An Improved Method of Support Vector Machine Algorithm with Choosing Segmentation in Electrical Capacitance Tomography

2013 ◽  
Vol 12 (7) ◽  
pp. 1454-1458
Author(s):  
Yan Li ◽  
Xiao-Hua Yuan ◽  
Jing-Song Liu ◽  
Guo-Hua Zhu ◽  
An-Na Yuan
2019 ◽  
Vol 15 (2) ◽  
pp. 275-280
Author(s):  
Agus Setiyono ◽  
Hilman F Pardede

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam.  One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.


2015 ◽  
Vol 77 (28) ◽  
Author(s):  
MT Masturah ◽  
MHF Rahiman ◽  
Zulkarnay Zakaria ◽  
AR Rahim ◽  
NM Ayob

This paper discussed the design–functionality and application of Flexible Electrical Capacitance Tomography sensor (FlexiECT). The sensors consist of 12 electrodes allocated surrounding the outer layer of the pipeline. The sensor is designed in such that the flexibility features suit the applications in the pipeline of multiple size. This paper also discussed the preliminary result of FlexiECT applications in fluid imaging by identifying the percentage of two mixing fluids.


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