Results 41 to 50 of about 161,068 (368)
发电技术, 2021 Anomaly detection of gas turbine hot components can ensure its operational safety and reliability. With the boom of artificial intelligence, data-driven fault diagnosis is becoming increasingly popular.
Mingliang BAI +4 more
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Integrating State-of-the-Art Approaches for Anomaly Detection and Localization in the Continual Learning Setting [PDF]
, 2023 openThe significant attention surrounding the application of anomaly detection (AD) in identifying defects within industrial environments using only normal samples has prompted research and development in this area.
BUGARIC, JOVANA
core
CoRR, 2019 Visual defect assessment is a form of anomaly detection. This is very relevant in finding faults such as cracks and markings in various surface inspection tasks like pavement and automotive parts. The task involves detection of deviation/divergence of anomalous samples from the normal ones.
Manpreet Singh Minhas, John S. Zelek
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An Adaptive Policy-Based Anomaly Object Control System for Enhanced Cybersecurity
IEEE AccessAnomaly detection research focuses on identifying rare patterns derived from daily occurrences. This study introduces an innovative anomaly–object control system that utilizes adaptive policies through anomaly detection algorithms.
Won Sakong, Wooju Kim
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Model selection for anomaly detection [PDF]
SPIE Proceedings, 2015 6 pages, 3 figures, Eighth International Conference on Machine Vision (December 8, 2015)
Evgeny Burnaev +2 more
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Anomaly detection and community detection in networks
Journal of Big Data, 2022 AbstractAnomaly detection is a relevant problem in the area of data analysis. In networked systems, where individual entities interact in pairs, anomalies are observed when pattern of interactions deviates from patterns considered regular. Properly defining what regular patterns entail relies on developing expressive models for describing the observed ...
Hadiseh Safdari, Caterina De Bacco
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Machine Learning for Anomaly Detection: A Systematic Review
IEEE Access, 2021 Anomaly detection has been used for decades to identify and extract anomalous components from data. Many techniques have been used to detect anomalies. One of the increasingly significant techniques is Machine Learning (ML), which plays an important role
Ali Bou Nassif +3 more
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CoRR, 2023 Anomaly detection is critical for finding suspicious behavior in innumerable systems. We need to detect anomalies in real-time, i.e. determine if an incoming entity is anomalous or not, as soon as we receive it, to minimize the effects of malicious activities and start recovery as soon as possible. Therefore, online algorithms that can detect anomalies
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A Survey on Explainable Anomaly Detection
ACM Transactions on Knowledge Discovery from Data, 2023 In the past two decades, most research on anomaly detection has focused on improving the accuracy of the detection, while largely ignoring the explainability of the corresponding methods and thus leaving the explanation of outcomes to practitioners. As anomaly detection algorithms are increasingly used in safety-critical domains, providing explanations
Zhong Li 0002 +2 more
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Anomaly-Detection in Diabetes using SVM [PDF]
, 2018 It's is an predominant anomaly detection technique , which is compared with many anomaly detection ...
Dr Jabez Jones (5190476)
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