scholarly journals BEAT CLASSIFICATION USING HYBRID WAVELET TRANSFORM BASED FEATURES AND SUPERVISED LEARNING APPROACH

2017 ◽  
Vol 13 (8) ◽  
pp. 6397-6405
Author(s):  
M. Sasireka ◽  
A. Senthilkumar
Keyword(s):  
Wavelet Transform ◽  
Nearest Neighbor ◽  
Discrete Wavelet ◽  
K Nearest Neighbor ◽  
Complex Wavelet Transform ◽  
Threshold Method ◽  
Hybrid Features ◽  
Qrs Complex Detection ◽  
Beat Classification ◽  
Learning Machine

This paper describes an automatic heartbeat recognition based on QRS detection, feature extraction and classification. In this paper five different type of ECG beats of MIT BIH arrhythmia database are automatically classified. The proposed method involves QRS complex detection based on the differences and approximation derivation, inversion and threshold method. The computation of combined Discrete Wavelet Transform (DWT) and Dual Tree Complex Wavelet Transform (DTCWT) of hybrid features coefficients are obtained from the QRS segmented beat from ECG signal which are then used as a feature vector. Then the feature vectors are given to Extreme Learning Machine (ELM) and k- Nearest Neighbor (kNN) classifier for automatic classification of heartbeat. The performance of the proposed system is measured by sensitivity, specificity and accuracy measures.

Comparison of the Hybrid Credit Scoring Models Based on Various Classifiers

2012 ◽  
pp. 220-239
Author(s):  
Fei-Long Chen ◽  
Feng-Chia Li
Keyword(s):  
Nearest Neighbor ◽  
Credit Scoring ◽  
Back Propagation ◽  
Support Vector ◽  
Data Sets ◽  
K Nearest Neighbor ◽  
Hybrid Features ◽  
Credit Data ◽  
Learning Machine ◽  
Propagation Network

Credit scoring is an important topic for businesses and socio-economic establishments collecting huge amounts of data, with the intention of making the wrong decision obsolete. In this paper, the authors propose four approaches that combine four well-known classifiers, such as K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Back-Propagation Network (BPN) and Extreme Learning Machine (ELM). These classifiers are used to find a suitable hybrid classifier combination featuring selection that retains sufficient information for classification purposes. In this regard, different credit scoring combinations are constructed by selecting features with four approaches and classifiers than would otherwise be chosen. Two credit data sets from the University of California, Irvine (UCI), are chosen to evaluate the accuracy of the various hybrid features selection models. In this paper, the procedures that are part of the proposed approaches are described and then evaluated for their performances.

ekspresi Mengidentifikasi Mood Mahasiswa Berdasarkan Ekspresi Wajah dengan Menggunakan Discrete Wavelet Transform dan Fuzzy K-Nearest Neighbor

2019 ◽  
Vol 11 (1) ◽  
pp. 34-38
Author(s):  
Nur Inzani Reski Amalia ◽  
Jayanti Yusmah Sari
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Nearest Neighbor ◽  
Discrete Wavelet ◽  
K Nearest Neighbor

Mood adalah keadaan emosional yang bersifat sementara. Mood biasanya memiliki nilai kualitas positif atau negatif. Kecerdasan emosional memiliki peran lebih dari 80% dalam mencapai kesuksesan hidup dan menjadi salah satu faktor yang mempengaruhi daya tangkap mahasiswa dalam proses perkuliahan. Dengan mengetahui emosi-emosi mahasiswa, kita dapat membantu daya tangkap mahasiswa saat proses perkuliahan, serta dibutuhkannya sistem yang dapat mengidentifikasi emosi-emosi yang terbentuk saat perkuliahan berlangsung. Sistem ini dibangun menggunakan  Discrete Wavelet Transform yang mentransformasikan citra menjadi 4 sub-image. Citra hasil Discrete Wavelet Transform tampak kasar atau membentuk wajah yang dapat membedakan ekspresi mahasiswa. Hasil pengolahan citra Discrete Wavelet Transform di klasifikasikan dengan menggunakan Fuzzy K-nearest neighbor. Pengklasifikasian dibagi kedalam tiga ekspresi yaitu : Marah, Senang dan Sedih dengan akurasi 77,49%

scholarly journals EEG Motor-Imagery BCI System Based on Maximum Overlap Discrete Wavelet Transform (MODWT) and Machine learning algorithm

2021 ◽  
Vol 17 (2) ◽  
pp. 38-45
Author(s):  
Samaa Abdulwahab ◽  
Hussain Khleaf ◽  
Manal Jassim
Keyword(s):  
Wavelet Transform ◽  
Decision Tree ◽  
Discrete Wavelet Transform ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Low Cost ◽  
Brain Diseases ◽  
Discrete Wavelet ◽  
K Nearest Neighbor ◽  
Brain Connection

The ability of the human brain to communicate with its environment has become a reality through the use of a Brain-Computer Interface (BCI)-based mechanism. Electroencephalography (EEG) has gained popularity as a non-invasive way of brain connection. Traditionally, the devices were used in clinical settings to detect various brain diseases. However, as technology advances, companies such as Emotiv and NeuroSky are developing low-cost, easily portable EEG-based consumer-grade devices that can be used in various application domains such as gaming, education. This article discusses the parts in which the EEG has been applied and how it has proven beneficial for those with severe motor disorders, rehabilitation, and as a form of communicating with the outside world. This article examines the use of the SVM, k-NN, and decision tree algorithms to classify EEG signals. To minimize the complexity of the data, maximum overlap discrete wavelet transform (MODWT) is used to extract EEG features. The mean inside each window sample is calculated using the Sliding Window Technique. The vector machine (SVM), k-Nearest Neighbor, and optimize decision tree load the feature vectors.

scholarly journals EMG Signal Based Pattern Recognition Of Grasping Movement Using MODWT And Weighted K- Nearest Neighbor

2019 ◽  
Vol 8 (10) ◽  
pp. 1759-1764
Keyword(s):  
Pattern Recognition ◽  
Wavelet Transform ◽  
Nearest Neighbor ◽  
Discrete Wavelet ◽  
K Nearest Neighbor ◽  
Signal Processing Technique ◽  
Semg Signal ◽  
Emg Signal ◽  
Semg Signals

This study purposed and evaluates a method based on weighted K-NN classification of surface Electromyogram (sEMG) signals. The sEMG signal classification plays the key role in designing a prosthetic for amputee persons. Wavelet transform is new signal processing technique, which provides better resolution in time and frequency domain simultaneously. Due to these wavelet properties, it can be effectively used in processing the sEMG signal to determine certain amplitude changes at certain frequencies. This paper propose a Maximal overlap Discrete Wavelet Transform (MODWT) approach for Weighted K-NN classifier for classification of sEMG signals based Grasping movements. At level 5 signal decomposition using MODWT, useful resolution component of the sEMG signal is obtained. In this paper Time-domain (TD) features set is used, which shows a decent performance. In WKNN, use a square-inverse weighted technique to improve the performance of the K-NN. Hence, a novel feature set obtained from decomposed signal using MODWT is used to improve the performance of sEMG for classification. MODWT was used for de-noising and time scale feature extraction of sEMG signals. Several WKNN classifiers are tested to optimize classification accuracy and computational problems. PCA is use to reduce the size of the level 5 decomposed data. WKNN performance evaluation on K=10 values with or without PCA. Six hand grasping movements have been classified, results indicate that this method allows the classification of hand pattern recognition with high precision.

scholarly journals Application-Specific Discriminant Analysis of Cardiac Anomalies Using Shift-Invariant Wavelet Transform

2021 ◽  
Vol 12 (4) ◽  
pp. 76-96
Author(s):  
Ritu Singh ◽  
Navin Rajpal ◽  
Rajesh Mehta
Keyword(s):  
Wavelet Transform ◽  
Discriminant Analysis ◽  
Nearest Neighbor ◽  
Medical Application ◽  
Computational Time ◽  
Support Vector ◽  
Testing Time ◽  
K Nearest Neighbor ◽  
Complex Wavelet Transform ◽  
Linear Discriminant

Automatic arrhythmia detection in electrocardiogram (ECG) using supervised learning has gained significant considerations in recent years. This paper projects the performance analysis of classifiers such as support vector machine (SVM), extreme learning machine (ELM), and k-nearest neighbor (KNN) with efficient time utilization showing multi-classification for specific medical application. The wavelet double decomposition is used to show the shift-invariant use of dual-tree complex wavelet transform for noise filtering and beat segmentation is done to extract 130 informative samples. Further, the linear discriminant analysis is applied to dimensionally reduce and elite the 12 most relevant features for classifying normal and four abnormal beats collected from MIT/BIH ECG database. The proposed executed system distinguishes SVM, ELM, and KNN with percentage accuracy of 99.8, 97, and 99.8 having classifier testing time as 0.0081s, 0.0031s, and 0.0234s, respectively. The simulated experimental outcomes in comparison with existing work yields adequate accuracy, and computational time.

Two-stage feature selection for bearing fault diagnosis based on dual-tree complex wavelet transform and empirical mode decomposition

2015 ◽  
Vol 230 (2) ◽  
pp. 291-302 ◽  
Author(s):  
Mien Van ◽  
Hee-Jun Kang
Keyword(s):  
Feature Selection ◽  
Wavelet Transform ◽  
Empirical Mode Decomposition ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Complex Wavelet Transform ◽  
Two Stage ◽  
Mode Decomposition ◽  
Complex Wavelet ◽  
Neighbor Classifier

This paper presents an automatic fault diagnosis of different rolling element bearing faults using a dual-tree complex wavelet transform, empirical mode decomposition, and a novel two-stage feature selection technique. In this method, dual-tree complex wavelet transform and empirical mode decomposition were used to preprocess the original vibration signal to obtain more accurate fault characteristic information. Then, features in the time domain were extracted from each of the original signals, the coefficients of the dual-tree complex wavelet transform, and some useful intrinsic mode functions to generate a rich combined feature set. Next, a two-stage feature selection algorithm was proposed to generate the smallest set of features that leads to the superior classification accuracy. In the first stage of the two-stage feature selection, we found the candidate feature set using the distance evaluation technique and a k-nearest neighbor classifier. In the second stage, a genetic algorithm-based k-nearest neighbor classifier was designed to obtain the superior combination of features from the candidate feature set with respect to the classification accuracy and number of feature inputs. Finally, the selected features were used as the input to a k-nearest neighbor classifier to evaluate the system diagnosis performance. The experimental results obtained from real bearing vibration signals demonstrated that the method combining dual-tree complex wavelet transform, empirical mode decomposition, and the two-stage feature selection technique is effective in both feature extraction and feature selection, which also increase classification accuracy.

scholarly journals A Novel Fault Diagnosis Method for Motor Bearing Based on DTCWT and AFSO-KELM

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Yan Lu ◽  
Peijiang Li
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Extreme Learning Machine ◽  
Discrete Wavelet ◽  
Complex Wavelet Transform ◽  
Swarm Optimization ◽  
Artificial Fish Swarm ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine ◽  
Motor Bearing

Aiming at the defects of wavelet transform-based feature extraction and extreme learning machine-based classification, a novel fault diagnosis method for motor bearing, based on dual tree complex wavelet transform and artificial fish swarm optimization-kernel extreme learning machine (DTCWT-AFSO-KELM), is proposed in this paper. Firstly, the dual tree complex wavelet transform instead of the discrete wavelet transform is used to decompose the motor bearing signal; then, the features with large differentiation of motor-bearing fault are extracted; finally, the states of motor bearing are classified by using artificial fish swarm optimization-kernel extreme learning machine. In order to better prove the superiority of this method, four kinds of state data of motor bearing under the conditions of 0 HP (horsepower) load, 1 HP load, 2 HP load, and 3 HP load are used to test. The experimental results indicate that the diagnosis accuracies of DTCWT-AFSO-KELM are obviously better than those of discrete wavelet transform and artificial fish swarm optimization-kernel extreme learning machine (DWT-AFSO-KELM) or discrete wavelet transform and extreme learning machine (DWT-ELM) under different loads.

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