scholarly journals Application of support vector machine modeling for prediction of common diseases: the case of diabetes and pre-diabetes

2010 ◽  
Vol 10 (1) ◽  
Author(s):  
Wei Yu ◽  
Tiebin Liu ◽  
Rodolfo Valdez ◽  
Marta Gwinn ◽  
Muin J Khoury
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Common Diseases ◽  
Machine Modeling

Application of Support Vector Machine Modeling on Phase Distribution in the Riser of an LSCFB Reactor

2014 ◽  
Vol 12 (1) ◽  
pp. 123-134 ◽  
Author(s):  
Shaikh A. Razzak ◽  
Muhammad I. Hossain ◽  
Syed M. Rahman ◽  
Mohammad M. Hossain
Keyword(s):  
Support Vector Machine ◽  
Circulating Fluidized Bed ◽  
Phase Distribution ◽  
Solid Phase ◽  
Correlation Coefficients ◽  
Absolute Error ◽  
Pilot Scale ◽  
Support Vector ◽  
Cross Sectional ◽  
Machine Modeling

Abstract Support vector machine (SVM) modeling approach is applied to predict the solids holdups distribution of a liquid–solid circulating fluidized bed (LSCFB) riser. The SVM model is developed/trained using experimental data collected from a pilot-scale LSCFB reactor. Two different size glass bead particles (500 μm (GB-500) and 1,290 μm (GB-1290)) are used as solid phase, and water is used as liquid phase. The trained model successfully predicted the experimental solids holdups of the LSCFB riser under different operating parameters. It is observed that the model predicted cross-sectional average of solids holdups in the axial directions and radial flow structure are well agreement with the experimental values. The goodness of the model prediction is verified by using different statistical performance indicators. For the both sizes of particles, the mean absolute error is found to be less than 5%. The correlation coefficients (0.998 for GB-500 and 0.994 for GB-1290) also show favorable indications of the suitability of SVM approach in predicting the solids holdup of the LSCFB system.

Sign in / Sign up

Export Citation Format

Share Document