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Integrating Neural Network With Genetic Algorithms For The Classification Plant Disease | ||
| Engineering and Technology Journal | ||
| Article 1, Volume 28, Issue 4, 2010, Pages 686-701 PDF (287.98 K) | ||
| DOI: 10.30684/etj.28.4.5 | ||
| Authors | ||
| Alia Karim Abdul Hassan; Sarah Sadoon Jasim | ||
| Abstract | ||
| In this work Aِِrtificial Neural Network (ANN) is used as a classifier capable of recognizing the most important features of the plant disease, with minimum error value. Genetic algorithm has been used to minimize error values of the ANN classifier. Error value of ANN classifier is defined as more than (%5). This ratio is a threshold (cut-of-value) to determine if GA is executed or not after the ANN classifier execution. Genetic algorithm execution results in either optimal solution (%100) recognition or suggests a modified parameter to the ANN classifier (specifically learning rate and number of neurons).The result obtained from integrating neural network with genetic algorithm for classification plant diseases indicates that the classifier recognizes most of input pattern with accuracy (96%). Integrating neural network with genetic algorithm for classification plant diseases implemented using Visual Basic version 6 programming | ||
| Keywords | ||
| artificial neural network; genetic algorithm | ||
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