scholarly journals Intelligent breakout prediction method based on support vector machine

2020 ◽  
Vol 1653 ◽  
pp. 012052
Author(s):  
Yuanpeng Tian ◽  
Yu Liu
Keyword(s):  
Support Vector Machine ◽  
Prediction Method ◽  
Support Vector

scholarly journals Voting Based Classification Method for Diabetes Prediction

2019 ◽  
Vol 8 (2S6) ◽  
pp. 913-918
Keyword(s):  
Support Vector Machine ◽  
Naive Bayes ◽  
Research Work ◽  
Prediction Method ◽  
Naïve Bayes ◽  
Support Vector ◽  
Decision Tree Classifier ◽  
Prediction Analysis ◽  
Tree Classifier ◽  
Diabetes Prediction

This research work is based on the diabetes prediction analysis. The prediction analysis technique has the three steps which are dataset input, feature extraction and classification. In this previous system, the Support Vector Machine and naïve bayes are applied for the diabetes prediction. In this research work, voting based method is applied for the diabetes prediction. The voting based method is the ensemble based which is applied for the diabetes prediction method. In the voting method, three classifiers are applied which are Support Vector Machine, naïve bayes and decision tree classifier. The existing and proposed methods are implemented in python and results in terms of accuracy, precision-recall and execution time. It is analyzed that voting based method give high performance as compared to other classifiers.

CPI Big Data Prediction Based on Wavelet Twin Support Vector Machine

2020 ◽  
pp. 2159013
Author(s):  
Yiqing Fan ◽  
Zhihui Sun
Keyword(s):  
Support Vector Machine ◽  
Big Data ◽  
Consumer Price Index ◽  
Low Frequency ◽  
Prediction Method ◽  
Twin Support Vector Machine ◽  
Support Vector ◽  
Reconstruction Method ◽  
Data Prediction ◽  
Svm Model

In order to effectively improve the accuracy of Consumer Price Index (CPI) prediction so as to more truly reflect the overall level of the country’s macroeconomic situation, a CPI big data prediction method based on wavelet twin support vector machine (SVM) is proposed. First, the historical CPI data are decomposed into high-frequency part and low-frequency part by wavelet transform. Then a more advanced twin SVM is used to build a prediction model to obtain two kinds of prediction results. Finally, the wavelet reconstruction method is used to fuse the two kinds of prediction results to obtain the final CPI prediction results. The wavelet twin SVM model is used to fit and predict CPI index. Experimental results show that compared with the similar prediction methods, the proposed prediction method has higher fitting accuracy and smaller root mean square error.

Gas Pipeline Corrosion Prediction Based on Modified Support Vector Machine and Unequal Interval Model

2013 ◽  
Vol 373-375 ◽  
pp. 1987-1994 ◽  
Author(s):  
Wei Dong Zhang ◽  
Bin Shen ◽  
Yi Bo Ai ◽  
Bin Yang
Keyword(s):  
Support Vector Machine ◽  
Oil And Gas ◽  
Prediction Method ◽  
Original Data ◽  
Gas Pipeline ◽  
Prediction Algorithm ◽  
Support Vector ◽  
Grey Prediction ◽  
Interval Model ◽  
Pipeline Corrosion

The corrosion is an important problem for the service safety of oil and gas pipeline. This research focuses. This paper proposed a new prediction algorithm on corrosion prediction of gathering gas pipeline, which combined modified Support Vector Machine (SVM) with unequal interval model. Firstly, grey prediction method with unequal interval model was used to pretreatment original data because there is unequal interval problem in actual collected data of pipeline. Secondly, improved Support Vector Regression (SVR) based on Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) has been proposed to resolve parameters selection problem for SVR. Finally, the corrosion prediction model of gas pipeline has been proposed which combined improved SVR and unequal interval grey prediction method. The experiment results show this algorithm could increase precision of the pipeline corrosion prediction compared with the traditional SVM. This research provides reliable basis for in-service pipeline life prediction and confirming inspecting cycle.

A threshold sensitive failure prediction method using support vector machine

2017 ◽  
Vol 13 (2) ◽  
pp. 97-111 ◽  
Author(s):  
Ahmad Fadaei Tehrani ◽  
Faramarz Safi-Esfahani
Keyword(s):  
Support Vector Machine ◽  
Failure Prediction ◽  
Prediction Method ◽  
Support Vector

Process Parameter Prediction of Differential Pressure Vacuum Casting Based on Support Vector Machine

2014 ◽  
Vol 621 ◽  
pp. 633-638 ◽  
Author(s):  
Xu Zhang ◽  
Hai Guang Zhang ◽  
Zhuang Ya Zhang ◽  
Qing Xi Hu
Keyword(s):  
Support Vector Machine ◽  
Quality Control ◽  
Product Quality ◽  
Predictive Accuracy ◽  
Prediction Method ◽  
Differential Pressure ◽  
Support Vector ◽  
Orthogonal Experimental Design ◽  
Vacuum Casting ◽  
Artificial Fish Swarm Algorithm

To reduce warpage deformation of the differential pressure vacuum casting (DPVC) products and to improve product quality, One prediction method for process parameters based on support vector machine (SVM) and artificial fish-swarm algorithm (AFSA) is proposed.Firstly sample test data is abtained by using orthogonal experimental design and numerical simulation to construct models to forecast warpage of DPVC product based on SVM. Simultaneously to improve the predictive accuracy of the model, AFSA is introduced to optimize the SVM model. And then using this model recommends and adjusts the DPVC process in order to achieve quality control. Finally , through the analysis of a mouse shell , the validity of the method proposed is verified, providing a feasible method for DPVC product quality control

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