Consequences of Data Uncertainty and Data Precision in Artificial Neural Network Sugar Cane Yield Prediction

2007 ◽  
pp. 1147-1154 ◽  
Author(s):  
Héctor F. Satizábal M. ◽  
Daniel R. Jiménez R. ◽  
Andres Pérez-Uribe
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Sugar Cane ◽  
Data Uncertainty ◽  
Cane Yield ◽  
Yield Prediction ◽  
Artificial Neural

scholarly journals Application of Artificial Neural Network for Yield Prediction of Lipase-Catalyzed Synthesis of Dioctyl Adipate

2009 ◽  
Vol 158 (3) ◽  
pp. 722-735 ◽  
Author(s):  
Mohd Basyaruddin Abdul Rahman ◽  
Naz Chaibakhsh ◽  
Mahiran Basri ◽  
Abu Bakar Salleh ◽  
Raja Noor Zaliha Raja Abdul Rahman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Yield Prediction ◽  
Artificial Neural ◽  
Dioctyl Adipate

Seed yield prediction of sesame using artificial neural network

2015 ◽  
Vol 68 ◽  
pp. 89-96 ◽  
Author(s):  
Samad Emamgholizadeh ◽  
M. Parsaeian ◽  
Mehdi Baradaran
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Seed Yield ◽  
Yield Prediction ◽  
Artificial Neural

Performance of Artificial Neural Network for Predicting Fermentation Characteristics in Biosurfactant Production by Bacillus subtilis ATCC 6633 using Sugar Cane Molasses

2011 ◽  
Vol 7 (6) ◽  
Author(s):  
Yousef Rahimi Kashkouli ◽  
Azadeh Mogharei ◽  
Saman Mousavian ◽  
Farzaneh Vahabzadeh
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Bacillus Subtilis ◽  
Sugar Cane ◽  
Model Building ◽  
Biosurfactant Production ◽  
Cane Molasses ◽  
Subtilis Atcc ◽  
Bacillus Subtilis Atcc ◽  
Artificial Neural

Artificial neural network (ANN) was successfully applied to model fermentation parameters for biosurfactant production by Bacillus subtilis ATCC 6633 using sugar cane molasses. Cell growth and biosurfactant production were monitored along the surface activity of the cell-free broth. Response surface methodology (RSM) as a formal statistical model building system was used for the ANN development. The network predicted biosurfactant concentration was 0.381 g/l which showed almost no differences with the relevant experimental value which obtained according to the RSM arrangement. Furthermore, the ANN surface tension reduction was 30.48 mN/m, which was within 3.24% of the experimental value. Comparisons between RSM and the ANN showed preference of using ANN as complementary to RSM and not as a replacement to it.

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