scholarly journals Performance Analysis of ECG Arrhythmia Classification based on Different SVM Methods

2020 ◽  
Vol 9 (12) ◽  
pp. 45-49
Keyword(s):  
Support Vector Machine ◽  
Decision Function ◽  
Support Vector ◽  
Svm Classifier ◽  
Data Sets ◽  
Artery Disease ◽  
Ecg Arrhythmia Classification ◽  
The One ◽  
Ecg Arrhythmia ◽  
Ecg Data

Heart arrhythmias are the different types of heartbeats which are irregular in nature. In Tachycardia the heartbeat works too fast and in case of Bradycardia it works too slow. In the study of different cardiac conditions automatic detection of heart arrhythmia is done by the classification and feature extraction of Electrocardiogram(ECG) data. Various Support Vector Machine based methods are used to analyze and classify ECG signals for arrhythmia detection. There are several Support Vector Machine (SVM) methods used to classify the ECG data such as one against all, one against one and fuzzy decision function. This classification detects the existence of the arrhythmia and it helps the physicians to treat the heart patient with more accurate way. To train SVM, the MIT BIH Arrhythmia database is used which works with the heart disorder like sinus bradycardy, old inferior myocardial infarction, coronary artery disease, right bundle branch block. All three methods are implemented in proper way, and their rate of accuracy with SVM classifier is optimal when it is processed with the one-against-all method. The data sets of ECG arrhythmia are usually complex in nature, so for the SVM based classification one-against-all method has great impact and will fetch better result.

A P2P Traffic Detection Method Based on Support Vector Machine

2012 ◽  
Vol 198-199 ◽  
pp. 1280-1285 ◽  
Author(s):  
Shang Fu Gong ◽  
Juan Chen
Keyword(s):  
Support Vector Machine ◽  
Detection Method ◽  
Detection Methods ◽  
Support Vector ◽  
Svm Classifier ◽  
Data Sets ◽  
Security Risks ◽  
Trip Time ◽  
And Control ◽  
Traffic Detection

The widely use of P2P (Peer-to-Peer) technology has caused resources take up too much, security risks and other problems, it is necessary to detect and control P2P traffic. After analyzing current P2P detection methods, a new method called TCBDM (Traffic Characters Based Detection Method) is put forward which combines P2P traffic character with support vector machine to detect P2P traffic. By choosing P2P traffic characters which differ from other network traffic, such as Round-Trip Time (RTT), the method creates a SVM classifier, uses a package named LIBSVM to classify P2P traffic in Moore_Set data sets. The result shows that TCBDM can detect P2P traffic effectively; the accuracy could reach 98%.

scholarly journals ELECTROCARDIOGRAM ARRHYTHMIA CLASSIFICATION SYSTEM USING SUPPORT VECTOR MACHINE BASED FUZZY LOGIC

2016 ◽  
Vol 9 (1) ◽  
pp. 35 ◽  
Author(s):  
Sugiyanto Sugiyanto ◽  
Tutuk Indriyani ◽  
Muhammad Heru Firmansyah
Keyword(s):  
Support Vector Machine ◽  
Fuzzy Logic ◽  
Support Vector ◽  
Ecg Signal ◽  
Average Value ◽  
Sensitivity Level ◽  
Accuracy Level ◽  
Baseline Wander ◽  
Ecg Arrhythmia Classification ◽  
Ecg Arrhythmia

Arrhythmia is a cardiovascular disease that can be diagnosed by doctors using an electrocardiogram (ECG). The information contained on the ECG is used by doctors to analyze the electrical activity of the heart and determine the type of arrhythmia suffered by the patient. In this study, ECG arrhythmia classification process was performed using Support Vector Machine based fuzzy logic. In the proposed method, fuzzy membership functions are used to cope with data that are not classifiable in the method of Support Vector Machine (SVM) one-against-one. An early stage of the data processing is the baseline wander removal process on the original ECG signal using Transformation Wavelet Discrete (TWD). Afterwards then the ECG signal is cleaned from the baseline wander segmented into units beat. The next stage is to look for six features of the beat. Every single beat is classified using SVM method based fuzzy logic. Results from this study show that ECG arrhythmia classification using proposed method (SVM based fuzzy logic) gives better results than original SVM method. ECG arrhythmia classification using SVM method based fuzzy logic forms an average value of accuracy level, sensitivity level, and specificity level of 93.5%, 93.5%, and 98.7% respectively. ECG arrhythmia classification using only SVM method forms an average value accuracy level, sensitivity level, and specificity level of 91.83%, 91.83%, and 98.36% respectively.

Toward a better fitness club: Evidence from exerciser online rating and review using latent Dirichlet allocation and support vector machine

2018 ◽  
Vol 61 (1) ◽  
pp. 64-76 ◽  
Author(s):  
Susan (Sixue) Jia
Keyword(s):  
Support Vector Machine ◽  
Latent Dirichlet Allocation ◽  
Support Vector ◽  
Svm Classifier ◽  
Data Sets ◽  
Fitness Clubs ◽  
Online Ratings ◽  
Online Rating ◽  
Quantitative Rating ◽  
Dirichlet Allocation

Fitness clubs have never ceased searching for quality improvement opportunities to better serve their exercisers, whereas exercisers have been posting online ratings and reviews regarding fitness clubs. Studied together, the quantitative rating and qualitative review can provide a comprehensive depiction of exercisers’ perception of fitness clubs. However, the typological and dimensional discrepancies of online rating and review have hindered the joint study of the two data sets to fully exploit their business value. To this end, this study bridges the gap by examined 53,979 pairs of exerciser online rating and review from 100 fitness clubs in Shanghai, China. Using latent Dirichlet allocation (LDA) based text mining, we identified the 17 major topics on which the exercisers were writing. A support vector machine (SVM) classifier was then employed to establish the rating-review relations, with an accuracy rate of up to 86%. Finally, the relative impact of each topic on exerciser satisfaction was computed and compared by introducing virtual reviews. The significance of this study is that it systematically creates a standardized protocol of mining and correlating the massive structured/quantitative and unstructured/qualitative data available online, which is readily transferable to the other service and product sectors.

scholarly journals Effect of noise on support vector machine based fault diagnosis of IM using vibration and current signatures

2018 ◽  
Vol 211 ◽  
pp. 03009
Author(s):  
Purushottam Gangsar ◽  
Rajiv Tiwari
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Support Vector ◽  
Svm Classifier ◽  
Severity Levels ◽  
Electrical Faults ◽  
Using Data ◽  
Multiclass Svm ◽  
The One ◽  
Analysis Of Performance

This paper analyzes the effect of noise on support vector machine (SVM) based fault diagnosis of IM (IM). For this, a number of mechanical (bearing fault, unbalanced rotor, bowed rotor and misaligned rotor) and electrical faults (broken rotor bar, stator winding fault with two severity levels and phase unbalance with two severity levels) of IM are considered here. The vibration and current signals are used here for the diagnosis. Different experiments were performed in order to generate these signals at various operating condition of IM (Speed and Load). Time domain feature are then extracted from the raw vibration and current signals obtained from the experiments. Then, the noise are added in the raw signals and the same features are extracted from this corrupted signals. The features from the original and corrupted signals are used to feed the classifier. The one-versus-one multiclass SVM are used here to perform multi-fault diagnosis. The comparative analysis of performance of the SVM classifier using data with and without noise is presented.

Linguistic Rule Extraction from Support Vector Machine Classifiers

2008 ◽  
pp. 1269-1279
Author(s):  
Xiuju Fu ◽  
Lipo Wang ◽  
GihGuang Hung ◽  
Liping Goh
Keyword(s):  
Support Vector Machine ◽  
Kernel Function ◽  
Rule Extraction ◽  
Support Vector ◽  
Svm Classifier ◽  
Data Sets ◽  
Linear Kernel ◽  
Linguistic Rule ◽  
Linguistic Rules ◽  
Rbf Kernel

Classification decisions from linguistic rules are more desirable compared to complex mathematical formulas from support vector machine (SVM) classifiers due to the explicit explanation capability of linguistic rules. Linguistic rule extraction has been attracting much attention in explaining knowledge hidden in data. In this chapter, we show that the decisions from an SVM classifier can be decoded into linguistic rules based on the information provided by support vectors and decision function. Given a support vector of a certain class, cross points between each line, which is extended from the support vector along each axis, and an SVM decision hyper-curve are searched first. A hyper-rectangular rule is derived from these cross points. The hyper-rectangle is tuned by a tuning phase in order to exclude those out-class data points. Finally, redundant rules are merged to produce a compact rule set. Simultaneously, important attributes could be highlighted in the extracted rules. Rule extraction results from our proposed method could follow SVM classifier decisions very well. We compare the rule extraction results from SVM with RBF kernel function and linear kernel function. Experiment results show that rules extracted from SVM with RBF nonlinear kernel function are with better accuracy than rules extracted from SVM with linear kernel function. Comparisons between our method and other rule extraction methods are also carried out on several benchmark data sets. Higher rule accuracy is obtained in our method with fewer number of premises in each rule.

Linguistic Rule Extraction from Support Vector Machine Classifiers

2007 ◽  
pp. 276-290
Author(s):  
Xiuju Fu ◽  
Lipo Wang ◽  
GihGuang Hung ◽  
Liping Goh
Keyword(s):  
Support Vector Machine ◽  
Kernel Function ◽  
Rule Extraction ◽  
Support Vector ◽  
Svm Classifier ◽  
Data Sets ◽  
Linear Kernel ◽  
Linguistic Rule ◽  
Linguistic Rules ◽  
Rbf Kernel

Classification decisions from linguistic rules are more desirable compared to complex mathematical formulas from support vector machine (SVM) classifiers due to the explicit explanation capability of linguistic rules. Linguistic rule extraction has been attracting much attention in explaining knowledge hidden in data. In this chapter, we show that the decisions from an SVM classifier can be decoded into linguistic rules based on the information provided by support vectors and decision function. Given a support vector of a certain class, cross points between each line, which is extended from the support vector along each axis, and an SVM decision hyper-curve are searched first. A hyper-rectangular rule is derived from these cross points. The hyper-rectangle is tuned by a tuning phase in order to exclude those out-class data points. Finally, redundant rules are merged to produce a compact rule set. Simultaneously, important attributes could be highlighted in the extracted rules. Rule extraction results from our proposed method could follow SVM classifier decisions very well. We compare the rule extraction results from SVM with RBF kernel function and linear kernel function. Experiment results show that rules extracted from SVM with RBF nonlinear kernel function are with better accuracy than rules extracted from SVM with linear kernel function. Comparisons between our method and other rule extraction methods are also carried out on several benchmark data sets. Higher rule accuracy is obtained in our method with fewer number of premises in each rule.

scholarly journals Classification of Chest X-Ray Images using Wavelet and MFCC Features and Support Vector Machine Classifier

2021 ◽  
Vol 11 (4) ◽  
pp. 7296-7301
Author(s):  
H. A. Owida ◽  
A. Al-Ghraibah ◽  
M. Altayeb
Keyword(s):  
Support Vector Machine ◽  
Deep Learning ◽  
Support Vector ◽  
Svm Classifier ◽  
Data Sets ◽  
Mel Frequency Cepstral Coefficients ◽  
X Ray ◽  
Proposed Model ◽  
Public Data ◽  
Chest X Ray

The shortage and availability limitation of RT-PCR test kits and is a major concern regarding the COVID-19 pandemic. The authorities' intention is to establish steps to control the propagation of the pandemic. However, COVID-19 is radiologically diagnosable using x-ray lung images. Deep learning methods have achieved cutting-edge performance in medical diagnosis software assistance. In this work, a new diagnostic method for detecting COVID-19 disease is implemented using advanced deep learning. Effective features were extracted using wavelet analysis and Mel Frequency Cepstral Coefficients (MFCC) method, and they used in the classification process using the Support Vector Machine (SVM) classifier. A total of 2400 X-ray images, 1200 of them classified as Normal (healthy) and 1200 as COVID-19, have been derived from a combination of public data sets to verify the validity of the proposed model. The experimental results obtained an overall accuracy of 98.8% by using five wavelet features, where the classification using MFCC features, MFCC-delta, and MFCC-delta-delta features reached accuracy around 97% on average. The results show that the proposed model has reached the required level of success to be applicable in COVID 19 diagnosis.

scholarly journals A Support Vector Machine-Firefly Algorithm for Movie Opinion Data Classification

2019 ◽  
Vol 13 (3) ◽  
pp. 219 ◽  
Author(s):  
Styawati Styawati ◽  
Khabib Mustofa
Keyword(s):  
Support Vector Machine ◽  
Parameter Optimization ◽  
Firefly Algorithm ◽  
Optimization Method ◽  
Support Vector ◽  
Svm Classifier ◽  
Test Results ◽  
Optimal Parameters ◽  
The One

The sentiment analysis used in this study is the process of classifying text into two classes, namely negative and positive classes. The classification method used is Support Vector Machine (SVM). The successful classification of the SVM method depends on the soft margin coefficient C, as well as the σ parameter of the kernel function. Therefore we need a combination of SVM parameters that are appropriate for classifying film opinion data using the SVM method. This study uses the Firefly method as an SVM parameter optimization method. The dataset used in this study is public opinion data on several films. The results of this study indicate that the Firefly Algorithm (FA) can be used to find optimal parameters in the SVM classifier. This is evidenced by the results of SVM system testing using 2179 data with nine SVM parameter combinations resulting in 85% highest accuracy, while the FA-SVM system with nine population and generation combinations produces the highest accuracy of 88%. The second test results using 1200 data using the same combination as the one test, the SVM method produces the highest accuracy of 87%, while the FA-SVM method produces the highest accuracy of 89%.

scholarly journals DA-Based Parameter Optimization of Combined Kernel Support Vector Machine for Cancer Diagnosis

Processes ◽  
2019 ◽  
Vol 7 (5) ◽  
pp. 263 ◽  
Author(s):  
Tao Xie ◽  
Jun Yao ◽  
Zhiwei Zhou
Keyword(s):  
Support Vector Machine ◽  
Kernel Function ◽  
Cancer Diagnosis ◽  
Classification Accuracy ◽  
Cancer Classification ◽  
Support Vector ◽  
Svm Classifier ◽  
Data Sets ◽  
Dragonfly Algorithm ◽  
Cancer Data

As is well known, the correct diagnosis for cancer is critical to save patients’ lives. Support vector machine (SVM) has already made an important contribution to the field of cancer classification. However, different kernel function configurations and their parameters will significantly affect the performance of SVM classifier. To improve the classification accuracy of SVM classifier for cancer diagnosis, this paper proposed a novel cancer classification algorithm based on the dragonfly algorithm and SVM with a combined kernel function (DA-CKSVM) which was constructed from a radial basis function (RBF) kernel and a polynomial kernel. Experiments were performed on six cancer data sets from University of California, Irvine (UCI) machine learning repository and two cancer data sets from Cancer Program Legacy Publication Resources to evaluate the validity of the proposed algorithm. Compared with four well-known algorithms: dragonfly algorithm-SVM (DA-SVM), particle swarm optimization-SVM (PSO-SVM), bat algorithm-SVM (BA-SVM), and genetic algorithm-SVM (GA-SVM), the proposed algorithm was able to find the optimal parameters of SVM classifier and achieved better classification accuracy on cancer datasets.

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