scholarly journals Electroencephalography based Emotion Recognition using Fisher’s Linear Discriminant Analysis on Support Vector Machine

2020 ◽  
Vol 1577 ◽  
pp. 012004
Author(s):  
I N Yulita ◽  
D Novita ◽  
A Sholahuddin ◽  
Emilliano
Keyword(s):  
Support Vector Machine ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Emotion Recognition ◽  
Support Vector ◽  
Linear Discriminant ◽  
Fisher’S Linear Discriminant

Multi-scale Entropy and Multiclass Fisher’s Linear Discriminant for Emotion Recognition Based on Multimodal Signal

2020 ◽  
pp. 71-78
Author(s):  
Lutfi Hakim ◽  
Sepyan Purnama Kristanto ◽  
Alfi Zuhriya Khoirunnisaa ◽  
Adhi Dharma Wibawa
Keyword(s):  
Support Vector Machine ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Emotion Recognition ◽  
Pulse Rate ◽  
Dimensional Reduction ◽  
Physiological Signals ◽  
Support Vector ◽  
Linear Discriminant ◽  
Fisher’S Linear Discriminant

Emotion recognition using physiological signals has been a special topic frequently discussed by researchers and practitioners in the past decade. However, the use of SpO2 and Pulse rate signals for emotion recognitionisvery limited and the results still showed low accuracy. It is due to the low complexity of SpO2 and Pulse rate signals characteristics. Therefore, this study proposes a Multiscale Entropy and Multiclass Fisher’s Linear Discriminant Analysis for feature extraction and dimensional reduction of these physiological signals for improving emotion recognition accuracy in elders.  In this study, the dimensional reduction process was grouped into three experimental schemes, namely a dimensional reduction using only SpO2 signals, pulse rate signals, and multimodal signals (a combination feature vectors of SpO2 and Pulse rate signals). The three schemes were then classified into three emotion classes (happy, sad, and angry emotions) using Support Vector Machine and Linear Discriminant Analysis Methods. The results showed that Support Vector Machine with the third scheme achieved optimal performance with an accuracy score of 95.24%. This result showed a significant increase of more than 22%from the previous works.

scholarly journals Raman Microspectral Study and Classification of the Pathological Evolution of Breast Cancer Using Both Principal Component Analysis-Linear Discriminant Analysis and Principal Component Analysis-Support Vector Machine

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Heping Li ◽  
Yu Ren ◽  
Fan Yu ◽  
Dongliang Song ◽  
Lizhe Zhu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Principal Component Analysis ◽  
Support Vector Machine ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Ductal Carcinoma ◽  
Principal Component ◽  
Component Analysis ◽  
Support Vector ◽  
Linear Discriminant

To facilitate the enhanced reliability of Raman-based tumor detection and analytical methodologies, an ex vivo Raman spectral investigation was conducted to identify distinct compositional information of healthy (H), ductal carcinoma in situ (DCIS), and invasive ductal carcinoma (IDC). Then, principal component analysis-linear discriminant analysis (PCA-LDA) and principal component analysis-support vector machine (PCA-SVM) models were constructed for distinguishing spectral features among different tissue groups. Spectral analysis highlighted differences in levels of unsaturated and saturated lipids, carotenoids, protein, and nucleic acid between healthy and cancerous tissue and variations in the levels of nucleic acid, protein, and phenylalanine between DCIS and IDC. Both classification models were principal component analysis-linear discriminant analysis to be extremely efficient on discriminating tissue pathological types with 99% accuracy for PCA-LDA and 100%, 100%, and 96.7% for PCA-SVM analysis based on linear kernel, polynomial kernel, and radial basis function (RBF), respectively, while PCA-SVM algorithm greatly simplified the complexity of calculation without sacrificing performance. The present study demonstrates that Raman spectroscopy combined with multivariate analysis technology has considerable potential for improving the efficiency and performance of breast cancer diagnosis.

scholarly journals ELECTRONIC NOSE FOR ANCHOVY FRESHNESS MONITORING BASED ON SENSOR ARRAY AND PATTERN RECOGNITION METHODS: PRINCIPAL COMPONENTS ANALYSIS, LINEAR DISCRIMINANT ANALYSIS AND SUPPORT VECTOR MACHINE

2014 ◽  
pp. 61-67
Author(s):  
A. Amari ◽  
N. El Bari ◽  
B. Bouchikhi
Keyword(s):  
Support Vector Machine ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Electronic Nose ◽  
Tin Dioxide ◽  
Principal Component ◽  
Support Vector ◽  
Current Pattern ◽  
Linear Discriminant ◽  
Pattern Recognition Methods

An electronic nose based system, which employs an array of six inexpensive commercial gas sensors based on tin dioxide (Figaro Engineering Inc., Japan), has been used to analyse the freshness states of anchovies. Fresh anchovies were stored in a refrigerator at 4 ± 1°C over a period of 15 days. Electronic nose measurements need no sample preparation and the results indicated that the spoilage process of anchovies could be followed by using this technique. Conductance responses of volatile compounds produced during storage of anchovy were monitored and the result were analysed by multivariate analysis methods. In this paper principal component analysis (PCA) and linear discriminant analysis (LDA) were used to investigate whether the electronic nose was able to distinguishing among different freshness states (fresh, moderated and non-fresh samples). The loadings analysis was used to identify the sensors responsible for discrimination in the current pattern file. Therefore, the support vector machines (SVM) method was applied to the new subset, with only the selected sensors, to confirm that a subset of a few sensors can be chosen to explain all the variance. The results obtained prove that the electronic nose can discriminate successfully different freshness state using LDA analysis. Some sensors have the highest influence in the current pattern file for electronic nose. Support vector machine (SVM) model, applied to the new subset of sensors show the good performance.

Sign in / Sign up

Export Citation Format

Share Document