Uncertainty handling using Improvised Intuitionistic fuzzy ANN based Voice Disorder Detection
P. Kokila1, G. M. Nasira2 

1P. Kokila, Research Scholar, Department of Computer Science, Chikkanna government Arts college, Tirupur-641602, (Tamil Nadu), India
2Dr. G. M. Nasira, Professor & Head, Department of Computer Applications, Chikkanna Government Arts College, Tirupur-641602, (Tamil Nadu), India.

Manuscript received on 03 March 2019 | Revised Manuscript received on 08 March 2019 | Manuscript published on 30 July 2019 | PP: 1225-1229 | Volume-8 Issue-2, July 2019 | Retrieval Number: A1424058119/19©BEIESP | DOI: 10.35940/ijrte.A1424.078219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The voice pathology detection is one of the essential process which has to be determined in the earlier stages because it is a sign for raising health related problems. The aim of this paper is to handle the uncertainty in voice dataset due to inconsistency in extracting potential features and vagueness in dealing voice signals. The raw voice signals are preprocessed by feature extraction using meyer wavelet and potential features involved in voice disorder detection are done using sequential forward feature selection methods as voice preprocessing. This research work introduced an improvised intuitionistic fuzzy artificial neural network which enhances the process of voice disorder detection is SVD database by using analytical hierarchical processing for assigning weights and thus the complete neural network performance was fine tuned instead of assigning the weights randomly. The simulation results proved the performance of the proposed model as best by producing more promising result while comparing with ANN, PANN and Fuzzy ANN models.
Keywords: Voice Pathology, Artificial Neural Network, Feature Extraction, Classification, Fuzzy, Intuitionistic Fuzzy, Uncertainty

Scope of the Article: Classification