With the exponential development of the number of users browsing the internet, an important
factor that now the developer community is focussing on is the user experience …

Wind speed forecasting models have seen significant development and growth in recent
years. In particular, hybrid models have been emerging since the last decade. Hybrid …

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 …

In existing recommendation systems, there exist the issues of “cold start” and “excessively
mature recommendation,” which cause the recommendation systems to have weak …

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 …

Transfer learning (TL) has grown popular in recent years. It is effective to improve the
classification accuracy in the target domain by using the training knowledge in the related …

With the advent of big data, data is being generated, collected, transformed, processed and
analyzed at an unprecedented scale. Since data is created at a fast velocity and with a large …

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

Disentangling patients' behavioral variations is a critical step for better understanding an
intervention's effects on individual outcomes. Missing data commonly exist in longitudinal …

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 …