Drilling wells in challenging oil/gas environments implies in large capital expenditure on
wellbore's construction. In order to optimize the drilling related operation, real-time decisions …

Wearable devices use sensors to evaluate physiological parameters, such as the heart rate,
pulse rate, number of steps taken, body fat and diet. The continuous monitoring of …

Anomaly detection for time-series data has been an important research field for a long time.
Seminal work on anomaly detection methods has been focussing on statistical approaches …

In the current modern era of information and technology, emerging remote advancements
have been widely established for detailed virtual inspections and assessments of …

Imbalanced data typically refers to a condition in which several data samples in a certain
problem is not equally distributed, thereby leading to the underrepresentation of one or more …

This paper proposes a robust classification model, based on support vector machine (SVM),
which simultaneously deals with outliers detection and feature selection. The classifier is …

Over the past two decades, the world has witnessed some of the worst corporate accounting
fraud incidents, starting with notable cases like Enron and Arthur Andersen in 2001, and …

Ensemble docking in drug discovery or chemical biology uses dynamical simulations of
target proteins to generate binding site conformations for docking campaigns. We show that …

The surface water temperature is a vital ecological and climate variable, and its monitoring is
critical. An extensive sensor network measures the ocean, but outliers pervade the …

Multidimensional times series prediction is a challenging task. Only recently the increased
data availability has made it possible to tackle with such problems. In this work we devised a …