Comparison of Performance Using GA and FIS in Spotting the Aberration Spot of Clinical Data with the Aid of NN and SVM

摘要

The main concern of the present investigation is to compare the performance between (1) Neural Network (NN) and Support Vector Machine (SVM) and (2) Genetic Algorithm (GA) and Fuzzy Inference System (FIS) which were used to identify the specific (acute) spots of the abnormal children speech who need specific practice in those identified spots to overcome stuttering behavior. In the previous work, there are three phases followed (1) Training Phase (2) Testing phase and (3) Optimization phase for blemishing the acute spot in which the speech practice can be provided for the abnormal children. The training and optimization in previous works were implemented using (1) NN and GA and (2) SVM and FIS. In this study, the comparison is done between the performance of (1) NN and SVM and (2) GA and FIS using Mathews Correlation Coefficient (MCC). Performance of the proposed system is tested by the dataset generated by abnormal and normal female children. The proposed system is implemented using MATLAB as working platform. This study is to provide a system to identify the acute spots in abnormal word where the stuttering children need the practice. Comparison Study is done between the performance of (1) NN and SVM and (2) GA and FIS.