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Comparison of Multilayer Feed Forward Neural Network and Hopfield Neural Network upon Buddhist Iconographies Classification

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dc.contributor.author Aye, Thin Thin
dc.contributor.author Khaing, Thiri Thitsar
dc.date.accessioned 2019-07-31T11:21:07Z
dc.date.available 2019-07-31T11:21:07Z
dc.date.issued 2009-12-30
dc.identifier.uri http://ucsy.edu.mm/onlineresource/handle/123456789/1501
dc.description.abstract This paper proposes two types of artificial neural networks to classify the Buddhist iconographies in order to compare the performances of two networks; multilayer feed forward network and Hopfield network. The quality of the image of Buddhist iconography is improved by smooth filtering. The smooth filtered image is transformed into intensity image. The resultant gray image is edge detected to obtain the abstract graph or the edge of the Buddhist iconography. The resultant image is resized into the defined pixel area. This is the final step of the image preprocessing stage. The resized image is applied into the two networks. Multilayer network is composed of three layers. The input layer accepts the preprocessed images and the certain values of the output layer indicate the type of the Buddhist iconography. Hopfield network consists of a single layer which serves as both input and output layers. The training time and accuracy of the two networks are then analyzed and compared in this proposed paper. en_US
dc.language.iso en en_US
dc.publisher Fourth Local Conference on Parallel and Soft Computing en_US
dc.title Comparison of Multilayer Feed Forward Neural Network and Hopfield Neural Network upon Buddhist Iconographies Classification en_US
dc.type Article en_US


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