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Two methods for classification of transmission line faults are presented and compared, one based on a wavelet analysis and the other on an artificial neural network (ANN). While the wavelet analysis based approach requires consideration of wavelet multiresolution analysis (MRA) level-1 details of the three phase currents and delta currents, the ANN based approach requires the samples of three phase currents as inputs. Simulation studies using EMTP and MATLAB and considering wide variations in fault location, fault inception angle, and fault point resistance for different types of faults have shown that the wavelet analysis based method has an edge over the ANN based method.

This research paper proposes the enhancement of the accuracy of the results by using Artificial Neural Network optimized with Genetic Algorithm in prediction of heart disease diagnosis with UCI dataset. In this study neural network is optimized with Genetic Algorithm and proved experimentally. The trained feed forward neural network and fitting neural network are optimized with genetic algorithm and is then compared with the scale conjugate gradient descent backpropagation algorithms trained feed forward neural network and fitting neural network respectively for the accuracy enhancement percentage. The proposed learning is much faster and accurate as compared to the other one. The proposed learning is designed and developed by using MATLAB GUI feature. The proposed method achieved an accuracy of 97.83%. With this higher achieved accuracy the heart disease can be diagnosed more accurately and much proper treatments can be suggested.