Classification of colored spun fabric structure based on wavelet decomposition and hierarchical hybrid classifier

2021 ◽  
pp. 1-6
Author(s):  
Xue Gong ◽  
Li Yuan ◽  
Yali Yang ◽  
Junping Liu ◽  
Muli Liu
Keyword(s):  
Wavelet Decomposition ◽  
Hybrid Classifier ◽  
Fabric Structure

scholarly journals A Novel Hybrid Machine Learning Classification for the Detection of Bruxism Patients Using Physiological Signals

2020 ◽  
Vol 10 (21) ◽  
pp. 7410
Author(s):  
Md Belal Bin Heyat ◽  
Faijan Akhtar ◽  
Asif Khan ◽  
Alam Noor ◽  
Bilel Benjdira ◽  
...  
Keyword(s):  
Home Monitoring ◽  
Physiological Signals ◽  
Sleep Stages ◽  
Monitoring Systems ◽  
The Novel ◽  
Hybrid Classifier ◽  
Machine Learning Classification ◽  
Power Spectral ◽  
Automatic Tool

Bruxism is a sleep disorder in which the patient clinches and gnashes their teeth. Bruxism detection using traditional methods is time-consuming, cumbersome, and expensive. Therefore, an automatic tool to detect this disorder will alleviate the doctor workload and give valuable help to patients. In this paper, we targeted this goal and designed an automatic method to detect bruxism from the physiological signals using a novel hybrid classifier. We began with data collection. Then, we performed the analysis of the physiological signals and the estimation of the power spectral density. After that, we designed the novel hybrid classifier to enable the detection of bruxism based on these data. The classification of the subjects into “healthy” or “bruxism” from the electroencephalogram channel (C4-A1) obtained a maximum specificity of 92% and an accuracy of 94%. Besides, the classification of the sleep stages such as the wake (w) stage and rapid eye movement (REM) stage from the electrocardiogram channel (ECG1-ECG2) obtained a maximum specificity of 86% and an accuracy of 95%. The combined bruxism classification and the sleep stages classification from the electroencephalogram channel (C4-P4) obtained a maximum specificity of 90% and an accuracy of 97%. The results show that more accurate bruxism detection is achieved by exploiting the electroencephalogram signal (C4-P4). The present work can be applied for home monitoring systems for bruxism detection.

WAVELET FEATURES BASED SLEEP STAGES DETECTION USING SINGLE CHANNEL EEG

2017 ◽  
Vol 5 (4) ◽  
pp. 99-102
Author(s):  
Vandana Roy ◽  
Anand Prakash ◽  
Shailja Shukla
Keyword(s):  
Feature Extraction ◽  
Wavelet Decomposition ◽  
Single Channel ◽  
Kappa Coefficient ◽  
Sleep Stages ◽  
Chi Square ◽  
Wavelet Features ◽  
Stage Classification ◽  
Selection Of

The sleep stages determination is important for the identification and diagnosis of different diseases. An efficient algorithm of wavelet decomposition is used for feature extraction of single channel EEG. The Chi-Square method is applied for the selection of the best attributes from the extracted features. The classification of different staged techniques is applied with the help AdaBoost.M1 algorithm. The accuracy of 89.82% achieved in the six stage classification. The weighted sensitivity of all stages is 89.8% and kappa coefficient of 77.93% is obtained in the six stage classification.

scholarly journals Analysis Strategy Based on Wavelet Decomposition for Classification of Voltage Sags

2004 ◽  
Vol 1 (02) ◽  
pp. 161-167
Author(s):  
J. Xargayó ◽  
◽  
J. Meléndez ◽  
J. Colomer
Keyword(s):  
Wavelet Decomposition ◽  
Voltage Sags ◽  
Analysis Strategy

scholarly journals A Five-Level Wavelet Decomposition and Dimensional Reduction Approach for Feature Extraction and Classification of MR and CT Scan Images

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Varun Srivastava ◽  
Ravindra Kumar Purwar
Keyword(s):  
Neural Network ◽  
Ct Scan ◽  
Wavelet Decomposition ◽  
Nearest Neighbor ◽  
Principal Component ◽  
Two Dimensional ◽  
Mr Images ◽  
Probabilistic Principal Component Analysis ◽  
Different Sources

This paper presents a two-dimensional wavelet based decomposition algorithm for classification of biomedical images. The two-dimensional wavelet decomposition is done up to five levels for the input images. Histograms of decomposed images are then used to form the feature set. This feature set is further reduced using probabilistic principal component analysis. The reduced set of features is then fed into either K nearest neighbor algorithm or feed-forward artificial neural network, to classify images. The algorithm is compared with three other techniques in terms of accuracy. The proposed algorithm has been found better up to 3.3%, 12.75%, and 13.75% on average over the first, second, and third algorithm, respectively, using KNN and up to 6.22%, 13.9%, and 14.1% on average using ANN. The dataset used for comparison consisted of CT Scan images of lungs and MR images of heart as obtained from different sources.

Sign in / Sign up

Export Citation Format

Share Document