Robust and explainable autoencoders for unsupervised time series outlier detection
Time series data occurs widely, and outlier detection is a fundamental problem in data
mining, which has numerous applications. Existing autoencoder-based approaches deliver …
mining, which has numerous applications. Existing autoencoder-based approaches deliver …
A Systematic Review of Anomaly Detection within High Dimensional and Multivariate Data
In data analysis, recognizing unusual patterns (outliers’ analysis or anomaly detection)
plays a crucial role in identifying critical events. Because of its widespread use in many …
plays a crucial role in identifying critical events. Because of its widespread use in many …
A survey on explainable anomaly detection for industrial internet of things
Anomaly detection techniques in the Industrial Internet of Things (IIoT) are driving traditional
industries towards an unprecedented level of efficiency, productivity and performance. They …
industries towards an unprecedented level of efficiency, productivity and performance. They …
Deep crowd anomaly detection: state-of-the-art, challenges, and future research directions
Crowd anomaly detection is one of the most popular topics in computer vision in the context
of smart cities. A plethora of deep learning methods have been proposed that generally …
of smart cities. A plethora of deep learning methods have been proposed that generally …
I will survive: An event-driven conformance checking approach over process streams
Online conformance checking deals with finding discrepancies between real-life and
modeled behavior on data streams. The current state-of-the-art output of online conformance …
modeled behavior on data streams. The current state-of-the-art output of online conformance …
[HTML][HTML] Coalitional Bayesian autoencoders: Towards explainable unsupervised deep learning with applications to condition monitoring under covariate shift
BX Yong, A Brintrup - Applied Soft Computing, 2022 - Elsevier
This paper aims to improve the explainability of autoencoder (AE) predictions by proposing
two novel explanation methods based on the mean and epistemic uncertainty of log …
two novel explanation methods based on the mean and epistemic uncertainty of log …
Optimal Transport for Efficient, Unsupervised Anomaly Detection on Industrial Data
A Langbridge, F O'Donncha… - … Conference on Big …, 2024 - ieeexplore.ieee.org
Effective anomaly detection frameworks are a central pillar of the Industry 4.0 paradigm. In
this paper, we introduce an Optimal Transport (OT)-based framework for anomaly detection …
this paper, we introduce an Optimal Transport (OT)-based framework for anomaly detection …
Large Language Models can Deliver Accurate and Interpretable Time Series Anomaly Detection
Time series anomaly detection (TSAD) plays a crucial role in various industries by
identifying atypical patterns that deviate from standard trends, thereby maintaining system …
identifying atypical patterns that deviate from standard trends, thereby maintaining system …
Towards explainable machine learning for prediction of disease progression
S Berendse, J Krabbe, J Klaus… - Applied Artificial …, 2024 - Taylor & Francis
This research focuses on addressing the challenges surrounding interpretability of machine
learning techniques in the field of prediction of disease progression. This paper summarizes …
learning techniques in the field of prediction of disease progression. This paper summarizes …
Predictive Analysis as an Efficiency Enabler for Network and Service Management
R Rocha, G Rato, R Ferreira, N Gomes… - … IEEE Conference on …, 2024 - ieeexplore.ieee.org
This article presents a novel Anomaly Detection (AD) feature for network and service
management, emphasizing ease of understanding, speed, versatility, and adaptability to …
management, emphasizing ease of understanding, speed, versatility, and adaptability to …