SVR Model Research Articles

Comparison of Biological Age Prediction Models Using Clinical Biomarkers Commonly Measured in Clinical Practice Settings: AI Techniques Vs. Traditional Statistical Methods

In this work, we used the health check-up data of more than 111,000 subjects for analysis, using only the data with all 35 variables entered. For the prediction of biological age, traditional statistical methods and four AI techniques (RF, XGB, SVR, and DNN), which are widely used recently, were simultaneously used to compare the predictive power. This study showed that AI models produced about 1.6 times stronger linear relationship on average than statistical models. In addition, the regression analysis on the predicted BA and CA revealed similar differences in terms of both the correlation coefficients (linear model: 0.831, polynomial model: 0.996, XGB model: 0.66, RF model: 0.927, SVR model: 0.787, DNN model: 0.998) and R2 values. Through this work, we confirmed that AI techniques such as the DNN model outperformed traditional statistical methods in predicting biological age.

Tide Prediction in Prigi Beach using Support Vector Regression (SVR) Method

Purpose: Prigi Beach has the largest fishing port in East Java, but the topography of this beach is quite gentle, so it is prone to disasters such as tidal flooding. The tides of seawater strongly influence the occurrence of this natural event. Therefore, information on tidal level data is essential. This study aims to provide information about tidal predictions.Methods: In this case using the SVE method. Input data and time were examined using PACF autocorrelation plots to form input data patterns. The working principle of SVR is to find the best hyperplane in the form of a function that produces the slightest error.Result: The best SVR model built from the linear kernel, the MAPE value is 0.5510%, the epsilon is 0.0614, and the bias is 0.6015. The results of the tidal prediction on Prigi Beach in September 2020 showed that the highest tide occurred on September 19, 2020, at 10.00 PM, and the lowest tide occurred on September 3, 2020, at 04.00 AM. Value: After conducting experiments on three types of kernels on SVR, it is said that linear kernels can predict improvements better than polynomial and gaussian kernels.

Comparative study of machine learning approaches for predicting short-term photovoltaic power output based on weather type classification

To combat the worsening global energy shortage, global photovoltaic (PV) installation capacity has been increasing rapidly every year. Since the instability and intermittence of PV power output have great impacts on utility grids, accurate PV power output prediction is crucial. This paper proposes the use of machine learning approaches, combined with a weather type classification method, to predict short-term PV power output. The datasets are collected from a commercial PV power station located in Yangjiang, Guangdong province of China (latitude 21.56 °N, longitude 112.09 °E). Firstly, daytime meteorological data from 07:30 to 18:00 are divided into six 2-h intervals, and then the meteorological conditions of each interval are divided into four categories using an Extremely randomized Trees Classification model according to the PV power generation in each period. Secondly, nine machine learning models are established based on the weather type classification to predict the PV power output. The results show that weather type classification is vital to the selection of appropriate machine learning models and the accurate prediction of PV power output because the characteristic correlation between the meteorological data and PV power output always changes. In general, the Lasso Regressor, Random Forest Regressor, Gradient Boosting Regressor, and Support Vector Regressor models show better performances than the other models. Furthermore, all the models’ accuracy is relatively high when the local meteorological conditions are relatively stable, such as in October, November, and December, during which time the Mean Relative Error values are 2.07, 1.07, and 1.73, respectively. During the period when the weather is unstable, the performance of the SVR model is better than that of the other models. The prediction accuracy can be significantly improved with integrating the accurate weather classification into the model. With regards to each daytime period, the prediction accuracy in the morning and evening is relatively high and the MREs for these times are small. This study provides a theoretical basis for selecting appropriate machine learning models to predict photovoltaic power generation under different weather conditions.

Comparing the Performance of Several Multivariate Control Charts Based on Residual of Multioutput Least Square SVR (MLS-SVR) Model in Monitoring Water Production Process

Water that is used as the basic human need, requires a processing process to get it. Water quality control in Tirtanadi Water Treatment Plant is still univariate, while theoretically the quality characteristics of water quality are correlated and there is also an autocorrelation due to the continuous process. In this study, quality control is performed on three main variables of water quality characteristics, namely acidity (pH), chlorine residual (ppm), and turbidity (NTU) using several multivariate control charts based on Multioutput Least Square Support Vector Regression (MLS-SVR) residuals. MLS-SVR modelling is used to overcome and get rid of autocorrelation. The input results of the MLS-SVR model are specified from the significant lag of the Partial Autocorrelation Function (PACF), which in this study, is the first lag. The results of the MLS-SVR input model and the optimal combination of hyper-parameters produce residual values that have no autocorrelation anymore. The residuals are used to develop the Hotelling’s T 2, Multivariate Exponentially Weighted Moving Average (MEWMA), and Multivariate Cumulative Sum (MCUSUM) control charts. In phase I, we found that the processes are statically controlled. Meanwhile, in phase II, the monitoring results show that there are several out-of-control observations.

Customer tiered purchase forecast by mobile edge computing based on Pareto/NBD and SVR

Mobile edge computing is trending nowadays for its computation efficiency and privacy. The rapid development of e-commerce show great interest in mobile edge computing due to numerous rise of small and middle-sized enterprises(SMEs) in the internet. This paper predicts the overall sales volume of the enterprise through the classic ARIMA model, and notes that the behavior and arrival differences between the new and old customer groups will affect the accuracy of our forecasts, so we then use Pareto/NBD to explore the repeated purchases of customers at the individual level of the old customer and the SVR model to predict the arrival of new customers, thus helping the enterprise to make layered and accurate marketing of new and old customers through machine learning. In general, machine learning relies on powerful computation and storage resources, while mobile edge computing typically provides limited computation resources locally. Therefore, it is essential to combine machine learning with mobile edge computing to further promote the proliferation of data analysis among SMEs.

Prediction of House Price Index Based on Bagging Integrated WOA-SVR Model

Aiming at the shortcomings of a single machine learning model with low model prediction accuracy and insufficient generalization ability in house price index prediction, a whale algorithm optimized support vector regression model based on bagging ensemble learning method is proposed. Firstly, gray correlation analysis is used to obtain the main influencing factors of house prices, and the segmentation forecasting method is used to divide the data set and forecast the house prices in the coming year using the data of the past ten years. Secondly, the whale optimization algorithm is used to find the optimal parameters of the penalty factor and kernel function in the SVR model, and then, the WOA-SVR model is established. Finally, in order to further improve the model generalization capability, a bagging integration strategy is used to further integrate and optimize the WOA-SVR model. The experiments are conducted to forecast the house price indices of four regions, Beijing, Shanghai, Tianjin, and Chongqing, respectively, and the results show that the prediction accuracy of the proposed integrated model is better than the comparison model in all cases.

Estimation of rectangular and triangular side weir discharge

ABSTRACT In this paper, a straightforward approach was applied for estimating the discharge of sharp-crested rectangular and triangular side weirs. The extensive experimental data (356 for rectangular side weir and 362 for triangular side weir) and the available data from other sources (171 for rectangular side weir and 145 for triangular side weir) were used for the analysis. The effects of approach Froude number Fr 1, ratio of weir height to upstream flow depth p/y 1, ratio of effective weir length to main channel width L/B, and ratio of effective weir length to upstream flow depth L/y 1 were studied. It was found that the parameters p/y 1, Fr 1, and L/B have a reciprocal effect on each other. Sensitivity analysis performed by using ANFIS model, showed that the relative discharge of the rectangular side weir is influenced by Fr 1, p/y 1, L/B, and L/y 1. Moreover, Fr 1 for triangular side weir and p/y 1 for rectangular side weir are the most effective parameters influencing the relative discharge. Meanwhile, L/y 1 has a secondary effect on the relative discharge of side weir. Based on dimensional analysis, new explicit equations for estimating the relative discharge for rectangular and triangular side weirs were presented. The results were also compared with previous studies. The capability of ANN, ANFIS, SVR, and GBRT methods was also evaluated in estimating the side weir relative discharge. The ANFIS model for rectangular side weir with MSE = 0.0011 and CP10% = 85.09, and SVR model for triangular side weir with MSE = 0.0011 and CP10% = 87.25 were found to be the superior models in estimating the relative discharge.

Modelling and forecasting cotton production using tuned-support vector regression

India is the largest producer of cotton in the world. For proper planning and designing of policies related to cotton, robust forecast of future production is utmost necessary. In this study, an effort has been made to model and forecast the cotton production of India using tuned-support vector regression (Tuned-SVR) model, and the importance of tuning has also been pointed out through this study. The Tuned-SVR performed better in both modelling and forecasting of cotton pro­duction compared to auto regressive integrated moving average and classical SVR models

Research on Precipitation Forecast Based on LSTM–CP Combined Model

The tremendous progress made in the field of deep learning allows us to accurately predict precipitation and avoid major and long-term disruptions to the entire socio-economic system caused by floods. This paper presents an LSTM–CP combined model formed by the Long Short-Term Memory (LSTM) network and Chebyshev polynomial (CP) as applied to the precipitation forecast of Yibin City. Firstly, the data are fed into the LSTM network to extract the time-series features. Then, the sequence features obtained are input into the BP (Back Propagation) neural network with CP as the excitation function. Finally, the prediction results are obtained. By theoretical analysis and experimental comparison, the LSTM–CP combined model proposed in this paper has fewer parameters, shorter running time, and relatively smaller prediction error than the LSTM network. Meanwhile, compared with the SVR model, ARIMA model, and MLP model, the prediction accuracy of the LSTM–CP combination model is significantly improved, which can aid relevant departments in making disaster response measures in advance to reduce disaster losses and promote sustainable development by providing them data support.

The Stock Index Prediction Based on SVR Model with Bat Optimization Algorithm

Accurate stock market prediction models can provide investors with convenient tools to make better data-based decisions and judgments. Moreover, retail investors and institutional investors could reduce their investment risk by selecting the optimal stock index with the help of these models. Predicting stock index price is one of the most effective tools for risk management and portfolio diversification. The continuous improvement of the accuracy of stock index price forecasts can promote the improvement and maturity of China’s capital market supervision and investment. It is also an important guarantee for China to further accelerate structural reforms and manufacturing transformation and upgrading. In response to this problem, this paper introduces the bat algorithm to optimize the three free parameters of the SVR machine learning model, constructs the BA-SVR hybrid model, and forecasts the closing prices of 18 stock indexes in Chinese stock market. The total sample comes from 15 January 2016 (the 10th trading day in 2016) to 31 December 2020. We select the last 20, 60, and 250 days of whole sample data as test sets for short-term, mid-term, and long-term forecast, respectively. The empirical results show that the BA-SVR model outperforms the polynomial kernel SVR model and sigmoid kernel SVR model without optimized initial parameters. In the robustness test part, we use the stationary time series data after the first-order difference of six selected characteristics to re-predict. Compared with the random forest model and ANN model, the prediction performance of the BA-SVR model is still significant. This paper also provides a new perspective on the methods of stock index forecasting and the application of bat algorithms in the financial field.

Visualization of heavy metal cadmium in lettuce leaves based on wavelet support vector machine regression model and visible‐near infrared hyperspectral imaging

AbstractIn order to effectively detect the distribution of Cd in lettuce leaves, this article proposes a wavelet support vector machine regression (WSVR) modeling method and uses it in the prediction of cadmium (Cd) content in lettuce leaves. In addition, this article also observed the internal and external tissue structure of lettuce leaves under different concentrations of Cd stress by scanning electron microscopy and transmission electron microscopy. Moreover, the lettuce leaf samples under different Cd concentration stresses were acquired by the hyperspectral imaging system, different preprocessing algorithms combined with dimensionality reduction algorithms were used to select the spectral characteristic wavelengths, and SVR and WSVR models were established based on the spectral characteristic data. The results showed that with the increase of Cd concentration, the internal and external tissue structure of lettuce leaves changed significantly. The WSVR model parameters for the best prediction of Cd concentration in lettuce were Rp2 of 0.8843, RMSEP of 0.1292 mg/kg. Finally, the Cd contents in lettuce leaves were visualized on the prediction maps by predicted spectral features on each hyperspectral image pixel.Practical ApplicationsIt is of great significance to realize the visual expression of the distribution of heavy metals in crop leaves through nondestructive testing. In order to demonstrate the feasibility of Vis‐NIR hyperspectral imaging technology for the detection of cadmium (Cd) content in lettuce leaves, scanning electron microscopy and transmission electron microscopy were used to observe the internal and external tissue structure of lettuce leaves under different concentrations of Cd. Wavelet support vector machine regression modeling method was proposed and used to predict the Cd content in lettuce leaves. It is proved that the Vis‐NIR hyperspectral imaging technology is a feasible and effective method to realize the visualization of Cd content distribution in crop leaves.

Natural language processing and machine learning to assist radiation oncology incident learning.

PurposeTo develop a Natural Language Processing (NLP) and Machine Learning (ML) pipeline that can be integrated into an Incident Learning System (ILS) to assist radiation oncology incident learning by semi‐automating incident classification. Our goal was to develop ML models that can generate label recommendations, arranged according to their likelihoods, for three data elements in Canadian NSIR‐RT taxonomy.MethodsOver 6000 incident reports were gathered from the Canadian national ILS as well as our local ILS database. Incident descriptions from these reports were processed using various NLP techniques. The processed data with the expert‐generated labels were used to train and evaluate over 500 multi‐output ML algorithms. The top three models were identified and tuned for each of three different taxonomy data elements, namely: (1) process step where the incident occurred, (2) problem type of the incident and (3) the contributing factors of the incident. The best‐performing model after tuning was identified for each data element and tested on unseen data.ResultsThe MultiOutputRegressor extended Linear SVR models performed best on the three data elements. On testing, our models ranked the most appropriate label 1.48 ± 0.03, 1.73 ± 0.05 and 2.66 ± 0.08 for process‐step, problem‐type and contributing factors respectively.ConclusionsWe developed NLP‐ML models that can perform incident classification. These models will be integrated into our ILS to generate a drop‐down menu. This semi‐automated feature has the potential to improve the usability, accuracy and efficiency of our radiation oncology ILS.

Machine Learning-Based CT Radiomics Analysis for Prognostic Prediction in Metastatic Non-Small Cell Lung Cancer Patients With EGFR-T790M Mutation Receiving Third-Generation EGFR-TKI Osimertinib Treatment.

Background and PurposeAs a third-generation EGFR tyrosine kinase inhibitor (TKI), osimertinib is approved for treating advanced non-small cell lung cancer (NSCLC) patients with EGFR-T790M mutation after progression on first- or second-generation EGFR-TKIs such as gefitinib, erlotinib and afatinib. We aim at exploring the feasibility and effectiveness of using radiomic features from chest CT scan to predict the prognosis of metastatic non-small cell lung cancer (NSCLC) patients with EGFR-T790M mutation receiving second-line osimertinib therapy.MethodsContrast-enhanced and unenhanced chest CT images before osimertinib treatment were collected from 201 and 273 metastatic NSCLC patients with EGFR-T790M mutation, respectively. Radiomic features were extracted from the volume of interest. LASSO regression was used to preliminarily evaluate the prognostic values of different radiomic features. We then performed machine learning-based analyses including random forest (RF), support vector machine (SVM), stepwise regression (SR) and LASSO regression with 5-fold cross-validation (CV) to establish the optimal radiomic model for predicting the progression-free survival (PFS) of osimertinib treatment. Finally, a combined clinical-radiomic model was developed and validated using the concordance index (C-index), decision-curve analysis (DCA) and calibration curve analysis.ResultsDisease progression occurred in 174/273 (63.7%) cases. CT morphological features had no ability in predicting patients’ prognosis in osimertinib treatment. Univariate COX regression followed by LASSO regression analyses identified 23 and 6 radiomic features from the contrast-enhanced and unenhanced CT with prognostic value, respectively. The 23 contrast-enhanced radiomic features were further used to construct radiomic models using different machine learning strategies. Radiomic model built by SR exhibited superior predictive accuracy than RF, SVR or LASSO model (mean C-index of the 5-fold CV: 0.660 vs. 0.560 vs. 0.598 vs. 0.590). Adding the SR radiomic model to the clinical model could remarkably strengthen the C-index of the latter from 0.672 to 0.755. DCA and calibration curve analyses also demonstrated good performance of the combined clinical-radiomic model.ConclusionsRadiomic features extracted from the contrast-enhanced chest CT could be used to evaluate metastatic NSCLC patients’ prognosis in osimertinib treatment. Prognostic models combing both radiomic features and clinical factors had a great performance in predicting patients’ outcomes.

The simple view of reading in French second language learners

This longitudinal study examined the relative contributions of word reading accuracy, word reading fluency and linguistic comprehension to reading comprehension from Grade 1 to Grade 2 in Canadian children who speak French as an L2. French is an opaque orthography but it is more consistent in how graphemes are mapped to phonemes than English. A total of 183 Non-Francophone children enrolled in French immersion programs in Canada (M = 80.91, SD = 3.61) were followed for one year from Grade 1 to Grade 2. The participants completed French measures of word reading accuracy, fluency, receptive vocabulary, and reading comprehension in each grade. The results showed that word reading fluency was a significant predictor of reading comprehension in both Grades 1 and 2. Additionally, word reading accuracy, word reading fluency and vocabulary all contributed to reading comprehension in Grades 1 and 2 and the magnitude of the contribution of each component remained similar across the two grades. Our findings provide support to the SVR model in young French L2 learners and highlight the importance of word reading fluency in reading comprehension in French.

Ultra-short-term Railway traction load prediction based on DWT-TCN-PSO_SVR combined model

The ultra-short-term prediction of traction load is a key aspect of electric power control in electrified railroads. In order to provide more accurate traction load prediction data for the control process, this paper designs a combined prediction method combining Discrete Wavelet Transform(DWT), Temporal Convolutional Network (TCN) and Support Vector Regression based on Particle Swarm Optimization(PSO_SVR) for the characteristics of traction load with strong random fluctuation, large jump amplitude and frequent no-load. This method will use DWT model to decompose the traction load with strong random fluctuation and difficult to predict into sub-series with single fluctuation frequency and easy to predict; then, according to the frequency difference of different series, TCN model is selected to predict medium and low frequency series. The SVR model is selected to predict the high frequency sequences. Finally, the prediction results of each sub-sequence are summed to obtain the final prediction. The combined prediction model designed in this paper is used to predict the traction load of traction power supply stations. The experimental results show that the prediction method proposed in this paper has higher prediction accuracy than the existing prediction methods in the current prediction field.

Predicting shear strength of CFS channels with slotted webs by machine learning models

Staggered rectangular perforations (slots) are provided in the webs of cold-formed steel (CFS) beams and columns to reduce their thermal conductivity and improve the energy efficiency of CFS buildings. The perforations adversely affect the structural characteristics of the members, especially those governed by the web parameters, such as the shear strength and shear buckling. This paper presents machine learning (ML) models to predict the elastic shear buckling load and the ultimate shear strength of CFS channels with slotted webs. Support vector machine (SVM), decision tree (DT), random forest (RF), and k-nearest neighbor (KNN) regressors were trained using a large dataset of numerical results with 3512 samples. An extensive search was conducted to find optimal hyperparameters of the models that result in the best predictions and prevent overfitting. The models’ performances were evaluated employing the ten-fold cross-validation method to make more data available for training and reduce bias and variance. The SVR, DT, and RF models demonstrate good prediction accuracy, which exceeds the accuracy of the existing descriptive equations. Relative feature importance was evaluated using the permutation and SHAP methods for each model. Partial dependence of the buckling load and the shear strength from the channel features was assessed. The predictions of the developed ML models were also compared with the predictions of previously developed artificial neural networks (ANNs). The comparisons demonstrated that ANNs showed higher accuracy in predicting the elastic buckling load, whereas the SVR model provided the most accurate shear strength predictions.

Health Assessment and Remaining Useful Life Prediction of Wind Turbine High-Speed Shaft Bearings

Vibration signals contain abundant information that reflects the health status of wind turbine high-speed shaft bearings ((HSSBs). Accurate health assessment and remaining useful life (RUL) prediction are the keys to the scientific maintenance of wind turbines. In this paper, a method based on the combination of a comprehensive evaluation function and a self-organizing feature map (SOM) network is proposed to construct a health indicator (HI) curve to characterizes the health state of HSSBs. Considering the difficulty in obtaining life cycle data of similar equipment in a short time, the exponential degradation model is selected as the degradation trajectory of HSSBs on the basis of the constructed HI curve, the Bayesian update model, and the expectation–maximization (EM) algorithm are used to predict the RUL of HSSBs. First, the time domain, frequency domain, and time–frequency domain degradation features of HSSBs are extracted. Second, a comprehensive evaluation function is constructed and used to select the degradation features with good performance. Third, the SOM network is used to fuse the selected degradation features to construct a one-dimensional HI curve. Finally, the exponential degradation model is selected as the degradation trajectory of HSSBs, and the Bayesian update and EM algorithm are used to predict the RUL of the HSSB. The monitoring data of a wind turbine HSSB in actual operation is used to validate the model. The HI curve constructed by the method in this paper can better reflect the degradation process of HSSBs. In terms of life prediction, the method in this paper has better prediction accuracy than the SVR model.

GIS-Based Landslide Susceptibility Mapping using Logistic Regression, Instability Index, and Support Vector Machine: Case Study of the Jingshan River, Taiwan

Many techniques have been developed to produce landslide susceptibility maps. However, mapping the spatial distribution of landslide susceptibility involves critical steps. This study evaluated susceptibility to the occurrence of shallow landslides in upstream areas of the Jingshan River, Taiwan, where heavy rainfall has resulted in increased landslide occurrence and reservoir sedimentation. The landslide susceptibility case study was conducted using logistic regression, an instability index method, and support vector machine (SVM). A selection procedure was first developed to identify the factors influencing landslide occurrence. Historical landslide data were used to assess the corresponding parameters of each model. The receiver operating characteristic curve was then used to evaluate the accuracy of model results. The results indicated that the instability index method underestimated landslide susceptibilities in areas near the river. The instability index method was highly affected by the classifications of model factors. Of the models, the SVR model was the most accurate regarding landslide susceptibility in the study area. Employing this framework, governments may implement suitable, cost-effective improvements to structural measures and land-use planning.

Fitting analysis and research of measured data of SAW yarn tension sensor based on PSO–SVR model

With the rapid growth of the SAW (Surface Acoustic Wave) yarn tension sensor, the requirement for its measurement accuracy is higher and higher. However, little research has been conducted in this field. Thus, this paper studies this field and provides a solution. This paper firstly investigates the principle and training of PSO–SVR model. On this basis, this paper also studies the association of output frequency difference data with the matching yarn tension exerted on the SAW yarn tension sensor. After that, employing the frequency difference data as input and corresponding tension as output, the PSO–SVR model is trained and employed to predict output tension of the sensor. Finally, the error with actually applied tension was calculated, the same in the least-squares approach and the BP neural network. By multiple comparisons of the same sample data set in the overall, as well as the local accuracy of the forecasted results, it is easy to confirm that the output error forecast by PSO–SVR model is much smaller relative to the least-squares approach and BP neural network. As a result, a new way for the data analysis of the SAW yarn tension sensor is provided.

Waterlogging Resistance Evaluation Index and Photosynthesis Characteristics Selection: Using Machine Learning Methods to Judge Poplar’s Waterlogging Resistance

Flood disasters are the major natural disaster that affects the growth of agriculture and forestry crops. Due to rapid growth and strong waterlogging resistance characteristics, many studies have explained the waterlogging resistance mechanism of poplar from different perspectives. However, there is no accurate method to define the evaluation index of waterlogging resistance. In addition, there is also a lack of research on predicting the waterlogging resistance of poplars. Based on the changes of poplar biomass and seedling height, the evaluation index of poplar resistance to waterlogging was well determined, and the characteristics of photosynthesis were used to predict the waterlogging resistance of poplars. First, four methods of hierarchical clustering, lasso, stepwise regression and all-subsets regression were used to extract the photosynthesis characteristics. After that, the support vector regression model of poplar resistance to waterlogging was established by using the characteristic parameters of photosynthesis. Finally, the results show that the SVR model based on Stepwise regression and Lasso method has high precision. On the test set, the coefficient of determination (R2) was 0.8581 and 0.8492, the mean square error (MSE) was 0.0104 and 0.0341, and the mean relative error (MRE) was 9.78% and 9.85%, respectively. Therefore, using the characteristic parameters of photosynthesis to predict the waterlogging resistance of poplars is feasible.