Objective To conduct a systematic review of deep learning models for electronic health
record (EHR) data, and illustrate various deep learning architectures for analyzing different …

Time series clustering is an essential unsupervised technique in cases when category
information is not available. It has been widely applied to genome data, anomaly detection …

This paper presents a novel method for imputing missing data of multivariate time series by
adapting the Long Short Term-Memory (LSTM) and Denoising Autoencoder (DAE). Missing …

Many of the current scientific advances in the life sciences have their origin in the intensive
use of data for knowledge discovery. In no area this is so clear as in bioinformatics, led by …

Understanding the statistics of fluctuation driven flows in the boundary layer of magnetically
confined plasmas is desired to accurately model the lifetime of the vacuum vessel …

Time Series data are used in many different fields such as science, engineering, finance or
business for tasks like astronomical object classification, economic indicator analysis or …

In this research, we studied how to extract features vector from the Multivariate Time Series
dataset (MTS) by using various types of Autoencoder. Do the classification method from …