A hybrid knowledge-based risk prediction method using fuzzy logic and CBR for avian influenza early warning

Zhang, J; Lu, J; Zhang, G

A hybrid knowledge-based risk prediction method using fuzzy logic and CBR for avian influenza early warning

Zhang, J Lu, J

Zhang, G

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Publication Type:: Journal Article
Citation:: Journal of Multiple-Valued Logic and Soft Computing, 2011, 17 (4), pp. 363 - 386
Issue Date:: 2011-05-16

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Field	Value	Language
dc.contributor.author	Zhang, J	en_US
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732	en_US
dc.contributor.author	Zhang, G https://orcid.org/0000-0003-3960-0583	en_US
dc.date.issued	2011-05-16	en_US
dc.identifier.citation	Journal of Multiple-Valued Logic and Soft Computing, 2011, 17 (4), pp. 363 - 386	en_US
dc.identifier.issn	1542-3980	en_US
dc.identifier.uri	http://hdl.handle.net/10453/31153
dc.description.abstract	The threat of highly pathogenic avian influenza persists, with the size of the epidemic growing worldwide. Various methods have been applied to measure and predict the threat. This paper outlines our research which develops a knowledge-based method that makes full use of previous knowledge to perform a comprehensive forecast of the risk of avian influenza and generate reliable warning signals for a specific region at a specific time. The method contains a risk estimation model and a knowledge-based prediction method using fuzzy logic and case-based reasoning (CBR) to generate timely early warnings to support decision makers to identify underlying ulnerabilities and implement relevant strategies. An example is presented that illustrates the capabilities and procedures of the proposed method in avian influenza early warning systems. © 2011 Old City Publishing, Inc.	en_US
dc.relation.ispartof	Journal of Multiple-Valued Logic and Soft Computing	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	A hybrid knowledge-based risk prediction method using fuzzy logic and CBR for avian influenza early warning	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	17	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	0803 Computer Software	en_US
dc.location.activity	WOS:000291082800006
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.issue	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	17	en_US

Abstract:

The threat of highly pathogenic avian influenza persists, with the size of the epidemic growing worldwide. Various methods have been applied to measure and predict the threat. This paper outlines our research which develops a knowledge-based method that makes full use of previous knowledge to perform a comprehensive forecast of the risk of avian influenza and generate reliable warning signals for a specific region at a specific time. The method contains a risk estimation model and a knowledge-based prediction method using fuzzy logic and case-based reasoning (CBR) to generate timely early warnings to support decision makers to identify underlying ulnerabilities and implement relevant strategies. An example is presented that illustrates the capabilities and procedures of the proposed method in avian influenza early warning systems. © 2011 Old City Publishing, Inc.

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http://hdl.handle.net/10453/31153