Timor Leste Tais Motif Recognition Using Wavelet and Backpropagation

Timor Leste is a new country of the 21st century in Southeast Asia that has a diverse culture. Tais Timor Leste has a high historical value as well as cultural identity. It is also one of the cultural heritages of Timor Leste. Tais Timor has its own characteristics and meanings in every motif, but there are still many communities of Timor Leste as well as foreign tourists who do not know the variety of the motif. Therefore, this study aimed to establish the system recognition of Tais Timor motif through the image based on the type of motif. The wavelet transform is used in the process of feature extraction and image decomposition to obtain coefficient values of which the value of energy and entropy will then be calculated. For the recognition of Tais Timor motif, backpropagation algorithm was used. This application is built using MATLAB programming language. The analysis and testing of these studies show that the accuracy of recognition of Tais Timor motif with 4 testing parameters got recognition accuracy and presentation of 80%. Thus the motif used can be identified by using both wavelet transform and backpropagation algorithm

Prediction of Wheat Stripe Rust Based on Neural Networks

Part 1: GIS, GPS, RS and Precision FarmingInternational audienceStripe rust caused byPuccinia striiformis f. sp.tritici, is a devastating wheat disease in the world. The prediction of this disease is very important to make control strategies. In order to figure out suitable prediction methods based on neural networks that could provide accurate prediction information with high stability, the predictions of wheat stripe rust by using backpropagation networks with different transfer functions, training functions and learning functions, radial basis networks, generalized regression networks (GRNNs) and probabilistic neural networks (PNNs) were conducted in this study. The results indicated that suitable backpropagation networks, radial basis networks and GRNNs could be used for the prediction of wheat stripe rust. Good fitting accuracy and prediction accuracy could be obtained by using backpropagation networks with trainlm, trainrp or trainbfg as training function. Radial basis networks had more power than backpropagation networks and GRNNs to predict wheat stripe rust. GRNNs were easier to be used than backpropagation networks. New methods based on neural networks were provided for the prediction of wheat stripe rust