scholarly journals Application of an inverse neural network model for the identification of optimal amendment to reduce copper toxicity in phytoremediated contaminated soils

2014 ◽  
Vol 136 ◽  
pp. 14-23 ◽  
Author(s):  
Nour Hattab ◽  
Mikael Motelica-Heino
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Contaminated Soils ◽  
Copper Toxicity

scholarly journals Application of neural network model for the prediction of chromium concentration in phytoremediated contaminated soils

2013 ◽  
Vol 128 ◽  
pp. 25-34 ◽  
Author(s):  
Nour Hattab ◽  
Ridha Hambli ◽  
Mikael Motelica-Heino ◽  
Xavier Bourrat ◽  
Michel Mench
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Contaminated Soils ◽  
Chromium Concentration

Neural Network Model to Calculate the Creep Data Using Size

2017 ◽  
Vol 7 (8) ◽  
pp. 257
Author(s):  
Seetharam .K ◽  
Sharana Basava Gowda ◽  
. Varadaraj
Keyword(s):  
Neural Network ◽  
Risk Factors ◽  
Software Engineering ◽  
Network Model ◽  
Neural Network Model ◽  
Software Metrics ◽  
Creep Data ◽  
Engineering Software ◽  
Function Points ◽  
Different Levels

In Software engineering software metrics play wide and deeper scope. Many projects fail because of risks in software engineering development[1]t. Among various risk factors creeping is also one factor. The paper discusses approximate volume of creeping requirements that occur after the completion of the nominal requirements phase. This is using software size measured in function points at four different levels. The major risk factors are depending both directly and indirectly associated with software size of development. Hence It is possible to predict risk due to creeping cause using size.

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