Recent developments in high-throughput technologies have accelerated the accumulation
of massive amounts of omics data from multiple sources: genome, epigenome …

Abstract The Restricted Boltzmann Machine (RBM) has aroused wide interest in machine
learning fields during the past decade. This review aims to report the recent developments in …

Electronic medical record (EMR) offers promises for novel analytics. However, manual
feature engineering from EMR is labor intensive because EMR is complex–it contains …

Increasing demand and costs for healthcare, exacerbated by ageing populations and a
great shortage of doctors, are serious concerns worldwide. Consequently, this has …

Increasing demand and costs for healthcare is a challenge because of the high populations
and the difficulty to cover all patients by the available doctors. The healthcare data …

Automated detection of abnormal events in video surveillance is an important task in
research and practical applications. This is, however, a challenging problem due to the …

Automated detection of abnormalities in data has been studied in research area in recent
years because of its diverse applications in practice including video surveillance, industrial …

Anomalies are those deviating significantly from the norm. Thus, anomaly detection amounts
to finding data points located far away from their neighbors, ie, those lying in low-density …

Instance type information is particularly relevant to perform reasoning and obtain further
information about entities in knowledge graphs (KGs). However, during automated or pay-as …

This review aims to report recent developments about deep learning algorithms based on
Restricted Boltzmann Machines (RBMs) and Conditional Random Fields (CRFs). Firstly, we …