Due to recent advances in the Vehicular Internet of Things (VIoT), a large volume traffic trajectory data has been generated. The is highly unstructured and pre-processing it very cumbersome task, due complexity data. However, accuracy flow learning models depends on quantity quality preprocessed Hence, there significant gap between size benchmarked datasets respective models. Additionally, gen...

In this study, we propose a novel method for training a regression function and apply it to a voice conversion task. The regression function is constructed using a Stacked Joint-Autoencoder (SJAE). Previously, we have used a more primitive version of this architecture for pre-training a Deep Neural Network (DNN). Using objective evaluation criteria, we show that the lower levels of the SJAE per...

In this paper, we propose an unsupervised domain adaptation for Word Sense Disambiguation (WSD) using Stacked Denoising Autoencoder (SdA). SdA is an unsupervised learning method of obtaining the abstract feature set of input data using Neural Network. The abstract feature set absorbs the difference of domains, and thus SdA can solve a problem of domain adaptation. However, SdA does not always c...