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Threat analysis using artificial neural network

The purpose of this study is to explore the use of an Artificial Neural Network threat analysis tools for analyzing threats in healthcare system. The research method used a feed forward neural network which consisted of 50 input variables and one output. The datasets used in neural network are provi...

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Bibliographic Details
Main Author:	Yee, Chan Pheng
Format:	Thesis
Language:	English
Published:	2009
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://eprints.utm.my/id/eprint/10076/1/YeeChanPengMFSKSM2009.pdf http://eprints.utm.my/id/eprint/10076/
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id	my.utm.10076
record_format	eprints
spelling	my.utm.100762018-06-25T01:31:00Z http://eprints.utm.my/id/eprint/10076/ Threat analysis using artificial neural network Yee, Chan Pheng QA75 Electronic computers. Computer science The purpose of this study is to explore the use of an Artificial Neural Network threat analysis tools for analyzing threats in healthcare system. The research method used a feed forward neural network which consisted of 50 input variables and one output. The datasets used in neural network are provided by previous research conducted in one of the Government Supported Hospital. The neural network is trained with the datasets and performed prediction. In order to test the accuracy of ANN prediction, internal validation will be made. Six experiments conducted and the mean square error used as a scale to measure the accuracy of prediction. First three experiments which with 50 input variables and one output used 80%, 60% and 40% of data for training. While the last three experiments change the number of input variables to 15 and use 80%, 60% and 40% of data for training. The results between the six experiments were compared. It was discovered that when the size of trained data reduced, the MSE value increased. In contrast, while the size of trained data increased, the MSE value decreased. Lower MSE value means better prediction. Overall, the accuracy of prediction for artificial neural network is high. The changes in the number of input variables will not affect the power of ANN prediction. However, quantity of data is one of the important factors that affect ANN prediction result. With larger data size, the ANN prediction is more accurate. 2009-04 Thesis NonPeerReviewed application/pdf en http://eprints.utm.my/id/eprint/10076/1/YeeChanPengMFSKSM2009.pdf Yee, Chan Pheng (2009) Threat analysis using artificial neural network. Masters thesis, Universiti Teknologi Malaysia, Faculty of Computer Science and Information System.
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
language	English
topic	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Yee, Chan Pheng Threat analysis using artificial neural network
description	The purpose of this study is to explore the use of an Artificial Neural Network threat analysis tools for analyzing threats in healthcare system. The research method used a feed forward neural network which consisted of 50 input variables and one output. The datasets used in neural network are provided by previous research conducted in one of the Government Supported Hospital. The neural network is trained with the datasets and performed prediction. In order to test the accuracy of ANN prediction, internal validation will be made. Six experiments conducted and the mean square error used as a scale to measure the accuracy of prediction. First three experiments which with 50 input variables and one output used 80%, 60% and 40% of data for training. While the last three experiments change the number of input variables to 15 and use 80%, 60% and 40% of data for training. The results between the six experiments were compared. It was discovered that when the size of trained data reduced, the MSE value increased. In contrast, while the size of trained data increased, the MSE value decreased. Lower MSE value means better prediction. Overall, the accuracy of prediction for artificial neural network is high. The changes in the number of input variables will not affect the power of ANN prediction. However, quantity of data is one of the important factors that affect ANN prediction result. With larger data size, the ANN prediction is more accurate.
format	Thesis
author	Yee, Chan Pheng
author_facet	Yee, Chan Pheng
author_sort	Yee, Chan Pheng
title	Threat analysis using artificial neural network
title_short	Threat analysis using artificial neural network
title_full	Threat analysis using artificial neural network
title_fullStr	Threat analysis using artificial neural network
title_full_unstemmed	Threat analysis using artificial neural network
title_sort	threat analysis using artificial neural network
publishDate	2009
url	http://eprints.utm.my/id/eprint/10076/1/YeeChanPengMFSKSM2009.pdf http://eprints.utm.my/id/eprint/10076/
_version_	1643645319201685504
score	13.211869

Threat analysis using artificial neural network

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