scholarly journals PALMPRINT RECOGNITION BASED ON DISCRETE CURVELET TRANSFORM AND SUPPORT VECTOR MACHINE

2010 ◽  
Vol 28 (6) ◽  
pp. 456-460 ◽  
Author(s):  
Xue-Bin XU ◽  
De-Yun ZHANG ◽  
Xin-Man ZHANG ◽  
Yang-Jie CAO
Keyword(s):  
Support Vector Machine ◽  
Curvelet Transform ◽  
Palmprint Recognition ◽  
Support Vector

scholarly journals A Novel Framework for Detection of Cervical Cancer

2018 ◽  
Vol 7 (2) ◽  
pp. 26-30
Author(s):  
V. Pushpalatha
Keyword(s):  
Cervical Cancer ◽  
Support Vector Machine ◽  
Curvelet Transform ◽  
Uterine Cervical Cancer ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Gray Level ◽  
Adaptive Support ◽  
Occurrence Matrix ◽  
Five Phases

Today, Uterine Cervical Cancer is most general form of cancer for women. Prevention of cervical cancer is possible via various screening courses. Colposcopy images of cervix are analyzed in this study for the recognition of cervical cancer. An innovative framework is suggested to correctly identify cervical cancer by employing effective pre-processing, image enhancement, and image segmentation techniques. This framework comprises of five phases, (i) Dual tree discrete wavelet transform to pre-process the image (ii) Curvelet transform and contour transform to enhance the image (iii) K-means for segmentation (iv) features computation using Gray level co-occurrence matrix (v) classification using adaptive Support vector machine. The experimental results evident that proposed technique is superior to existing methodologies.

scholarly journals Energy Saving Diagnosis Model of Petrochemical Plant Based on Intelligent Curvelet Support Vector Machine

2021 ◽  
Author(s):  
bin zhao ◽  
dou qin ◽  
Diankui Gao ◽  
lizhi xu
Keyword(s):  
Support Vector Machine ◽  
Energy Saving ◽  
Simulation Analysis ◽  
Diagnostic System ◽  
Curvelet Transform ◽  
Theory Model ◽  
Support Vector ◽  
Petrochemical Plant ◽  
Time Operation ◽  
Saving Effect

Abstract The energy and resources saving has become a major task of petrochemical enterprises, it is necessary to construct the energy saving diagnostic system for understand the real time operation information of petrochemical plant and provide theoretical basis for taking energy saving measures. The energy saving diagnosis process of petrochemical plant based on twin Curvelet support vector machine optimized by hybrid glowworm swarm algorithm is designed. The Curvelet kernel function is constructed based on curvelet transform to establish theory model of twin curvelet support vector machine. In order to improve the prediction precision of the twin curvelet support vector machine, the hybrid glowworm swarm optimization algorithm is constructed based on simulated annealing simulation to optimize the parameters of the twin Curvelet support vector machine. Finally, a petrochemical plant is used as research object to carry out diagnosis simulation analysis, and results showed that the proposed prediction model can effectively improve diagnostic effectiveness of the energy saving effect of petrochemical plant.

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.

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