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A CBRN detection framework using fuzzy logic
Ahmed Nagy
author
Lusine Mkrtchyan
author
Klaas Van Der Meer
author
2013
Karlsruher Institut fur Technologie
KIT; Baden-Baden
English
Identifying a chemical, biological, radiological, and nuclear incident (CBRN) is a challenge. Evidence and health symptoms resulting from CBRN malevolent incident overlap with other normal non malevolent human activities. However, proper fusion of symptoms and evidence can aid in drawing conclusions with a certain degree of credibility about the existence of an incident. There are two types of incidents directly observable, overt, or indirectly observable, covert, which can be detected from the symptoms and consequences. This paper describes a framework for identifying a CBRN incident from available evidence using a fuzzy belief degree distributed approach. We present two approaches for evidence fusion and aggregation; the first, two level cumulative belief degree (CBD) while the second is ordered weighted aggregation of belief degrees (OWA). The evaluation approach undertaken shows the potential value of the two techniques.
Data mining
Decision support systems
Disaster prevention
Fuzzy set theory
Information systems
Decision supports
Degree of credibility
Disaster management
Distributed approaches
Evaluation approach
Human activities
Ordered weighted aggregations
Potential values
Fuzzy logic
exported from refbase (http://idl.iscram.org/show.php?record=804), last updated on Sun, 09 Aug 2015 05:05:43 +0200
text
http://idl.iscram.org/files/nagy/2013/804_Nagy_etal2013.pdf
AhmedNagy_etal2013
ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management
ISCRAM 2013
T. Comes
F
Fiedrich
editor
10th International ISCRAM Conference on Information Systems for Crisis Response and Management
2013
Karlsruher Institut fur Technologie
KIT; Baden-Baden
conference publication
266
271
9783923704804
2411-3387
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