Sequential process of Mel Frequency Cepstrum Coefficient (MFCC) and Binary Particle Swarm Optimization (BPSO) technique for improving the performance of Multi-Layer Perceptron (MLP) to detect asphyxia diseases through infant cries / Azlee Zabidi

Infant asphyxia is a condition caused by inadequate intake of oxygen suffered by newborn babies. Early diagnosis of asphyxia is important to avoid complications such as damage to the brain, organ and tissue or even death. Asphyxia occurs in infants with neurological level disturbance, which is found...

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Main Author:	Zabidi, Azlee
Format:	Thesis
Language:	English
Published:	2012
Subjects:	Applications of electric power
Online Access:	https://ir.uitm.edu.my/id/eprint/20425/6/20425.pdf
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id	my-uitm-ir.20425
record_format	uketd_dc
spelling	my-uitm-ir.204252022-12-06T07:01:41Z Sequential process of Mel Frequency Cepstrum Coefficient (MFCC) and Binary Particle Swarm Optimization (BPSO) technique for improving the performance of Multi-Layer Perceptron (MLP) to detect asphyxia diseases through infant cries / Azlee Zabidi 2012 Zabidi, Azlee Applications of electric power Infant asphyxia is a condition caused by inadequate intake of oxygen suffered by newborn babies. Early diagnosis of asphyxia is important to avoid complications such as damage to the brain, organ and tissue or even death. Asphyxia occurs in infants with neurological level disturbance, which is found to affect sound of cry produced by infants. The infant cry signals with asphyxia have distinct patterns which can be recognized with pattern classifiers such as Artificial Neural Network (ANN). This study investigates the performance of feature selection techniques namely F-Ratio, Orthogonal Lest Square (OLS) and Binary Particle Swarm Optimisation (BPSO) analysis in selecting optimal features extracted from feature extraction technique; Mel Frequency Cepstrum Coefficient (MFCC). Mel Frequency Cepstrum Coefficient (MFCC) was employed to extract the significant features from infant cry. The selected MFCC features were then used to train several ANN Multi-Layer Perceptron (MLP). The simulation results showed each method is able to improve classifier performance. Among three method discusses, BPSO was the best feature selection method with 96.03% classification accuracy followed by OLS (94%) and F-Ratio (93.38%). 2012 Thesis https://ir.uitm.edu.my/id/eprint/20425/ https://ir.uitm.edu.my/id/eprint/20425/6/20425.pdf text en public mphil masters Universiti Teknologi MARA (UiTM) Faculty of Electrical Engineering Mansor, Wahidah
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Mansor, Wahidah
topic	Applications of electric power
spellingShingle	Applications of electric power Zabidi, Azlee Sequential process of Mel Frequency Cepstrum Coefficient (MFCC) and Binary Particle Swarm Optimization (BPSO) technique for improving the performance of Multi-Layer Perceptron (MLP) to detect asphyxia diseases through infant cries / Azlee Zabidi
description	Infant asphyxia is a condition caused by inadequate intake of oxygen suffered by newborn babies. Early diagnosis of asphyxia is important to avoid complications such as damage to the brain, organ and tissue or even death. Asphyxia occurs in infants with neurological level disturbance, which is found to affect sound of cry produced by infants. The infant cry signals with asphyxia have distinct patterns which can be recognized with pattern classifiers such as Artificial Neural Network (ANN). This study investigates the performance of feature selection techniques namely F-Ratio, Orthogonal Lest Square (OLS) and Binary Particle Swarm Optimisation (BPSO) analysis in selecting optimal features extracted from feature extraction technique; Mel Frequency Cepstrum Coefficient (MFCC). Mel Frequency Cepstrum Coefficient (MFCC) was employed to extract the significant features from infant cry. The selected MFCC features were then used to train several ANN Multi-Layer Perceptron (MLP). The simulation results showed each method is able to improve classifier performance. Among three method discusses, BPSO was the best feature selection method with 96.03% classification accuracy followed by OLS (94%) and F-Ratio (93.38%).
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Zabidi, Azlee
author_facet	Zabidi, Azlee
author_sort	Zabidi, Azlee
title	Sequential process of Mel Frequency Cepstrum Coefficient (MFCC) and Binary Particle Swarm Optimization (BPSO) technique for improving the performance of Multi-Layer Perceptron (MLP) to detect asphyxia diseases through infant cries / Azlee Zabidi
title_short	Sequential process of Mel Frequency Cepstrum Coefficient (MFCC) and Binary Particle Swarm Optimization (BPSO) technique for improving the performance of Multi-Layer Perceptron (MLP) to detect asphyxia diseases through infant cries / Azlee Zabidi
title_full	Sequential process of Mel Frequency Cepstrum Coefficient (MFCC) and Binary Particle Swarm Optimization (BPSO) technique for improving the performance of Multi-Layer Perceptron (MLP) to detect asphyxia diseases through infant cries / Azlee Zabidi
title_fullStr	Sequential process of Mel Frequency Cepstrum Coefficient (MFCC) and Binary Particle Swarm Optimization (BPSO) technique for improving the performance of Multi-Layer Perceptron (MLP) to detect asphyxia diseases through infant cries / Azlee Zabidi
title_full_unstemmed	Sequential process of Mel Frequency Cepstrum Coefficient (MFCC) and Binary Particle Swarm Optimization (BPSO) technique for improving the performance of Multi-Layer Perceptron (MLP) to detect asphyxia diseases through infant cries / Azlee Zabidi
title_sort	sequential process of mel frequency cepstrum coefficient (mfcc) and binary particle swarm optimization (bpso) technique for improving the performance of multi-layer perceptron (mlp) to detect asphyxia diseases through infant cries / azlee zabidi
granting_institution	Universiti Teknologi MARA (UiTM)
granting_department	Faculty of Electrical Engineering
publishDate	2012
url	https://ir.uitm.edu.my/id/eprint/20425/6/20425.pdf
_version_	1783733729350385664

Sequential process of Mel Frequency Cepstrum Coefficient (MFCC) and Binary Particle Swarm Optimization (BPSO) technique for improving the performance of Multi-Layer Perceptron (MLP) to detect asphyxia diseases through infant cries / Azlee Zabidi

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