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  • In Jour Smart Sensing And Intelligent Systems

 

Research Article | 12-September-2018

ECG Decision Support System based on feedforward Neural Networks

Abstract The success of an Electrocardiogram (ECG) Decision Support System (DSS) requires the use of an optimum machine learning approach. For this purpose, this paper investigates the use of three feedforward neural networks; the Multilayer Perceptron (MLP), the Radial Basic Function Network (RBF), and the Probabilistic Neural Network (PNN) for recognition of normal and abnormal heartbeats. Feature sets were based on ECG morphology and Discrete Wavelet Transformer (DWT) coefficients. Then, a

Hela Lassoued, Raouf Ketata, Slim Yacoub

International Journal on Smart Sensing and Intelligent Systems, Volume 11 , ISSUE 1, 1–15

Article | 01-June-2015

INTELLIGENT NEURAL NETWORK CONTROL STRATEGY OF HYDRAULIC SYSTEM DRIVEN BY SERVO MOTOR

response and easy to realize closed loop control. System uses the structure of the combination of neural network control and RBF network online identification. The parameters of the controller are optimized by PSO algorithm offline and error back propagation (BP) algorithm offline, and a RBF network is built to identify the system online. The hydraulic power system’s control simulation experiments are conducted, and the experimental results at the typical working conditions of the hydraulic source show

Ma Yu

International Journal on Smart Sensing and Intelligent Systems, Volume 8 , ISSUE 2, 1406–1423

Research Article | 01-December-2017

PERFORMANCE EVALUATION OF SVM KERNELS ON MULTISPECTRAL LISS III DATA FOR OBJECT CLASSIFICATION

the LIBSVM package. In this paper, Support Vector Machine (SVM) is evaluated as classifier with four different kernels namely linear kernel, polynomial kernel, radial basis function kernel and sigmoid kernel. Several datasets are being experimented to find out the performance of various kernels of SVM .By changing the value of ‘C’ and γ varying results are observed. Among these RBF kernel with a value of C = 1000 and gamma=0.75 got an excellent accuracy of 99.1509%.The SVM-RBF

S.V.S. Prasad, T. Satya Savithri, Iyyanki V. Murali Krishna

International Journal on Smart Sensing and Intelligent Systems, Volume 10 , ISSUE 4, 829–844

Research Article | 01-December-2017

PERFORMANCE EVALUATION OF SVM KERNELS ON MULTISPECTRAL LISS III DATA FOR OBJECT CLASSIFICATION

the LIBSVM package. In this paper, Support Vector Machine (SVM) is evaluated as classifier with four different kernels namely linear kernel, polynomial kernel, radial basis function kernel and sigmoid kernel. Several datasets are being experimented to find out the performance of various kernels of SVM .By changing the value of ‘C’ and γ varying results are observed. Among these RBF kernel with a value of C = 1000 and gamma=0.75 got an excellent accuracy of 99.1509%.The SVM-RBF kernel gave an edge

S.V.S. Prasad, T. Satya Savithri, Iyyanki V. Murali Krishna

International Journal on Smart Sensing and Intelligent Systems, Volume 10 , ISSUE 4, 863–878

Article | 01-September-2015

Classification of artificial light sources and estimation of Color Rendering Index using RGB sensors, K Nearest Neighbor and Radial Basis Function

J.-S. Botero V., F.-E. López G., J.-F. Vargas B.

International Journal on Smart Sensing and Intelligent Systems, Volume 8 , ISSUE 3, 1505–1524

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