Squares-support Vector Machine Model Research Articles

Improved Least Squares Support Vector Machine Model Based on Grey Wolf Optimizer Algorithm for Predicting CO2–Crude Oil Minimum Miscibility Pressure

The minimum miscibility pressure (MMP) is an important reference parameter in the study of CO2 oil drive systems. In response to the problems of time‐consuming and costly prediction of MMP by conventional experimental methods, an improved least squares support vector machine (LSSVM) model based on grey wolf optimizer (GWO) algorithm is proposed to predict the CO2–crude oil MMP. Based on Pearson correlation analysis, reservoir temperature, C5+ molecular weight, intermediate component mole fraction, and volatile component mole fraction are selected as independent variables of the model, and MMP is the dependent variable. A total of 51 MMP experimental data are collected, of which 35 are used to fine‐tune the model's parameters and 16 are used to verify the model's reliability. The high leverage point method is used to detect anomalies in all experimental data to check the reliability of the model, and the abnormality of only one piece of experimental data is identified. Finally, a comparison of the model with other intelligent models is found. The absolute relative deviation of the GWO‐LSSVM model is 2.24% for the training data and 3.48% for the test data, which provides high adaptability and accuracy.

Klasifikasi Nasabah Potensial menggunakan Algoritma Ensemble Least Square Support Vector Machine dengan AdaBoost

In the era of business and economics that are interconnected with each other and competition between companies in seeking market share so that there will be an increase, especially in the number of customers, especially deposit customers, financial institutions and other companies are increasingly realizing the importance of understanding and identifying potential customers correctly to get potential customers. customers subscribe to deposits. Potential customer classification is a strategic approach that allows financial institutions to identify potential customers who have the potential to subscribe to deposits. With a deeper understanding of the characteristics and needs of potential customers, financial institutions can direct marketing resources more effectively, increase marketing efforts, and increase the conversion of potential customers to active customers. The aim of this research is to develop and test the Ensemble Least Square Support Vector Machine model with AdaBoost in classifying potential customers which can increase accuracy in identifying potential customers who have the potential to subscribe to deposits. The research results showed that this method achieved an accuracy of 95.15%, a sensitivity of 92.93%, and a specificity of 97.61%. In comparison with single Support Vector Machine and Least Squares Support Vector Machine models, the Ensemble Least Squares Support Vector Machine outperforms both in terms of accuracy.

A Hybrid Random Forest and Least Squares Support Vector Machine Model Based on Particle Swarm Optimization Algorithm for Slope Stability Prediction: A Case Study in Sichuan–Tibet Highway, China

Slope stability estimation is an engineering problem that involves several parameters. The problems of low accuracy of the model and blind data preprocessing are commonly existent in slope stability prediction research. To address these problems, 10 quantitative indicators are selected from 135 field cases to improve the accuracy of the model. These indicators were analyzed and visualized to examine their reliabilities after preprocessing. Combined with random forest (RF), particle swarm optimization (PSO), and least squares support vector machine (LSSVM) algorithms, a hybrid prediction model that the RF–PSO–LSSVM model is proposed for identifying slope stability, and its reliability is verified by other prediction models that SVM, logistic regression, decision trees, k-nearest neighbor, naive Bayes, and linear discriminant analysis. Besides, the importance score of each indicator in the prediction of slope stability is discussed by employing the RF algorithm. The research results show that the proposed hybrid model exhibits the best accuracy and superiority in slope stability prediction than other models in this paper, which its values of the best fitness, area under the curve, T-measure, and accuracy are 98.15%, 96.4%, 96.55%, and 95.82%, respectively. The most influential factors affecting slope stability are precipitation and gravity, and the slope type and pore water ratio are identified as the least significant factors in this paper. The results provide a novel approach toward slope stability prediction in the field of geotechnical engineering.

Predicting Ink Transfer Rate of 3D Additive Printing Using EGBO Optimized Least Squares Support Vector Machine Model

Ink transfer rate (ITR) is a reference index to measure the quality of 3D additive printing. In this study, an ink transfer rate prediction model is proposed by applying the least squares support vector machine (LSSVM). In addition, enhanced garden balsam optimization (EGBO) is used for selection and optimization of hyperparameters that are embedded in the LSSVM model. 102 sets of experimental sample data have been collected from the production line to train and test the hybrid prediction model. Experimental results show that the coefficient of determination (R2) for the introduced model is equal to 0.8476, the root-mean-square error (RMSE) is 6.6 × 10 (−3), and the mean absolute percentage error (MAPE) is 1.6502 × 10 (−3) for the ink transfer rate of 3D additive printing.

Modelling postharvest quality of blueberry affected by biological variability using image and spectral data.

Hyperspectral reflectance and transmittance sensing as well as near-infrared (NIR) spectroscopy were investigated as non-destructive tools for estimating blueberry firmness, elastic modulus and soluble solid content (SSC). Least squares-support vector machine models were established from these three spectra based on samples from three cultivars viz. Bluecrop, Duke and M2 and two harvest years viz. 2014 and 2015 for predicting blueberry postharvest quality. One-cultivar reflectance models (establishing model using one cultivar) derived better results than the corresponding transmittance and NIR models for predicting blueberry firmness with few cultivar effects. Two-cultivar NIR models (establishing model using two cultivars) proved to be suitable for estimating blueberry SSC with correlations over 0.83. Rp (RMSEp ) values of the three-cultivar reflectance models (establishing model using 75% of three cultivars) were 0.73 (0.094) and 0.73 (0.186), respectively , for predicting blueberry firmness and elastic modulus. For SSC prediction, the three-cultivar NIR model was found to achieve an Rp (RMSEp ) value of 0.85 (0.090). Adding Bluecrop samples harvested in 2014 could enhance the three-cultivar model robustness for firmness and elastic modulus. The above results indicated the potential for using spatial and spectral techniques to develop robust models for predicting blueberry postharvest quality containing biological variability. © 2015 Society of Chemical Industry.

Endpoint Detection in Low Open Area TSV Fabrication Using Optical Emission Spectroscopy

Through-silicon via (TSV) technology is a key enabler for 3-D and 2.5-D integration, which provides low-power and high-bandwidth chip-to-chip communication. During TSV fabrication, over-etching may cause notching at the base of the TSVs, resulting in TSV diameter variations. Endpoint detection (EPD) techniques are critical for controlling TSV diameter, and detecting the endpoint for low open areas presents a serious challenge to process engineers. In this paper, a hybrid partial least squares-support vector machine model for optical emission spectroscopy data are successfully demonstrated for an EPD of low open area TSVs. Accurate EPD results are shown for 120, 80, and 25 \(\mu\) m diameter TSVs.

Multi-Agent Dam Management Model Based on Improved Reinforcement Learning Technology

In order to achieve efficient management of the dam, the new algorithms such as reinforcement learning, Synergetic, Structural Risk Minimization and Particle Swarm Optimization are used to establish a Cooperative Wavelet Least Squares Support Vector Machine Model. To improve the convergence rate and make full use of knowledge and advice of mechanics and hydraulics of the dam, WLS-SVRM and WLS-SVCM models are used cooperatively. Before the training online, mapping provides training samples for WLS-SVCM. During the course of training online, the numerical simulation and WLS-SVCM will provide knowledge and advices for WLS-SVRM. Case study shows that the model can provide timely information of gate opening and management information of the dam so as to provide decision support for engineering management.

Variety Identification of Rice Vinegars Using Visible and Near Infrared Spectroscopy and Multivariate Calibrations

Visible and near infrared spectroscopy was investigated to identify the varieties of rice vinegars based on back propagation neural network and least squares-support vector machine. Five varieties of rice vinegars were prepared. Partial least squares discriminant analysis was implemented for calibration and extraction of partial least squares factors. The factors were used as inputs of back propagation neural network and least squares-support vector machine. Finally, back propagation neural network and least squares-support vector machine models were achieved. The threshold value of prediction was set as ±0.1. An excellent precision and recognition ratio of 100% was achieved by both methods. Simultaneously, certain effective wavelengths were proposed by x-loading weights and regression coefficients. The performance of effective wavelengths was validated and an acceptable result was achieved. The results indicated that visible and near infrared spectroscopy could be used as a rapid and high precision method for the identification of different varieties of rice vinegars.

English

The content of flavonoids and phenolic acids in the extract of bamboo leaves (EBL) was determined using near-infrared spectroscopy. Optimal partial least squares models and least squares-support vector machine models were developed to enable rapid and non-destructive validation based on EBL contents as determined by photocolorimetric methods and high performance liquid chromatography. The least squares-support vector machine models performed slightly better than the partial least squares models. For least squares-support vector machine models, the correlation coefficients (R) and root mean square errors (RMSE) of calibration and validation were R(cal)= 0.9998, RMSE(cal)= 0.05, R(val)= 0.9418 and RMSE(val)= 3.91 for total flavonoids (TF) and R(cal)= 0.9778, RMSE(cal)= 2.49, R(val)= 0.9535, and RMSE(val)= 3.61 for total phenolic acids (TP). For optimal partial least squares models, the corresponding values were 0.9962, 1.12, 0.9124 and 4.97 for TF and 0.9966, 0.98, 0.9325 and 4.33 for TP. The results indicated that near-infrared spectroscopy with least squares-support vector machine could be utilized as a precise method for the determination of flavonoids and phenolic acids in EBL.

Usage of artificial intelligence methods in free flowing gated closed conduits for estimation of oxygen transfer efficiency

Oxygen transfer is the process which oxygen is transferred from the gaseous to the liquid phase. The oxygen transfer efficiency depends almost entirely on the amount of surface contact between the air and water. This surface contact can be increased by conduit flow that involves air-water mixture flow. In reality, the physical structure of the air-water interface is complex and still awaits clarification. In the past few years, many artificial intelligence methods have been successfully applied to the solution of complex problems. In this study, models based on Adaptive Network based Fuzzy Inference Systems and Least Squares Support Vector Machines methods were developed to predict oxygen transfer efficiency in free flowing gated closed conduits. Experimental results were compared with the results of these artificial intelligence methods. The best performance was obtained with the Least Squares Support Vector Machine model. Average correlation coefficient (R^2) and average root mean square error (RMSE) in the Least Squares Support Vector Machine model were achieved equal to 0.9927 and 0.0073, respectively. Extremely good agreement between the predicted and measured values proves the validity of the Least Squares Support Vector Machine model.