In the news portal text, there is a lot of important information such as name person, organization, or place. To obtain in text documents manually, humans must read contents entire text. overcome this issue, extraction, commonly called Named Entity Recognition (NER) was used. The extraction expressly for NER field used to make it easier process word sentence data. It helps search engines and al...

In this paper, the problem of sentiment analysis on Amazon products is tackled. fact, systems are applied in all business and social fields. This because opinions at center human activities, they key influencers our behaviors. study, recurrent neural network (RNN) model used to classify reviews. Three review datasets were predict sentiments authors. conclusion, results comparable best state art...

In this paper, we explore the inclusion of latent random variables into the hidden state of a recurrent neural network (RNN) by combining the elements of the variational autoencoder. We argue that through the use of high-level latent random variables, the variational RNN (VRNN)1 can model the kind of variability observed in highly structured sequential data such as natural speech. We empiricall...

This paper presents prototyping of a recurrent type neural network (RNN) convolutional decoder using system-level design specification and design flow that enables easy mapping to the target FPGA architecture. Implementation and the performance measurement results have shown that an RNN decoder for hard-decision decoding coupled with a simple hard-limiting neuron activation function results in ...

We apply a general recurrent neural network (RNN) encoder framework to community question answering (cQA) tasks. Our approach does not rely on any linguistic processing, and can be applied to different languages or domains. Further improvements are observed when we extend the RNN encoders with a neural attention mechanism that encourages reasoning over entire sequences. To deal with practical i...

Dealing with high-dimensional input spaces, like visual input, is a challenging task for reinforcement learning (RL). Neuroevolution (NE), used for continuous RL problems, has to either reduce the problem dimensionality by (1) compressing the representation of the neural network controllers or (2) employing a pre-processor (compressor) that transforms the high-dimensional raw inputs into low-di...

Generation of desired trajectory behavior using neural networks involves a particularly challenging spatio-temporal learning problem. This paper introduces a novel solution, i.e., designing a dynamic system whose terminal behavior emulates a prespecified spatio-temporal pattern independently of its initial conditions. The proposed solution uses a dynamic neural network (DNN), a hybrid architect...

in this paper, a recurrent fuzzy-neural network (rfnn) controller with neural network identifier in direct control model is designed to control the speed and exhaust temperature of the gas turbine in a combined cycle power plant. since the turbine operation in combined cycle unit is considered, speed and exhaust temperature of the gas turbine should be simultaneously controlled by fuel command ...

Convolutional neural network (CNN) models have achieved tremendous success in many visual detection and recognition tasks. Unfortunately, visual tracking, a fundamental computer vision problem, is not handled well using the existing CNN models, because most object trackers implemented with CNN do not effectively leverage temporal and contextual information among consecutive frames. Recurrent ne...

This paper describes a simple and efficient Neural Language Model approach for text classification that relies only on unsupervised word representation inputs. Our model employs Recurrent Neural Network Long Short-Term Memory (RNN-LSTM), on top of pre-trained word vectors for sentence-level classification tasks. In our hypothesis we argue that using word vectors obtained from an unsupervised ne...