Persistent Link:
http://hdl.handle.net/10150/581675
Title:
An Autonomous Machine Learning Approach for Global Terrorist Recognition
Author:
Hill, Jerry L.; Mora, Randall P.
Affiliation:
Avum, Inc.
Issue Date:
2012-10
Rights:
Copyright © held by the author; distribution rights International Foundation for Telemetering
Collection Information:
Proceedings from the International Telemetering Conference are made available by the International Foundation for Telemetering and the University of Arizona Libraries. Visit http://www.telemetry.org/index.php/contact-us if you have questions about items in this collection.
Publisher:
International Foundation for Telemetering
Journal:
International Telemetering Conference Proceedings
Abstract:
A major intelligence challenge we face in today's national security environment is the threat of terrorist attack against our national assets, especially our citizens. This paper addresses global reconnaissance which incorporates an autonomous Intelligent Agent/Data Fusion solution for recognizing potential risk of terrorist attack through identifying and reporting imminent persona-oriented terrorist threats based on data reduction/compression of a large volume of low latency data possibly from hundreds, or even thousands of data points.
Keywords:
reconnaissance; data fusion; intelligent agent; service-based framwork; data compression; derived data; machine learning
Sponsors:
International Foundation for Telemetering
ISSN:
0884-5123; 0074-9079
Additional Links:
http://www.telemetry.org/

Full metadata record

DC FieldValue Language
dc.language.isoen_USen
dc.titleAn Autonomous Machine Learning Approach for Global Terrorist Recognitionen_US
dc.contributor.authorHill, Jerry L.en
dc.contributor.authorMora, Randall P.en
dc.contributor.departmentAvum, Inc.en
dc.date.issued2012-10en
dc.rightsCopyright © held by the author; distribution rights International Foundation for Telemeteringen_US
dc.description.collectioninformationProceedings from the International Telemetering Conference are made available by the International Foundation for Telemetering and the University of Arizona Libraries. Visit http://www.telemetry.org/index.php/contact-us if you have questions about items in this collection.en_US
dc.publisherInternational Foundation for Telemeteringen
dc.description.abstractA major intelligence challenge we face in today's national security environment is the threat of terrorist attack against our national assets, especially our citizens. This paper addresses global reconnaissance which incorporates an autonomous Intelligent Agent/Data Fusion solution for recognizing potential risk of terrorist attack through identifying and reporting imminent persona-oriented terrorist threats based on data reduction/compression of a large volume of low latency data possibly from hundreds, or even thousands of data points.en
dc.subjectreconnaissanceen
dc.subjectdata fusionen
dc.subjectintelligent agenten
dc.subjectservice-based framworken
dc.subjectdata compressionen
dc.subjectderived dataen
dc.subjectmachine learningen
dc.description.sponsorshipInternational Foundation for Telemeteringen
dc.identifier.issn0884-5123en
dc.identifier.issn0074-9079en
dc.identifier.urihttp://hdl.handle.net/10150/581675en
dc.identifier.journalInternational Telemetering Conference Proceedingsen
dc.typetexten
dc.typeProceedingsen
dc.relation.urlhttp://www.telemetry.org/en
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