scholarly journals Artificial awareness for robots using artificial neural nets to monitor robotic workcells

1997 ◽  
Author(s):  
S.D. Tucker ◽  
L.P. Ray
Keyword(s):  
Neural Nets ◽  
Artificial Neural Nets ◽  
Artificial Neural

Recognition of Emotions in Gait Patterns by Means of Artificial Neural Nets

2008 ◽  
Vol 32 (2) ◽  
pp. 79-92 ◽  
Author(s):  
Daniel Janssen ◽  
Wolfgang I. Schöllhorn ◽  
Jessica Lubienetzki ◽  
Karina Fölling ◽  
Henrike Kokenge ◽  
...  
Keyword(s):  
Neural Nets ◽  
Gait Patterns ◽  
Artificial Neural Nets ◽  
Artificial Neural ◽  
Recognition Of Emotions

Two-dimensional defocusing correction using artificial neural nets

1998 ◽  
Author(s):  
Carlos Ortiz de Solorzano ◽  
Vicente Gonzalez ◽  
Andres Santos ◽  
Francisco del Pozo
Keyword(s):  
Neural Nets ◽  
Two Dimensional ◽  
Artificial Neural Nets ◽  
Artificial Neural

scholarly journals Arrangement of risk group with congenital fetus pathology using artificial neural nets

2003 ◽  
Vol 2 (2) ◽  
pp. 103-109
Author(s):  
O. S. Philippov ◽  
A. A. Kazantseva
Keyword(s):  
Pregnant Women ◽  
Risk Group ◽  
Pregnancy Termination ◽  
Maternal Serum ◽  
High Risk Group ◽  
Neural Nets ◽  
Congenital Defects ◽  
Artificial Neural Nets ◽  
Artificial Neural ◽  
Numeric Scale

The aim of the study is the evaluation of the significance of various risk factors for congenital fetus pathology (congenital defect – CD) and the development of risk numeric scale. 424 pregnant women with fetus CD and 520 pregnant women with fetus without congenital defects have been examined. Artificial neural nets have been used for investigation how various factors effect on pregnancy termination. It has been found that the important factors for congenital fetus defect risk are: age younger 18 years of an pregnant women, age older 35 years, noncarring of pregnancy in anamnesis, complicated clinical course of the first pregnancy trimester, CD cases in a family, ultrasonic markers of chromosome pathology in the first pregnancy trimester. Changes in maternal serum AFP, hCG and uE3 levels and blood flow disorders are important to forming high risk group. A numeric scale for CD risk has been developed on the basis of neuronic net analysis.

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