The use of medical data for machine learning, including unsupervised methods, such as
clustering, is often restricted by privacy regulations, such as the health insurance portability …

Wearable sensors have gathered tremendous interest for a plethora of applications, yet
there is a void of robust and accurate wearable systems for reliable drug monitoring …

Web-delivered trials are an important component in eHealth services. These trials, mostly
behavior-based, generate big heterogeneous data that are longitudinal, high dimensional …

The clustering of incomplete patterns is a very challenging task because the estimations
may negatively affect the distribution of real centers and thus cause uncertainty and …

Missing data are common in longitudinal observational and randomized controlled trials in
smart health studies. Multiple-imputation based fuzzy clustering is an emerging non …

Web-delivered clinical trials generate big complex data. To help untangle the heterogeneity
of treatment effects, unsupervised learning methods have been widely applied. However …

The existence of some differences in the results obtained from varying clustering k-means
algorithms necessitated the need for a simplified approach in validation of cluster quality …

Big longitudinal data provide more reliable information for decision making and are common
in all kinds of fields. Trajectory pattern recognition is in an urgent need to discover important …

Real-world datasets, particularly Electronic Health Records, are routinely found to be mixed
(comprised of both categorical and continuous variables) and/or missing in nature. Such …

With the popularity of distributed photovoltaic systems and the large growth of data collected
in the systems, it is quite necessary to make some data processing operations. In this paper …