This paper is to provide practical options for prognostics so that beginners can select
appropriate methods for their fields of application. To achieve this goal, several popular …

This survey presents the most relevant neural network models of autism spectrum disorder
and schizophrenia, from the first connectionist models to recent deep neural network …

In this paper, we propose a variety of Long Short-Term Memory (LSTM) based models for
sequence tagging. These models include LSTM networks, bidirectional LSTM (BI-LSTM) …

Neural networks have been extensively applied to short-term traffic prediction in the past
years. This study proposes a novel architecture of neural networks, Long Short-Term Neural …

Automatic speech recognition (ASR) is one of the most demanding tasks in natural language
processing owing to its complexity. Recently, deep learning approaches have been …

Deep learning has gained much success in sentence-level relation classification. For
example, convolutional neural networks (CNN) have delivered competitive performance …

Congestion on road networks has a negative impact on sustainability in many cities through
the exacerbation of air pollution. Smart congestion management allows road users to avoid …

As the amount of unstructured text data that humanity produces overall and on the Internet
grows, so does the need to intelligently to process it and extract different types of knowledge …

We present several modifications of the original recurrent neural network language model
(RNN LM). While this model has been shown to significantly outperform many competitive …

In this paper, we propose a method for predicting final grades of students by a Recurrent
Neural Network (RNN) from the log data stored in the educational systems. We applied this …