scholarly journals Classification of EMI discharge sources using time–frequency features and multi-class support vector machine

2018 ◽  
Vol 163 ◽  
pp. 261-269 ◽  
Author(s):  
Imene Mitiche ◽  
Gordon Morison ◽  
Alan Nesbitt ◽  
Michael Hughes-Narborough ◽  
Brian G. Stewart ◽  
...  
2020 ◽  
Author(s):  
Tuan Pham

The importance of automated classification of histopathological images has been increasingly recognized for effective processing of large volumes of data in the era of digital pathology for new discovery of disease mechanism. This paper presents a deep-learning approach that extracts time-frequency features of H&E stained tissue images for classification by long short-term memory networks. Using two large public databases of colorectal-cancer and heart-failure H&E stained tissue images, the proposed approach outperforms several state-of-the-art benchmark classification methods, including support vector machines and convolutional neural networks in terms of several statistical measures.


2020 ◽  
Author(s):  
Tuan Pham

The importance of automated classification of histopathological images has been increasingly recognized for effective processing of large volumes of data in the era of digital pathology for new discovery of disease mechanism. This paper presents a deep-learning approach that extracts time-frequency features of H&E stained tissue images for classification by long short-term memory networks. Using two large public databases of colorectal-cancer and heart-failure H&E stained tissue images, the proposed approach outperforms several state-of-the-art benchmark classification methods, including support vector machines and convolutional neural networks in terms of several statistical measures.


2020 ◽  
Vol 10 (11) ◽  
pp. 3959
Author(s):  
Un-Chang Jeong

This study proposes a classification method that uses the continuous wavelet transform and the support vector machine approach to classify refrigerant flow noises generated in an air conditioner. The air conditioning noise was identified as an abnormal signal by the use of the first- and second-order moments. The start and end times of refrigerant flow noises were identified by detecting the singularities of the continuous wavelet transform coefficient in the time domain and by means of listening to the measured sounds. Further, the time-frequency characteristics of refrigerant flow noise were analyzed with the continuous wavelet transform. For the support vector machine-based classification of refrigerant flow noise in an air conditioner, the grid search method was used to determine kernel hyperparameters. Five-fold cross validation was employed for the application of the support vector machine to the classification of air conditioner refrigerant noise. In addition, measured sound sources were modified based on classified refrigerant flow noise to compare the classification accuracy of a jury test with the results of the support vector machine.


2015 ◽  
Vol 27 (02) ◽  
pp. 1550015 ◽  
Author(s):  
Assya Bousbia-Salah ◽  
Malika Talha-Kedir

Wavelet transform decomposition of electroencephalogram (EEG) signals has been widely used for the analysis and detection of epileptic seizure of patients. However, the classification of EEG signals is still challenging because of high nonstationarity and high dimensionality. The aim of this work is an automatic classification of the EEG recordings by using statistical features extraction and support vector machine. From a real database, two sets of EEG signals are used: EEG recorded from a healthy person and from an epileptic person during epileptic seizures. Three important statistical features are computed at different sub-bands discrete wavelet and wavelet packet decomposition of EEG recordings. In this study, to select the best wavelet for our application, five wavelet basis functions are considered for processing EEG signals. After reducing the dimension of the obtained data by linear discriminant analysis and principal component analysis (PCA), feature vectors are used to model and to train the efficient support vector machine classifier. In order to show the efficiency of this approach, the statistical classification performances are evaluated, and a rate of 100% for the best classification accuracy is obtained and is compared with those obtained in other studies for the same dataset. However, this method is not meant to replace the clinician but can assist him for his diagnosis and reinforce his decision.


Author(s):  
Rishi Raj Sharma ◽  
Mohit Kumar ◽  
Ram Bilas Pachori

Electromyogram (EMG) signals are commonly used by doctors to diagnose abnormality of muscles. Manual analysis of EMG signals is a time-consuming and cumbersome task. Hence, this chapter aims to develop an automated method to detect abnormal EMG signals. First, authors have applied the improved eigenvalue decomposition of Hankel matrix and Hilbert transform (IEVDHM-HT) method to obtain the time-frequency (TF) representation of motor unit action potentials (MUAPs) extracted from EMG signals. Then, the obtained TF matrices are used for features extraction. TF matrix has been sliced into several parts and fractional energy in each slice is computed. A percentile-based slicing is applied to obtain discriminating features. Finally, the features are used as an input to the classifiers such as random forest, least-squares support vector machine, and multilayer perceptron to classify the EMG signals namely, normal and ALS, normal and myopathy, and ALS and myopathy, and achieved accuracy of 83%, 80.8%, and 96.7%, respectively.


2011 ◽  
Vol 131 (8) ◽  
pp. 1495-1501
Author(s):  
Dongshik Kang ◽  
Masaki Higa ◽  
Hayao Miyagi ◽  
Ikugo Mitsui ◽  
Masanobu Fujita ◽  
...  

2018 ◽  
Vol 62 (5) ◽  
pp. 558-562
Author(s):  
Uchaev D.V. ◽  
◽  
Uchaev Dm.V. ◽  
Malinnikov V.A. ◽  
◽  
...  

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