Default ARTMAP Neural Networks for Classification of Anthrax Time Series from Inhalation Anthrax Models
Abstract
The possibility of the usage of deadly aerosolized pathogens, particularly anthrax, in bioterrorist attack has raised tremendous concerns in recent years. Several anthrax incubation models have been introduced in order to characterize the incubation period of human inhalation anthrax. It is important to accurately identify the model that fits best with the observed anthrax time series, which directly affects the prediction results of the severity of the potential anthrax attacks. Here, we applied Default ARTMAP, an important neural network algorithm for classification, to separate anthrax time series generated from different inhalation anthrax models. Experimental results on anthrax time series derived from major inhalation anthrax models, together with anti-patterns and a smallpox time series, demonstrate the effectiveness of Default ARTMAP in identifying anthrax time series derived from different models, as well as discriminating unrelated cases.
Recommended Citation
R. Xu et al., "Default ARTMAP Neural Networks for Classification of Anthrax Time Series from Inhalation Anthrax Models," Proceedings of the 2008 International Conference on Data Mining (2008, Las Vegas, NV), Jul 2008.
Meeting Name
2008 International Conference on Data Mining, DMIN 2008 (2008: Jul. 14-17, Las Vegas, NV)
Department(s)
Electrical and Computer Engineering
Keywords and Phrases
Adaptive Resonance Theory; Anthrax Time Series; Default ARTMAP; Ellipsoid ARTMAP; Fuzzy ARTMAP
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Publication Date
17 Jul 2008
