A CBRN Detection Framework Using Fuzzy Logic

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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.


Original languageEnglish
Title of host publicationISCRAM2013. Conference Proceedings. Book of Papers
Place of PublicationGermany
Publication statusPublished - May 2013
EventISCRAM2013. Information Systems for Crisis Response and Management - KIT, Baden-Baden, Germany
Duration: 12 May 201315 May 2013


ConferenceISCRAM2013. Information Systems for Crisis Response and Management


  • Data mining, Crises management, Decision support, Disaster management, Fuzzy set theory

ID: 74842