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In this paper, a prediction model is developed using support vector machine for forecasting the parameter associated with ground motion of a seismic signal. The prediction model is developed using three learning algorithms, e-support vector regression, n-support vector regression and least squaresupport vector regression (Ls-SVR) for forecasting peak ground acceleration (PGA), a parameter associated with ground motion of a seismic signal. The prediction model is developed for each of the algorithms with different kernel functions, namely linear kernel, polynomial kernel and radial basis function kernel. The ground motion parameter is related to four seismic parameters, namely faulting mechanism, average soil shear wave velocity, earthquake magnitude and source to site distance. The database used for modelling is NGA flatfile released by Pacific Earthquake Engineering Research Center. The experimental results show that the optimal prediction model for forecasting PGA is Ls-SVR with RBF kernel. It is observed that the developed prediction model is better compared to the existing conventional models and benchmark models in the same database. This paper further compares the three variations of SVR algorithm for ground motion parameter prediction model. The learning effectiveness of each algorithm is measured in terms of accuracy, testing error and overfitness measure.

Epilepsy is a neurological disorder which is most widespread in human DWT decomposes the signal into approximation and detail coefficients, the ApEn values the coefficients were computed using pattern length (m= 2 and 3) as an input feature for the Least square support vector machine (LS-SVM). The classification is done using LS-SVM and the resultswere compared using RBF and linear kernels. The proposed model has used the EEG data consisting of 5 classes and compared with using the approximate and detailed coefficients combined and individually. The classification accuracy of the LS-SVM using the RBF and Linear kernel with ApEn using different cases is compared and it is found that the best accuracy percentage is 100% with RBF kernel.

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