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The abstraction tasks are challenging for multi-modal sequences as they require a deeper semantic understanding and a novel text generation for the data. Although the recurrent neural networks (RNN) can be used to model the context of the time-sequences, in most cases the long-term dependencies of multi-modal data make the back-propagation through time training of RNN tend to vanish in the time domain...
In our data driven world, categorization is of major importance to help end-users and decision makers understanding information structures. Supervised learning techniques rely on annotated samples that are often difficult to obtain and training often overfits. On the other hand, unsupervised clustering techniques study the structure of the data without disposing of any training data. Given the difficulty...
Tourism has become a major sector for economic development on Lombok. Tourist expenditure in Lombok give a good implications on public revenue. Tourist expenditures are not only distributed to the tourism sector, but also to other sectors. Prediction of tourists visit is very important as an information and planning for the future. Prediction is one of very important element in decision, because the...
This work proposes a method for reconstruction of multivariate signals with missing parts of the data. The proposal consists in employing an artificial neural network (ANN), specifically recurrent multilayer perceptron (RMLP), to restore the missing intervals of the multivariate signals. In RMLP network, every neuron receives in puts from every other neuron in the network previous layer. In this approach,...
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