A hybrid knowledge-based prediction method for avian influenza early warning

Zhang, J; Lu, J; Zhang, G

A hybrid knowledge-based prediction method for avian influenza early warning

Zhang, J Lu, J

Zhang, G

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Publication Type:: Conference Proceeding
Citation:: Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, 2009, pp. 617 - 622
Issue Date:: 2009-12-01

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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	2009-12-01	en_US
dc.identifier.citation	Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, 2009, pp. 617 - 622	en_US
dc.identifier.isbn	9781424427949	en_US
dc.identifier.issn	1062-922X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/10884
dc.description.abstract	High pathogenic avian influenza remains rampant and the epidemic size has been growing in the world. The early warning system (EWS) for avian influenza becomes increasingly essential to militating against the risk of outbreak crisis. An EWS can generate timely early warnings to support decision makers in identifying underlying vulnerabilities and implementing relevant strategies. This paper addresses this crucial issue and focuses on how to make full use of previous events to perform comprehensive forecasting and generate reliable warning signals. It proposes a hybrid knowledge-based prediction (HKBP) method which combines case-based reasoning (CBR) with the fuzzy logic technique. The method can improve the prediction accuracy for avian influenza in a specific region at a specific time. An example is presented to illustrate the capabilities and procedures of the HKBP method. ©2009 IEEE.	en_US
dc.relation.ispartof	Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics	en_US
dc.relation.isbasedon	10.1109/ICSMC.2009.5346630	en_US
dc.title	A hybrid knowledge-based prediction method for avian influenza early warning	en_US
dc.type	Conference Proceeding
utslib.for	080108 Neural, Evolutionary and Fuzzy Computation	en_US
utslib.for	080105 Expert Systems	en_US
utslib.for	080605 Decision Support and Group Support Systems	en_US
dc.location.activity	San Antonio, Texas, USA	en_US
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.publication-status	Published	en_US

Abstract:

High pathogenic avian influenza remains rampant and the epidemic size has been growing in the world. The early warning system (EWS) for avian influenza becomes increasingly essential to militating against the risk of outbreak crisis. An EWS can generate timely early warnings to support decision makers in identifying underlying vulnerabilities and implementing relevant strategies. This paper addresses this crucial issue and focuses on how to make full use of previous events to perform comprehensive forecasting and generate reliable warning signals. It proposes a hybrid knowledge-based prediction (HKBP) method which combines case-based reasoning (CBR) with the fuzzy logic technique. The method can improve the prediction accuracy for avian influenza in a specific region at a specific time. An example is presented to illustrate the capabilities and procedures of the HKBP method. ©2009 IEEE.

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