In the past 30 years Malaysian Industry has progressed a lot and emerged as a key player in maintaining the economical stability. Despite providing efficient solutions for Malaysian economical progress most of the major industries face a nemesis in the form of Noise. The high frequency noise emitted from the machines is causing adverse side-effects to the health of the blue-collared employees. One of the most common health injury caused due to exposure to high frequency noise is known as Noise-Induced Hearing Loss (NIHL). ...
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In the past 30 years Malaysian Industry has progressed a lot and emerged as a key player in maintaining the economical stability. Despite providing efficient solutions for Malaysian economical progress most of the major industries face a nemesis in the form of Noise. The high frequency noise emitted from the machines is causing adverse side-effects to the health of the blue-collared employees. One of the most common health injury caused due to exposure to high frequency noise is known as Noise-Induced Hearing Loss (NIHL). Several studies have been conducted to find the major factors involved in causing NIHL in humans using Artificial Neural Networks. But these studies have neglected some important factors that play a major role in causing hearing loss. In this book, Gradient Descent with Adaptive Momentum (GDAM) algorithm is used to predict the NIHL in TNB workers. GDAM shows promising results and will be helpful in improving the declining hearing condition of industrial workers in Malaysia. The achievements made by GDAM has paved way for indicating NIHL in workers before it becomes severe and cripples him for life.
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