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Proceedings Paper

Machine intelligence-based decision-making (MIND) for automatic anomaly detection
Author(s): Nadipuram R. Prasad; Jason C. King; Thomas Lu
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Paper Abstract

Any event deemed as being out-of-the-ordinary may be called an anomaly. Anomalies by virtue of their definition are events that occur spontaneously with no prior indication of their existence or appearance. Effects of anomalies are typically unknown until they actually occur, and their effects aggregate in time to show noticeable change from the original behavior. An evolved behavior would in general be very difficult to correct unless the anomalous event that caused such behavior can be detected early, and any consequence attributed to the specific anomaly. Substantial time and effort is required to back-track the cause for abnormal behavior and to recreate the event sequence leading to abnormal behavior. There is a critical need therefore to automatically detect anomalous behavior as and when they may occur, and to do so with the operator in the loop. Human-machine interaction results in better machine learning and a better decision-support mechanism. This is the fundamental concept of intelligent control where machine learning is enhanced by interaction with human operators, and vice versa. The paper discusses a revolutionary framework for the characterization, detection, identification, learning, and modeling of anomalous behavior in observed phenomena arising from a large class of unknown and uncertain dynamical systems.

Paper Details

Date Published: 9 April 2007
PDF: 9 pages
Proc. SPIE 6574, Optical Pattern Recognition XVIII, 65740F (9 April 2007); doi: 10.1117/12.723635
Show Author Affiliations
Nadipuram R. Prasad, New Mexico State Univ. (United States)
Jason C. King, Raytheon Missile Systems (United States)
Thomas Lu, Jet Propulsion Lab. (United States)

Published in SPIE Proceedings Vol. 6574:
Optical Pattern Recognition XVIII
David P. Casasent; Tien-Hsin Chao, Editor(s)

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