Outlier detection (outlier and anomaly are used interchangeably in this review) in non-
independent and identically distributed (non-IID) data refers to identifying unusual or …

Shale gas reservoirs have huge amounts of reserves. Economically evaluating these
reserves is challenging due to complex driving mechanisms, complex drilling and …

This article investigates the applicability domain (AD) of machine learning (ML) models
trained on high-dimensional data, for the prediction of the ideal gas enthalpy of formation …

People nowadays use the internet to project their assessments, impressions, ideas, and
observations about various subjects or products on numerous social networking sites. These …

Economically evaluating shale gas reservoirs, which have huge amounts of reserves, is
challenging because of the intricate driving mechanisms. Decline Curve Analysis (DCA) has …

Outlier detection becomes challenging when data are featured by high-dimension. Using
dimensionality reduction (DR) techniques to discard the irrelevant attributes is a …

Outlier detection is very important in the field of data mining and is applied to various
scenarios, such as financial fraud detection and network intrusion. Traditional outlier …

The fatigue evaluation of the bogie frame is an important part of the structural health
monitoring of the vehicle. During the dynamic stress monitoring, some signal spikes, which …

A useful strategy to perform outlier detection (OD) in highdimensional data, especially in the
presence of multiple classes of outliers, is to decompose the outlier detection problem into a …

This paper presents the implementation of an outlier detection method based on the Grubbs
test, specifically designed for microcontroller applications. The theoretical background for …