A unifying review of deep and shallow anomaly detection
Deep learning approaches to anomaly detection (AD) have recently improved the state of
the art in detection performance on complex data sets, such as large collections of images or …
the art in detection performance on complex data sets, such as large collections of images or …
An overview of online fake news: Characterization, detection, and discussion
X Zhang, AA Ghorbani - Information Processing & Management, 2020 - Elsevier
Over the recent years, the growth of online social media has greatly facilitated the way
people communicate with each other. Users of online social media share information …
people communicate with each other. Users of online social media share information …
Widespread global increase in intense lake phytoplankton blooms since the 1980s
Freshwater blooms of phytoplankton affect public health and ecosystem services globally,.
Harmful effects of such blooms occur when the intensity of a bloom is too high, or when toxin …
Harmful effects of such blooms occur when the intensity of a bloom is too high, or when toxin …
Hyperspectral anomaly detection: A survey
Hyperspectral imagery can obtain hundreds of narrow spectral bands of ground objects. The
abundant and detailed spectral information offers a unique diagnostic identification ability for …
abundant and detailed spectral information offers a unique diagnostic identification ability for …
The Burden of Proof studies: assessing the evidence of risk
Exposure to risks throughout life results in a wide variety of outcomes. Objectively judging
the relative impact of these risks on personal and population health is fundamental to …
the relative impact of these risks on personal and population health is fundamental to …
A review of local outlier factor algorithms for outlier detection in big data streams
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 …
are different from the normal form of a dataset. It has drawn considerable interest in the field …
Data fusion and machine learning for industrial prognosis: Trends and perspectives towards Industry 4.0
The so-called “smartization” of manufacturing industries has been conceived as the fourth
industrial revolution or Industry 4.0, a paradigm shift propelled by the upsurge and …
industrial revolution or Industry 4.0, a paradigm shift propelled by the upsurge and …
Construction of the average variance extracted index for construct validation in structural equation models with adaptive regressions
PM dos Santos, MÂ Cirillo - Communications in Statistics …, 2023 - Taylor & Francis
A range of indicators, such as the average variance extracted (AVE), is commonly used to
validate constructs. In statistics, AVE is a measure of the amount of variance that is captured …
validate constructs. In statistics, AVE is a measure of the amount of variance that is captured …
Artificial intelligence (AI) in augmented reality (AR)-assisted manufacturing applications: a review
Augmented reality (AR) has proven to be an invaluable interactive medium to reduce
cognitive load by bridging the gap between the task-at-hand and relevant information by …
cognitive load by bridging the gap between the task-at-hand and relevant information by …
Revisiting time series outlier detection: Definitions and benchmarks
Time series outlier detection has been extensively studied with many advanced algorithms
proposed in the past decade. Despite these efforts, very few studies have investigated how …
proposed in the past decade. Despite these efforts, very few studies have investigated how …