Performance enhancement of respiratory tumor motion prediction using adaptive support vector regression: Comparison with adaptive neural network method

2014 ◽  
Vol 24 (1) ◽  
pp. 8-15 ◽  
Author(s):  
SeungWook Choi ◽  
Yongjun Chang ◽  
Namkug Kim ◽  
Sung Ho Park ◽  
Si Yeol Song ◽  
...  
Keyword(s):  
Neural Network ◽  
Support Vector Regression ◽  
Performance Enhancement ◽  
Support Vector ◽  
Motion Prediction ◽  
Tumor Motion ◽  
Adaptive Neural Network ◽  
Neural Network Method ◽  
Network Method ◽  
Adaptive Support

Speech Recognization Based on Support Vector Machine

2012 ◽  
Vol 433-440 ◽  
pp. 7516-7521
Author(s):  
Ling Zhang
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Speech Recognition ◽  
Bp Neural Network ◽  
Speech Signal ◽  
Local Minimum ◽  
Support Vector ◽  
Neural Network Method ◽  
Network Method ◽  
Fast Classification

Aiming at the deficiency of the local minimum occurring in neural network used for speech recognition, the paper employs support vector machine (SVM) to recognize the speech signal with four different components. First, SVM is utilized to perform the speech recognition. Then, the results are compared with those obtained by the BP neural network method. The comparison shows that SVM effectively overcomes the local minimum existing in neural network and has the advantages of the accurate and fast classification, indicating that SVM looks feasible to recognize the speech signal.

scholarly journals World Covid-19 Vaccine Names Classification Using Neural Network Method

2021 ◽  
Vol 1 (1) ◽  
pp. 31
Author(s):  
Kristiawan Nugroho
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Support Vector ◽  
Neural Network Method ◽  
The Public ◽  
Machine Learning Methods ◽  
The Neural Network ◽  
The World ◽  
Network Method

The Covid-19 pandemic has occurred for a year on earth. Various attempts have been made to overcome this pandemic, especially in making various types of vaccines developed around the world. The level of vaccine effectiveness in dealing with Covid-19 is one of the questions that is often asked by the public. This research is an attempt to classify the names of vaccines that have been used in various nations by using one of the robust machine learning methods, namely the Neural Network. The results showed that the Neural Network method provides the best accuracy, which is 99.9% higher than the Random Forest and Support Vector Machine(SVM) methods.

scholarly journals Application of the Support Vector Machine and Neural Network Model Based on Particle Swarm Optimization for Breast Cancer Prediction

SinkrOn ◽  
2019 ◽  
Vol 4 (1) ◽  
pp. 66 ◽  
Author(s):  
Esty Purwaningsih
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Network Model ◽  
Neural Network Model ◽  
Support Vector ◽  
Swarm Optimization ◽  
Neural Network Method ◽  
Network Method

There are several studies in the medical field that classify data to diagnose and analyze decisions. To predict breast cancer, this study compares two methods, the Support Vector Machine method and the Neural Network method based on Particle Swarm Optimization (PSO) which is intended to determine the highest accuracy value in the Coimbra dataset data. To implement the Support Vector Machine and Neural Network method based on PSO, RapidMiner software is used. Then the application results are compared using Confusion Matrix and ROC Curve. Based on the accuracy of the two models, it is known that the PSO-based Neural Network model has a higher accuracy value of 84.55% than the results of the PSO-based Vector Support Machine with an accuracy value of 80.08%. The calculation results, the accuracy of the AUC performance obtained by the results of the study are, the two methods are PSO-based Neural Network with AUC value of 0.885 and PSO-based Support Vector Machine with a value of 0.819 included in the category of Good Classification.

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