Semi-supervised anomaly detection algorithms: A comparative summary and future research directions
ME Villa-Pérez, MA Alvarez-Carmona… - Knowledge-Based …, 2021 - Elsevier
While anomaly detection is relatively well-studied, it remains a topic of ongoing interest and
challenge, as our society becomes increasingly interconnected and digitalized. In this paper …
challenge, as our society becomes increasingly interconnected and digitalized. In this paper …
A real-time adaptive model for bearing fault classification and remaining useful life estimation using deep neural network
Rolling element bearings are essential components of a wide variety of industrial machinery
and the leading cause of equipment failure. The prediction of Remaining Useful Life (RUL) …
and the leading cause of equipment failure. The prediction of Remaining Useful Life (RUL) …
State of the art on quality control for data streams: A systematic literature review
These days, endless streams of data are generated by various sources such as sensors,
applications, users, etc. Due to possible issues in sources, such as malfunctions in sensors …
applications, users, etc. Due to possible issues in sources, such as malfunctions in sensors …
[HTML][HTML] Bayesian autoencoders with uncertainty quantification: Towards trustworthy anomaly detection
BX Yong, A Brintrup - Expert Systems with Applications, 2022 - Elsevier
Despite numerous studies of deep autoencoders (AEs) for unsupervised anomaly detection,
AEs still lack a way to express uncertainty in their predictions, crucial for ensuring safe and …
AEs still lack a way to express uncertainty in their predictions, crucial for ensuring safe and …
Driving behaviour analysis using machine and deep learning methods for continuous streams of vehicular data
In the last few decades, vehicles are equipped with a plethora of sensors which can provide
useful measurements and diagnostics for both the vehicle's condition as well as the driver's …
useful measurements and diagnostics for both the vehicle's condition as well as the driver's …
Machine learning based models for defect detection in composites inspected by Barker coded thermography: a qualitative analysis
Abstract Machine learning and artificial intelligence have evolved as enablers for
automation in various industrial applications. Barker-coded thermography is an active …
automation in various industrial applications. Barker-coded thermography is an active …
Modified autoencoder training and scoring for robust unsupervised anomaly detection in deep learning
N Merrill, A Eskandarian - IEEE Access, 2020 - ieeexplore.ieee.org
The autoencoder (AE) is a fundamental deep learning approach to anomaly detection. AEs
are trained on the assumption that abnormal inputs will produce higher reconstruction errors …
are trained on the assumption that abnormal inputs will produce higher reconstruction errors …
A survey of using machine learning in IoT security and the challenges faced by researchers
KM Harahsheh, CH Chen - Informatica, 2023 - digitalcommons.odu.edu
Abstract The Internet of Things (IoT) has become more popular in the last 15 years as it has
significantly improved and gained control in multiple fields. We are nowadays surrounded by …
significantly improved and gained control in multiple fields. We are nowadays surrounded by …
Spatio-temporal features based human action recognition using convolutional long short-term deep neural network
AFMS Saif, ED Wollega… - International Journal of …, 2023 - search.proquest.com
Recognition of human intention is crucial and challenging due to subtle motion patterns of a
series of action evolutions. Understanding of human actions is the foundation of many …
series of action evolutions. Understanding of human actions is the foundation of many …
Supervised-learning-based intelligent fault diagnosis for mechanical equipment
G Hong, D Suh - IEEE Access, 2021 - ieeexplore.ieee.org
Recently, anomaly detection for improving the productivity of machinery in industrial
environments has drawn considerable attention. As large-scale data collection and …
environments has drawn considerable attention. As large-scale data collection and …