Financial Fraud Detection using Radial Basis Network

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.

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Discriminating Input Variables for Fraud Detection using Radial Basis Function Network

Circulation in Computer Science ◽

10.22632/ccs-2017-252-70 ◽

2018 ◽

Vol 3 (1) ◽

pp. 1-9

Author(s):

I. O. Alabi ◽

R. G. Jimoh

Keyword(s):

Radial Basis Function ◽

Basis Function ◽

Information Gain ◽

Radial Basis Function Network ◽

Data Gathering ◽

Fraud Detection ◽

Fixed Time ◽

Rbf Network ◽

Radial Basis ◽

Input Variables

Fraud is an adaptive crime; special methods of data gathering and analysis are required to combat fraud issues as criminals often quest for dubious techniques to evade detection. Radial basis function (RBF) network, was used to build base models that identifies and detect the risk of fraud in transactions. At first, it is imperative to isolate the basic factors that are predictive of fraud occurrences so as to determine the Information gain of each attribute. The input variables’ importance was ascertained to indicate how some of the input variables were distinguished as strong indicators or weak indicators of fraud. Hence, the relevant attributes were selected prior to examining the model’s performance. This study has found relevance among corporate business professionals and government agencies, to minimizing the time and cost of fraud detection. The researcher recommended that fraud mining processes be regularly updated at fixed time intervals to checkmate criminals.

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Arrhythmia Detection Using a Radial Basis Function Network With Wavelet Features

International Journal of Knowledge-Based Organizations ◽

10.4018/ijkbo.2020040104 ◽

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.

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Radial Basis Function Network Based Design of Incipient Motion Condition of Alluvial Channels with Seepage

Journal of Hydrology and Hydromechanics ◽

10.2478/v10098-010-0010-4 ◽

2010 ◽

Vol 58 (2) ◽

pp. 102-113 ◽

Cited By ~ 3

Author(s):

Bimlesh Kumar ◽

Gopu Sreenivasulu ◽

Achanta Rao

Keyword(s):

Radial Basis Function ◽

Basis Function ◽

Radial Basis Function Network ◽

Incipient Motion ◽

Alluvial Channels ◽

Rbf Network ◽

Motion Condition ◽

Radial Basis ◽

Alluvial Channel ◽

Function Network

Radial Basis Function Network Based Design of Incipient Motion Condition of Alluvial Channels with SeepageIncipient motion is the critical condition at which bed particles begin to move. Existing relationships for incipient motion prediction do not consider the effect of seepage. Incipient motion design of an alluvial channel affected from seepage requires the information about five basic parameters, i.e., particle sized, water depthy, energy slopeSf, seepage velocityvsand average velocityu.As the process is extremely complex, getting deterministic or analytical form of process phenomena is too difficult. Data mining technique, which is particularly useful in modeling processes about which adequate knowledge of the physics is limited, is presented here as a tool complimentary to model the incipient motion condition of alluvial channel at seepage. This article describes the radial basis function (RBF) network to predict the seepage velocity vs and average velocityubased on experimental data of incipient condition. The prediction capability of model has been found satisfactory and methodology to use the model is also presented. It has been found that model predicts the phenomena very well. With the help of the RBF network, design curves have been presented for designing the alluvial channel when it is affected by seepage.

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Radial Basis Function Network Based MPPT for Photovoltaic System during Shading Condition

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.448-453.1474 ◽

2013 ◽

Vol 448-453 ◽

pp. 1474-1479

Author(s):

Mahamad Abd Kadir ◽

Saon Sharifah

Keyword(s):

Radial Basis Function ◽

Basis Function ◽

Variable Temperature ◽

Radial Basis Function Network ◽

Photovoltaic System ◽

Pv System ◽

Rbf Network ◽

Point Tracking ◽

Radial Basis ◽

Temperature Constant

The output powers of photovoltaic (PV) system are crucially depending of the two variable factors, which are the cell temperatures and solar irradiances. A method to utilize effectively the PV is known as a maximum power point tracking (MPPT) method. This method is extract the maximum available power from PV module by making them operates at the most efficient output. This paper presents Radial Basis Function (RBF) Network to control the MPPT of PV system. The performances of the controller is analyzed in four conditions with are constant irradiation and temperature, constant irradiation and variable temperature, constant temperature and variable irradiation, and variable temperature and irradiation. The proposed system is simulated by using MATLAB-SIMULINK. According to the results, RBF controller has shown better performance during partially shaded conditions.

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A FULLY COMPLEX-VALUED RADIAL BASIS FUNCTION NETWORK AND ITS LEARNING ALGORITHM

International Journal of Neural Systems ◽

10.1142/s0129065709002026 ◽

2009 ◽

Vol 19 (04) ◽

pp. 253-267 ◽

Cited By ~ 88

Author(s):

R. SAVITHA ◽

S. SURESH ◽

N. SUNDARARAJAN

Keyword(s):

Radial Basis Function ◽

Basis Function ◽

Learning Algorithm ◽

Radial Basis Function Network ◽

Activation Function ◽

Rbf Network ◽

Practical Applications ◽

Xor Problem ◽

Radial Basis ◽

Complex Valued

In this paper, a fully complex-valued radial basis function (FC-RBF) network with a fully complex-valued activation function has been proposed, and its complex-valued gradient descent learning algorithm has been developed. The fully complex activation function, sech(.) of the proposed network, satisfies all the properties needed for a complex-valued activation function and has Gaussian-like characteristics. It maps Cn → C, unlike the existing activation functions of complex-valued RBF network that maps Cn → R. Since the performance of the complex-RBF network depends on the number of neurons and initialization of network parameters, we propose a K-means clustering based neuron selection and center initialization scheme. First, we present a study on convergence using complex XOR problem. Next, we present a synthetic function approximation problem and the two-spiral classification problem. Finally, we present the results for two practical applications, viz., a non-minimum phase equalization and an adaptive beam-forming problem. The performance of the network was compared with other well-known complex-valued RBF networks available in literature, viz., split-complex CRBF, CMRAN and the CELM. The results indicate that the proposed fully complex-valued network has better convergence, approximation and classification ability.

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