gaussian kernel based Support Vector Regression (SVR) prediction model using multiple input variables is proposed for estimating the maximum power obtained from using perturb observation method in… More >

  • Open Access

    ARTICLE

    Grain Yield Predict Based on GRA-AdaBoost-SVR Model

    Diantao Hu, Cong Zhang*, Wenqi Cao, Xintao Lv, Songwu Xie

    Journal on Big Data, Vol.3, No.2, pp. 65-76, 2021, DOI:10.32604/jbd.2021.016317

    Abstract Grain yield security is a basic national policy of China, and changes in grain yield are influenced by a variety of factors, which often have a complex, non-linear relationship with each other. Therefore, this paper proposes a Grey Relational Analysis–Adaptive Boosting–Support Vector Regression (GRAAdaBoost-SVR) model, which can ensure the prediction accuracy of the model under small sample, improve the generalization ability, and enhance the prediction accuracy. SVR allows mapping to high-dimensional spaces using kernel functions, good for solving nonlinear problems. Grain yield datasets generally have small sample sizes and many features, making SVR a promising application for grain yield datasets.… More >

  • Open Access

    ARTICLE

    Prediction of College Students’ Physical Fitness Based on K-Means Clustering and SVR

    Peng Tang, Yu Wang, Ning Shen

    Computer Systems Science and Engineering, Vol.35, No.4, pp. 237-246, 2020, DOI:10.32604/csse.2020.35.237

    Abstract In today’s modern society, the physical fitness of college students is gradually declining. In this paper, a prediction model for college students’ physical fitness is established, in which support vector regression (SVR) and k-means clustering are combined together for the prediction of college students’ fitness. Firstly, the physical measurement data of college students are classified according to gender and class characteristics. Then, the k-means clustering method is used to classify the physical measurement data of college students. Next, the physical characteristics of college students are extracted by SVR to establish the prediction model of physical indicators, and the model for… More >

  • Open Access

    ARTICLE

    The effect of right ventricular function on survival and morbidity following stage 2 palliation: An analysis of the single ventricle reconstruction trial public data set

    Vanessa Marie Hormaza1, Mark Conaway2, Daniel Scott Schneider1, Jeffrey Eric Vergales1

    Congenital Heart Disease, Vol.14, No.2, pp. 274-279, 2019, DOI:10.1111/chd.12722

    Abstract Objective: Limited information is known on how right ventricular function affects outcomes after stage 2 palliation. We evaluated the impact of different right ventricular indices prior to stage 2 palliation on morbidity and mortality.
    Design: Retrospective study design.
    Setting: Pediatric Heart Network Single Ventricle Reconstruction Trial Public Data Set.
    Patient: Any variant of stage 1 palliation and all anatomic hypoplastic left heart syndrome variants in the trial were evaluated. Echocardiograms prior to stage 2 palliation were analyzed and compared between those who failed and those who survived.
    Intervention: None.
    Outcome measures: Mortality was defined as death, listed for transplant, or… More >

  • Open Access

    ARTICLE

    Yield Stress Prediction Model of RAFM Steel Based on the Improved GDM-SA-SVR Algorithm

    Sifan Long1, Ming Zhao2,*, Xinfu He3

    CMC-Computers, Materials & Continua, Vol.58, No.3, pp. 727-760, 2019, DOI:10.32604/cmc.2019.04454

    Abstract With the development of society and the exhaustion of fossil energy, researcher need to identify new alternative energy sources. Nuclear energy is a very good choice, but the key to the successful application of nuclear technology is determined primarily by the behavior of nuclear materials in reactors. Therefore, we studied the radiation performance of the fusion material reduced activation ferritic/martensitic (RAFM) steel. The main novelty of this paper are the statistical analysis of RAFM steel data sets through related statistical analysis and the formula derivation of the gradient descent method (GDM) which combines the gradient descent search strategy of the… More >

  • Open Access

    ARTICLE

    Forecasting Model Based on Information-Granulated GA-SVR and ARIMA for Producer Price Index

    Xiangyan Tang1,2, Liang Wang3, Jieren Cheng1,2,4,*, Jing Chen2, Victor S. Sheng5

    CMC-Computers, Materials & Continua, Vol.58, No.2, pp. 463-491, 2019, DOI:10.32604/cmc.2019.03816

    Abstract The accuracy of predicting the Producer Price Index (PPI) plays an indispensable role in government economic work. However, it is difficult to forecast the PPI. In our research, we first propose an unprecedented hybrid model based on fuzzy information granulation that integrates the GA-SVR and ARIMA (Autoregressive Integrated Moving Average Model) models. The fuzzy-information-granulation-based GA-SVR-ARIMA hybrid model is intended to deal with the problem of imprecision in PPI estimation. The proposed model adopts the fuzzy information-granulation algorithm to pre-classification-process monthly training samples of the PPI, and produced three different sequences of fuzzy information granules, whose Support Vector Regression (SVR) machine… More >

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