Smart Calibration Technique for Auto-ranging of LVDT Using Support Vector Machine

2018 ◽  
pp. 549-560
Author(s):  
K. V. Santhosh ◽  
Preeti Mohanty
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Calibration Technique

The Analysis of Steganographic by Sub-Pixel Calibration

2014 ◽  
Vol 599-601 ◽  
pp. 1753-1756
Author(s):  
Qian Chen ◽  
Jing Xin Hong ◽  
Gang Ou ◽  
Xu Chen ◽  
Ling Wang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
High Accuracy ◽  
Experimental Results ◽  
Support Vector ◽  
Calibration Technique ◽  
Stego Image ◽  
Class Separation ◽  
Nonlinear Classifier ◽  
Blind Steganalysis

In order to achieve high accuracy, we present a new calibration technique aimed at blind steganalysis. The calibration can be considered as a preprocessing of stego image before extracting the features. The extracted features will be more effective for our classification [1] Moreover, the calibrated feature was used to train SVM (Support Vector Machine)[2], a nonlinear classifier, which is effective in class separation. For comparison, we conducted extensive experiments and drawn a conclusion that the steganalytic scheme based on our novel calibration can detect the stego information with high accuracy. Experimental results demonstrate that our proposed scheme outperforms the best effective JPEG steganalysis having been presented.

Support vector machine? – not only for MRI-images

2020 ◽  
Author(s):  
V Vasilevska ◽  
K Schlaaf ◽  
H Dobrowolny ◽  
G Meyer-Lotz ◽  
HG Bernstein ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Support Vector

KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

2019 ◽  
Vol 15 (2) ◽  
pp. 275-280
Author(s):  
Agus Setiyono ◽  
Hilman F Pardede
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Spam Detection ◽  
Support Vector Machine Algorithm ◽  
Data Mining Techniques ◽  
To Receive

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.

Classification of Fatigued Bills Based on a Support Vector Machine by Using the Amplitude Difference Features of Acoustic Signals

2011 ◽  
Vol 131 (8) ◽  
pp. 1495-1501
Author(s):  
Dongshik Kang ◽  
Masaki Higa ◽  
Hayao Miyagi ◽  
Ikugo Mitsui ◽  
Masanobu Fujita ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Acoustic Signals ◽  
Support Vector ◽  
Amplitude Difference

An Accurate Packer Identification Method Using Support Vector Machine

2014 ◽  
Vol E97.A (1) ◽  
pp. 253-263 ◽  
Author(s):  
Ryoichi ISAWA ◽  
Tao BAN ◽  
Shanqing GUO ◽  
Daisuke INOUE ◽  
Koji NAKAO
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Identification Method
Sign in / Sign up

Export Citation Format

Share Document