Browsing by Subject "Neural structures"
Now showing items 1-6 of 6
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Article
Artificial neural network learning: A comparative review
(2002)Various neural learning procedures have been proposed by different researchers in order to adapt suitable controllable parameters of neural network architectures. These can be from simple Hebbian procedures to complicated ...
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Conference Object
Artificial neural networks to investigate the significance of PAPPA and b-hCG for the prediction of chromosomal abnormalities
(2011)A systematic approach has been done, to investigate different neural network structures for the appraisal of the significance of the free b-human chorionic gonadotrophin (b-hCG) and the pregnancy associated plasma protein-A ...
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Article
Ethnicity as a factor for the estimation of the risk for preeclampsia: A neural network approach
(2010)A large number of feedforward neural structures, both standard multilayer and multi-slab schemes have been applied to a large data base of pregnant women, aiming at generating a predictor for the risk of preeclampsia ...
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Conference Object
First trimester diagnosis of trisomy-21 using artificial neural networks
(2010)Langdon Down in 1866 reported on a syndrome in which individuals have skin appearing to be too large for the body, a nose small and a flat face. This is a chromosomal disorder caused by the presence of all or part of an ...
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Conference Object
Neural networks to estimate the risk for preeclampsia occurrence
(2009)A number of neural network schemes have been applied to a large data base of pregnant women, aiming at generating a predictor for the estimation of the risk of occurrence of preeclampsia at an early stage. The database was ...
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Conference Object
Neural networks to investigate the effects of smoking and alcohol abuse on the risk for preeclampsia
(2009)Following the application of a large number of neural network schemes that have been applied to a large data base of pregnant women, aiming at generating a predictor for the risk of preeclampsia occurrence at an early ...