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International Conference on Information Technology: Coding and Computing
The Investigation of Mercury Presence in Human Blood: An Extrapolation from Animal Data Using Neural Networks
Las Vegas, Nevada
April 08-April 10
ISBN: 0-7695-1506-1
ASCII Text
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Ray R. Hashemi, Mahmood Bahar, Alexander A. Tyler, John Young, "The Investigation of Mercury Presence in Human Blood: An Extrapolation from Animal Data Using Neural Networks," Information Technology: Coding and Computing, International Conference on, pp. 0512, International Conference on Information Technology: Coding and Computing, 2002.
 
 
BibTex
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@article{ 10.1109/ITCC.2002.1000440,
author = {Ray R. Hashemi and Mahmood Bahar and Alexander A. Tyler and John Young},
title = {The Investigation of Mercury Presence in Human Blood: An Extrapolation from Animal Data Using Neural Networks},
journal ={Information Technology: Coding and Computing, International Conference on},
volume = {0},
year = {2002},
isbn = {0-7695-1506-1},
pages = {0512},
doi = {http://doi.ieeecomputersociety.org/10.1109/ITCC.2002.1000440},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Information Technology: Coding and Computing, International Conference on
TI - The Investigation of Mercury Presence in Human Blood: An Extrapolation from Animal Data Using Neural Networks
SN - 0-7695-1506-1
SP
EP
A1 - Ray R. Hashemi,
A1 - Mahmood Bahar,
A1 - Alexander A. Tyler,
A1 - John Young,
PY - 2002
VL - 0
JA - Information Technology: Coding and Computing, International Conference on
ER -
 
Ray R. Hashemi, University of Arkansas at Little Rock and National Center for Toxicological Research
Mahmood Bahar, Teacher Training University
Alexander A. Tyler, University College Cork
John Young, National Center for Toxicological Research
In this research effort a neural network approach was used as a method of extrapolating the presence of mercury in human blood from animal data. We also investigated the effect of different data representations (As-is, Category, Simple binary, Thermometer, and Flag) on the model performance. In addition, we used the Rough Sets methodology to identify the redundant independent variables and then examined the proposed extrapolation model performance for a reduced set of independent variables. Moreover, a quality measure was introduced that revealed that the proposed extrapolation model performed extremely well for the Thermometer data representation.
Citation:
Ray R. Hashemi, Mahmood Bahar, Alexander A. Tyler, John Young, "The Investigation of Mercury Presence in Human Blood: An Extrapolation from Animal Data Using Neural Networks," itcc, pp.0512, International Conference on Information Technology: Coding and Computing, 2002
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