A hybrid Artificial Neural Network-Genetic Algorithm approach for classification of microarray data

2015 ◽  
Author(s):  
Mehmet Bilen ◽  
Ali Hakan Isik ◽  
Tuncay Yigit
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Microarray Data ◽  
Genetic Algorithm Approach ◽  
Artificial Neural ◽  
Hybrid Artificial Neural Network

On-capillary derivatization using a hybrid artificial neural network-genetic algorithm approach

The Analyst ◽  
2009 ◽  
Vol 134 (10) ◽  
pp. 2067 ◽  
Author(s):  
Toni Riveros ◽  
Grady Hanrahan ◽  
Sarah Muliadi ◽  
Jennifer Arceo ◽  
Frank A. Gomez
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Genetic Algorithm Approach ◽  
Artificial Neural ◽  
Hybrid Artificial Neural Network

Development of a Hybrid Artificial Neural Network and Genetic Algorithm Model for Regime Identification of Slurry Transport in Pipelines

2009 ◽  
Vol 4 (1) ◽  
Author(s):  
Sandip K Lahiri ◽  
Kartik Chandra Ghanta
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Operating Conditions ◽  
Misclassification Error ◽  
Wide Range ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Pipeline Design ◽  
Hybrid Artificial Neural Network

Four distinct regimes were found existent (namely sliding bed, saltation, heterogeneous suspension and homogeneous suspension) in slurry flow in pipeline depending upon the average velocity of flow. In the literature, few numbers of correlations has been proposed for identification of these regimes in slurry pipelines. Regime identification is important for slurry pipeline design as they are the prerequisite to apply different pressure drop correlation in different regime. However, available correlations fail to predict the regime over a wide range of conditions. Based on a databank of around 800 measurements collected from the open literature, a method has been proposed to identify the regime using artificial neural network (ANN) modeling. The method incorporates hybrid artificial neural network and genetic algorithm technique (ANN-GA) for efficient tuning of ANN meta parameters. Statistical analysis showed that the proposed method has an average misclassification error of 0.03%. A comparison with selected correlations in the literature showed that the developed ANN-GA method noticeably improved prediction of regime over a wide range of operating conditions, physical properties, and pipe diameters.

Batch growth of Kluyveromyces lactis cells from deproteinized whey: Response surface methodology versus Artificial neural network—Genetic algorithm approach

2016 ◽  
Vol 109 ◽  
pp. 305-311 ◽  
Author(s):  
Fábio Coelho Sampaio ◽  
Tamara Lorena da Conceição Saraiva ◽  
Gabriel Dumont de Lima e Silva ◽  
Janaína Teles de Faria ◽  
Cristiano Grijó Pitangui ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Kluyveromyces Lactis ◽  
Genetic Algorithm Approach ◽  
Artificial Neural ◽  
Batch Growth
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