This paper reviews the recent literature on machine learning (ML) models that have been
used for condition monitoring in wind turbines (eg blade fault detection or generator …

Detecting and repairing dirty data is one of the perennial challenges in data analytics, and
failure to do so can result in inaccurate analytics and unreliable decisions. Over the past few …

An outlier is a datum that is far from other data points in which it occurs. It can have a
considerable impact on the output. Therefore, removing or resolving it before the analysis is …

Detecting outliers is a significant problem that has been studied in various research and
application areas. Researchers continue to design robust schemes to provide solutions to …

An integrated framework for density-based cluster analysis, outlier detection, and data
visualization is introduced in this article. The main module consists of an algorithm to …

Outliers are also referred to as abnormalities, discordants, deviants, or anomalies in the data
mining and statistics literature. In most applications, the data is created by one or more …

Traditional outlier detection methods create a model for data and then label as outliers for
objects that deviate significantly from this model. However, when dat has many outliers …

Building operations account for the largest proportion of energy use throughout the building
life cycle. The energy saving potential is considerable taking into account the existence of a …

Thermophysical properties of fluid mixtures are important in many fields of science and
engineering. However, experimental data are scarce in this field, so prediction methods are …

Many outlier detection methods do not merely provide the decision for a single data object
being or not being an outlier but give also an outlier score or" outlier factor" signaling" how …