A Coke Quality Prediction Model Based on Support Vector Machine

2013 ◽  
Vol 690-693 ◽  
pp. 3097-3101
Author(s):  
Hong Jun Chen ◽  
Jin Feng Bai
Keyword(s):  
Support Vector Machine ◽  
Coke Quality ◽  
Prediction Method ◽  
Support Vector ◽  
Hot Strength ◽  
Neural Network Learning ◽  
Related Data ◽  
Coal Rock ◽  
Hot Strength Of Coke ◽  
Mixed Nature

Due to the complexity of coking coal, as well as the mixed nature of some single coal procured, the error is significantly larger to predict coke quality only through coal conventional indicators. Thus the coking enterprises urgently need a coke prediction method using many blend coal-related data. In view of the complexity of coking, there are some limitations as to the regression prediction method and neural network learning methods. On the base of the conventional indicators of single coal and coal rock indicators, the paper utilizes support vector machine to predict the cold and hot strength of coke. The experiments show that the accurate prediction of this method can meet the requirements of enterprises.

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.

scholarly journals Support Vector Machine-Based Classifier for the Assessment of Finger Movement of Stroke Patients Undergoing Rehabilitation

2019 ◽  
Vol 40 (1) ◽  
pp. 91-100 ◽  
Author(s):  
Toyohiro Hamaguchi ◽  
Takeshi Saito ◽  
Makoto Suzuki ◽  
Toshiyuki Ishioka ◽  
Yamato Tomisawa ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Peak Velocity ◽  
Hand Movement ◽  
Classification Model ◽  
Support Vector ◽  
Leap Motion ◽  
Stroke Patients ◽  
Related Data ◽  
Testing Dataset ◽  
Linear Svm

Abstract Purpose Traditionally, clinical evaluation of motor paralysis following stroke has been of value to physicians and therapists because it allows for immediate pathophysiological assessment without the need for specialized tools. However, current clinical methods do not provide objective quantification of movement; therefore, they are of limited use to physicians and therapists when assessing responses to rehabilitation. The present study aimed to create a support vector machine (SVM)-based classifier to analyze and validate finger kinematics using the leap motion controller. Results were compared with those of 24 stroke patients assessed by therapists. Methods A non-linear SVM was used to classify data according to the Brunnstrom recovery stages of finger movements by focusing on peak angle and peak velocity patterns during finger flexion and extension. One thousand bootstrap data values were generated by randomly drawing a series of sample data from the actual normalized kinematics-related data. Bootstrap data values were randomly classified into training (940) and testing (60) datasets. After establishing an SVM classification model by training with the normalized kinematics-related parameters of peak angle and peak velocity, the testing dataset was assigned to predict classification of paralytic movements. Results High separation accuracy was obtained (mean 0.863; 95% confidence interval 0.857–0.869; p = 0.006). Conclusion This study highlights the ability of artificial intelligence to assist physicians and therapists evaluating hand movement recovery of stroke patients.

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.

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