Arrhythmia Detection Using a Radial Basis Function Network With Wavelet Features

2020 ◽  
Vol 10 (2) ◽  
pp. 48-56
Author(s):  
Saumendra Kumar Mohapatra ◽  
Mihir Narayan Mohanty
Keyword(s):  
Data Mining ◽  
Wavelet Transform ◽  
Radial Basis Function ◽  
Basis Function ◽  
Radial Basis Function Network ◽  
Discrete Wavelet ◽  
Data Mining Technique ◽  
Result Section ◽  
Wavelet Features ◽  
Radial Basis

This article describes how the demand of hospital services increasing day by day. The smart service to the patients is highly essential that counts the death rate. The diagnosis of the heart disease facilitates to store our data. It motivates the application of data mining techniques are useful in health sectors. Some progress has been made for data mining in different areas. However, a large gap of this application found in medical and patient services. In this paper authors have taken an approach to detect arrhythmias using wavelet transform and data mining technique. In first stage R-peaks of arrhythmia data has been detected using wavelet transform. In the next stage the wavelet coefficients are consider as the input features to the radial basis function (RBFN) model. It has been found that the peaks have been detected using discrete wavelet transform. However, the result with RBFN using wavelet features outperforms. The accuracy and the mean square error (MSE) are obtained and shown in result section.

Financial Fraud Detection using Radial Basis Network

2018 ◽  
Vol 3 (1) ◽  
pp. 10-21
Author(s):  
I. O. Alabi ◽  
R. G. Jimoh
Keyword(s):  
Radial Basis Function ◽  
Basis Function ◽  
Radial Basis Function Network ◽  
Fraud Detection ◽  
Misclassification Error ◽  
Computationally Efficient ◽  
Operating Characteristics ◽  
Data Mining Technique ◽  
Rbf Network ◽  
Radial Basis

The ubiquitous cases of abnormal transactions with intent to defraud is a global phenomenon. An architecture that enhances fraud detection using a radial basis function network was designed using a supervised data mining technique― radial basis function (RBF) network, a multivariate interpolation approximation method. Several base models were thus created, and in turn used in aggregation to select the optimum model using the misclassification error rate (MER), accuracy, sensitivity, specificity and receiver operating characteristics (ROC) metrics. The results shows that the model has a zero-tolerance for fraud with better prediction especially in cases where there were no fraud incidents doubtful cases were rather flagged than to allow a fraud incident to pass undetected. Expectedly, the model’s computations converge faster at 200 iterations. This study is generic with similar characteristics with other classification methods but distinct parameters thereby minimizing the time and cost of fraud detection by adopting computationally efficient algorithm.

RECOGNITION OF FACIAL EXPRESSION BY IMAGE PROCESSING AND RADIAL BASIS FUNCTION NETWORK

2016 ◽  
Author(s):  
Olímpio Murilo Capeli ◽  
Euvaldo Ferreira Cabral Junior ◽  
Sadao Isotani ◽  
Antonio Roberto Pereira Leite de Albuquerque
Keyword(s):  
Image Processing ◽  
Facial Expression ◽  
Radial Basis Function ◽  
Basis Function ◽  
Radial Basis Function Network ◽  
Radial Basis ◽  
Function Network
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