Predicting noise-induced hearing loss (NIHL) in TNB workers using GDAM algorithm
Noise is a form of a pollutant that is terrorizing the occupational health experts for many decades due to its adverse side-effects on the workers in the industry. Noise�Induced Hearing Loss (NIHL) handicap is one out of many health hazards caused due to excessive exposure to high frequency nois...
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| Format: | Thesis |
| Language: | English English English |
| Published: |
2012
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/2467/1/24p%20SYED%20MUHAMMAD%20ZUBAIR%20REHMAN%20GILLANI.pdf http://eprints.uthm.edu.my/2467/2/SYED%20MUHAMMAD%20ZUBAIR%20REHMAN%20GILLANI%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/2467/3/SYED%20MUHAMMAD%20ZUBAIR%20REHMAN%20GILLANI%20WATERMARK.pdf |
| Summary: | Noise is a form of a pollutant that is terrorizing the occupational health experts for
many decades due to its adverse side-effects on the workers in the industry. Noise�Induced Hearing Loss (NIHL) handicap is one out of many health hazards caused
due to excessive exposure to high frequency noise emitted from the machines. A
number of studies have been carried-out to find the significant factors involved in
causing NIHL in industrial workers using Artificial Neural Networks (ANN). Despite
providing useful information on hearing loss, these studies have neglected some
important factors.
The traditional Back-propagation Neural Network (BPNN) is a supervised
Artificial Neural Networks (ANN) algorithm. It is widely used in solving many real
time problems in world. But BPNN possesses a problem of slow convergence and
network stagnancy. Previously, several modifications were suggested to improve the
convergence rate of Gradient Descent Back-propagation algorithm such as careful
selection of initial weights and biases, learning rate, momentum, network topology,
activation function and ‘gain’ value in the activation function.
This research proposed an algorithm for improving the current working
performance of Back-propagation algorithm by adaptively changing the momentum
value and at the same time keeping the ‘gain’ parameter fixed for all nodes in the
neural network. The performance of the proposed method known as ‘Gradient
Descent Method with Adaptive Momentum (GDAM)’ is compared with ‘Gradient
Descent Method with Adaptive Gain (GDM-AG)’ (Nazri, 2007) and ‘Gradient
Descent with Simple Momentum (GDM)’ by performing simulations on
classification problems. The results show that GDAM is a better approach than
previous methods with an accuracy ratio of 1.0 for classification problems like
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Thyroid disease, Heart disease, Breast Cancer, Pima Indian Diabetes, Wine Quality,
Australian Credit-card approval problem and Mushroom problem.
The efficiency of the proposed GDAM is further verified by means of
simulations on Noise-Induced Hearing loss (NIHL) audiometric data obtained from
Tenaga Nasional Berhad (TNB). The proposed GDAM shows improved prediction
results on both ears and will be helpful in improving the declining health condition of
industrial workers in Malaysia. At present, only few studies have emerged to predict
NIHL using ANN but have failed to achieve high accuracy. The achievements made
by GDAM has paved way for indicating NIHL in workers before it becomes severe
and cripples him or her for life. GDAM is also helpful in educating the blue collared
employees to avoid noisy environments and remedies against exposure to excessive
noise can be taken in the future to prevent hearing damage. |
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