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Feature reduction of Zernike moments using genetic algorithm for neural network classification of rice grain

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6 Author(s)
Chong Yaw Wee ; Fac. of Eng., Malaya Univ., Kuala Lumpur, Malaysia ; Raveendran, P. ; Takeda, F. ; Tsuzuki, T.
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In this paper, Zernike moment features extracted from rice grains are used in classifying normal and damaged rice. Genetic algorithm (GA) is used to reduce the number of features while maximizing the classification performance. The GA chromosome fitness is evaluated using a multilayer perceptron (MLP) trained by backpropagation learning algorithm

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Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on  (Volume:1 )

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