A CBRN Detection Framework Using Fuzzy Logic

Research output: Contribution to report/book/conference proceedingsIn-proceedings paperpeer-review

Standard

A CBRN Detection Framework Using Fuzzy Logic. / Nagy, Ahmed; Mkrtchyan, Lusine; van der Meer, Klaas; Turcanu, Catrinel (Peer reviewer).

ISCRAM2013. Conference Proceedings. Book of Papers. Germany, 2013. p. 266-271.

Research output: Contribution to report/book/conference proceedingsIn-proceedings paperpeer-review

Harvard

Nagy, A, Mkrtchyan, L, van der Meer, K & Turcanu, C 2013, A CBRN Detection Framework Using Fuzzy Logic. in ISCRAM2013. Conference Proceedings. Book of Papers. Germany, pp. 266-271, ISCRAM2013. Information Systems for Crisis Response and Management, Baden-Baden, Germany, 2013-05-12.

APA

Nagy, A., Mkrtchyan, L., van der Meer, K., & Turcanu, C. (2013). A CBRN Detection Framework Using Fuzzy Logic. In ISCRAM2013. Conference Proceedings. Book of Papers (pp. 266-271).

Vancouver

Nagy A, Mkrtchyan L, van der Meer K, Turcanu C. A CBRN Detection Framework Using Fuzzy Logic. In ISCRAM2013. Conference Proceedings. Book of Papers. Germany. 2013. p. 266-271

Author

Nagy, Ahmed ; Mkrtchyan, Lusine ; van der Meer, Klaas ; Turcanu, Catrinel. / A CBRN Detection Framework Using Fuzzy Logic. ISCRAM2013. Conference Proceedings. Book of Papers. Germany, 2013. pp. 266-271

Bibtex - Download

@inproceedings{ab0abefbfa6a4133b776697825c098b5,
title = "A CBRN Detection Framework Using Fuzzy Logic",
abstract = "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.",
keywords = "Data mining, Crises management, Decision support, Disaster management, Fuzzy set theory",
author = "Ahmed Nagy and Lusine Mkrtchyan and {van der Meer}, Klaas and Catrinel Turcanu",
note = "Score = 3; ISCRAM2013. Information Systems for Crisis Response and Management ; Conference date: 12-05-2013 Through 15-05-2013",
year = "2013",
month = may,
language = "English",
isbn = "978-3-923704-80-4",
pages = "266--271",
booktitle = "ISCRAM2013. Conference Proceedings. Book of Papers",

}

RIS - Download

TY - GEN

T1 - A CBRN Detection Framework Using Fuzzy Logic

AU - Nagy, Ahmed

AU - Mkrtchyan, Lusine

AU - van der Meer, Klaas

A2 - Turcanu, Catrinel

N1 - Score = 3

PY - 2013/5

Y1 - 2013/5

N2 - 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.

AB - 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.

KW - Data mining

KW - Crises management

KW - Decision support

KW - Disaster management

KW - Fuzzy set theory

UR - http://ecm.sckcen.be/OTCS/llisapi.dll/open/ezp_133064

UR - http://knowledgecentre.sckcen.be/so2/bibref/10995

M3 - In-proceedings paper

SN - 978-3-923704-80-4

SP - 266

EP - 271

BT - ISCRAM2013. Conference Proceedings. Book of Papers

CY - Germany

T2 - ISCRAM2013. Information Systems for Crisis Response and Management

Y2 - 12 May 2013 through 15 May 2013

ER -

ID: 74842