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- W1518869062 abstract "Response data from microhotplate (MHP) sensor arrays were measured for various chemical warfare (CW) agents in several concentrations in dry hair. Efficient large-scale artificial neural networks (ANN) modeling has been evaluated as a method for the classification and concentration prediction of the CW agents based on the MHP data. Four MHP sensor elements, two pairs of SnO/sub 2/ and two pairs of TiO/sub 2/ were operated in a pulsed, ramped temperature mode to generate the data used. The CW agents and related compounds tested were tabun (GA), sarin (GB), sulfur mustard (HD), and chloroethyl-ethyl-sulfide (CES), in four concentration levels in dry hair, between several nmole/mole (ppb) to several /spl mu/mole/mole (ppm). Recursive ANN pruning and re-training techniques were used to identify the more relevant inputs, among the original 80 inputs (different sensor elements and temperatures). ANN models with 6-15 inputs produced good classification between the different CW agents. Other ANN models, trained for each agent, gave good prediction values for the concentrations of the CW agents." @default.
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- W1518869062 date "2004-03-22" @default.
- W1518869062 modified "2023-09-26" @default.
- W1518869062 title "Artificial neural networks methods applied to conductometric microhotplate data for the identification of the type and relative concentration of chemical warfare agents" @default.
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- W1518869062 doi "https://doi.org/10.1109/ijcnn.2003.1223838" @default.
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