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ACCEPT: Introduction of the Adverse Condition and Critical Event Prediction ToolboxThe prediction of anomalies or adverse events is a challenging task, and there are a variety of methods which can be used to address the problem. In this paper, we introduce a generic framework developed in MATLAB (sup registered mark) called ACCEPT (Adverse Condition and Critical Event Prediction Toolbox). ACCEPT is an architectural framework designed to compare and contrast the performance of a variety of machine learning and early warning algorithms, and tests the capability of these algorithms to robustly predict the onset of adverse events in any time-series data generating systems or processes.
Document ID
20150023003
Document Type
Technical Memorandum (TM)
Authors
Martin, Rodney A. (NASA Ames Research Center Moffett Field, CA United States)
Santanu, Das (University Affiliated Research Center (Calif. Univ. Santa Cruz) Moffett Field, CA, United States)
Janakiraman, Vijay Manikandan (University Affiliated Research Center (Calif. Univ. Santa Cruz) Moffett Field, CA, United States)
Hosein, Stefan (University of the West Indies Saint Augustine, Trinidad and Tobago)
Date Acquired
December 15, 2015
Publication Date
November 1, 2015
Subject Category
Cybernetics, Artificial Intelligence and Robotics
Computer Programming and Software
Statistics and Probability
Report/Patent Number
ARC-E-DAA-TN21456
NASA/TM-2015-218927
Funding Number(s)
WBS: WBS 999182.02.60.01.01
CONTRACT_GRANT: NAS2-03144
Distribution Limits
Public
Copyright
Public Use Permitted.
Keywords
machine learning
algorithms
early warning systems

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