Outlier detection is a statistical procedure that aims to find suspicious events or items that
are different from the normal form of a dataset. It has drawn considerable interest in the field …

In this paper, a novel, parameter-free, incremental local density and cluster-based outlier
factor (iLDCBOF) method is presented that unifies incremental versions of local outlier factor …

Enormous amount of earth information, gathered from satellite sensors, simulations, and
other resources, are collectively referred to as Big Earth Observation Data (BEOD). The data …

The last decade has seen tremendous improvements in technologies for Type 1 Diabetes
(T1D) management, in particular the so-called artificial pancreas (AP), a wearable closed …

Detecting outliers in real time from multivariate streaming data is a vital and challenging
research topic in many areas. Recently introduced the incremental Local Outlier Factor …

Multi-class outlier detection from multi-class data streams is a crucial yet challenging task for
many real-life applications and services. Various detection methods are available eg …

Various uncertainties arising during acquisition process of geoscience data may result in
anomalous data instances (ie, outliers) that do not conform with the expected pattern of …

Zeltmann, et al. demonstrated that structural integrity and other quality damage to objects
can be caused by changing its position on a 3D printer's build plate. On some printers, for …

In prior work, Zeltmann, et al. demonstrated the negative impact that can be created by
defects of various sizes in 3D printed objects. These defects may make the object unsuitable …

In many practical classification problems, datasets would have a portion of outliers, which
could greatly affect the performance of the constructed models. In order to address this …