Improved autoencoder for unsupervised anomaly detection
Sign Up to like & getrecommendations! Published in 2021 at "International Journal of Intelligent Systems"
DOI: 10.1002/int.22582
Abstract: Deep autoencoder‐based methods are the majority of deep anomaly detection. An autoencoder learning on training data is assumed to produce higher reconstruction error for the anomalous samples than the normal samples and thus can distinguish… read more here.
Keywords: detection; anomaly detection; improved autoencoder; unsupervised anomaly ... See more keywords
Graph‐based Bayesian network conditional normalizing flows for multiple time series anomaly detection
Sign Up to like & getrecommendations! Published in 2022 at "International Journal of Intelligent Systems"
DOI: 10.1002/int.23027
Abstract: Various devices and sensors of cyber‐physical systems interact with each other in time and space, and the generated multiple time series have implicit correlations and highly nonlinear relationships. Determining how to model the multiple time… read more here.
Keywords: time; time series; anomaly detection; multiple time ... See more keywords
Log‐based anomaly detection for distributed systems: State of the art, industry experience, and open issues
Sign Up to like & getrecommendations! Published in 2024 at "Journal of Software: Evolution and Process"
DOI: 10.1002/smr.2650
Abstract: Distributed systems have been widely used in many safety‐critical areas. Any abnormalities (e.g., service interruption or service quality degradation) could lead to application crashes or decrease user satisfaction. These things may cause serious economic losses.… read more here.
Keywords: anomaly detection; based anomaly; log based; distributed systems ... See more keywords
Anomaly detection by robust statistics
Sign Up to like & getrecommendations! Published in 2018 at "Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery"
DOI: 10.1002/widm.1236
Abstract: Real data often contain anomalous cases, also known as outliers. These may spoil the resulting analysis but they may also contain valuable information. In either case, the ability to detect such anomalies is essential. A… read more here.
Keywords: detection; robust statistics; anomaly detection; analysis ... See more keywords
Gryphon: a semi-supervised anomaly detection system based on one-class evolving spiking neural network
Sign Up to like & getrecommendations! Published in 2019 at "Neural Computing and Applications"
DOI: 10.1007/s00521-019-04363-x
Abstract: The backbone of the economy, security and sustainability of a state is inseparably linked to the security of its critical infrastructure. Critical infrastructures define goods, systems or subsystems that are essential to maintain the vital… read more here.
Keywords: detection system; system; semi supervised; anomaly detection ... See more keywords
Contextual anomaly detection on time series: a case study of metro ridership analysis
Sign Up to like & getrecommendations! Published in 2021 at "Neural Computing and Applications"
DOI: 10.1007/s00521-021-06455-z
Abstract: The increase in the amount of data collected in the transport domain can greatly benefit mobility studies and create high value-added mobility information for passengers, data analysts, and transport operators. This work concerns the detection… read more here.
Keywords: network; time series; anomaly detection; transport ... See more keywords
Data-driven heat pump management: combining machine learning with anomaly detection for residential hot water systems
Sign Up to like & getrecommendations! Published in 2025 at "Neural Computing and Applications"
DOI: 10.1007/s00521-025-11318-y
Abstract: Heat pumps (HPs) have emerged as a cost-effective and clean technology for sustainable energy systems, but their efficiency in producing hot water remains restricted by conventional threshold-based control methods. Although machine learning (ML) has been… read more here.
Keywords: usepackage; hot water; anomaly detection; machine ... See more keywords
Video anomaly detection in 10 years: a survey and outlook
Sign Up to like & getrecommendations! Published in 2024 at "Neural Computing and Applications"
DOI: 10.1007/s00521-025-11659-8
Abstract: Video anomaly detection (VAD) holds immense importance across diverse domains such as surveillance, healthcare, and environmental monitoring. While numerous surveys focus on conventional VAD methods, they often lack depth in exploring specific approaches and emerging… read more here.
Keywords: detection; video anomaly; survey; anomaly detection ... See more keywords
Anomaly detection in facial skin temperature using variational autoencoder
Sign Up to like & getrecommendations! Published in 2021 at "Artificial Life and Robotics"
DOI: 10.1007/s10015-020-00634-2
Abstract: Facial skin temperature is a physiological index that varies with skin blood flow controlled by autonomic nervous system activity. The facial skin temperature can be remotely measured using infrared thermography, and it has recently attracted… read more here.
Keywords: anomaly detection; skin temperature; temperature; facial skin ... See more keywords
Unsupervised Deep Anomaly Detection for Medical Images Using an Improved Adversarial Autoencoder
Sign Up to like & getrecommendations! Published in 2022 at "Journal of Digital Imaging"
DOI: 10.1007/s10278-021-00558-8
Abstract: Anomaly detection has been applied in the various disease of medical practice, such as breast cancer, retinal, lung lesion, and skin disease. However, in real-world anomaly detection, there exist a large number of healthy samples,… read more here.
Keywords: deep anomaly; anomaly detection; detection; adversarial autoencoder ... See more keywords
Revisiting streaming anomaly detection: benchmark and evaluation
Sign Up to like & getrecommendations! Published in 2024 at "Artificial Intelligence Review"
DOI: 10.1007/s10462-024-10995-w
Abstract: Anomaly detection in streaming data is an important task for many real-world applications, such as network security, fraud detection, and system monitoring. However, streaming data often exhibit concept drift, which means that the data distribution… read more here.
Keywords: detection; detection streaming; anomaly detection; streaming data ... See more keywords