A Hybrid Knowledge-based Risk Prediction Method Using Fuzzy Logic and CBR for Avian Influenza Early Warning

被引:0
作者
Zhang, Jie [1 ]
Lu, Jie [1 ]
Zhang, Guangquan [1 ]
机构
[1] Univ Technol Sydney, Decis Syst & E Serv Intelligence Lab, Ctr Quantum Computat & Intelligent Syst, Fac Engn & Informat Technol, Broadway, NSW 2007, Australia
基金
澳大利亚研究理事会;
关键词
Case-based reasoning; knowledge-based systems; fuzzy logic; avian influenza; early warning systems; risk analysis; SYSTEM; H5N1; MIGRATION; EPIDEMIC; FAILURE; SPREAD; CRISES;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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 vulnerabilities and implement relevant strategies. An example is presented that illustrates the capabilities and procedures of the proposed method in avian influenza early warning systems.
引用
收藏
页码:363 / 386
页数:24
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