Results 31 to 40 of about 128,875 (283)
An Incremental Local Outlier Detection Method in the Data Stream
Applied Sciences, 2018 Outlier detection has attracted a wide range of attention for its broad applications, such as fault diagnosis and intrusion detection, among which the outlier analysis in data streams with high uncertainty and infinity is more challenging.
Haiqing Yao +3 more
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Hybrid Machine Learning–Statistical Method for Anomaly Detection in Flight Data
Applied Sciences, 2022 This paper investigates the use of an unsupervised hybrid statistical–local outlier factor algorithm to detect anomalies in time-series flight data. Flight data analysis is an activity carried out by airlines primarily as a means of improving the safety ...
Sameer Kumar Jasra +3 more
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TADILOF: Time Aware Density-Based Incremental Local Outlier Detection in Data Streams
Sensors, 2020 Outlier detection in data streams is crucial to successful data mining. However, this task is made increasingly difficult by the enormous growth in the quantity of data generated by the expansion of Internet of Things (IoT).
Jen-Wei Huang +2 more
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Scientific Reports, 2022 White matter lesions (WML) underlie multiple brain disorders, and automatic WML segmentation is crucial to evaluate the natural disease course and effectiveness of clinical interventions, including drug discovery.
Kokhaur Ong +9 more
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Abrupt user load change detection based on multiple features and LOF algorithm
Zhejiang dianli, 2023 The sudden load changes impact power grids by frequency and power oscillations. In order to distinguish the complex and massive abnormal user load data， this paper proposes a method combining multiple features and LOF （local outlier factor） algorithm ...
ZENG Jing +4 more
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A subsampling method for the computation of multivariate estimators with high breakdown point [PDF]
, 1994 All known robust location and scale estimators with high breakdown point for multivariate sample's are very expensive to compute. In practice, this computation has to be carried out using an approximate subsampling procedure.
Juan, Jesús, Prieto, Francisco J.
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Applied Computational Intelligence and Soft Computing, 2022 Data clustering algorithms experience challenges in identifying data points that are either noise or outlier. Hence, this paper proposes an enhanced connectivity measure based on the outlier detection approach for multi-objective data clustering problems.
Hossam M. J. Mustafa, Masri Ayob
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A geometrical analysis of global stability in trained feedback networks [PDF]
, 2019 Recurrent neural networks have been extensively studied in the context of neuroscience and machine learning due to their ability to implement complex computations. While substantial progress in designing effective learning algorithms has been achieved in
Mastrogiuseppe, Francesca +1 more
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Robust regression for large-scale neuroimaging studies [PDF]
, 2015 Multi-subject datasets used in neuroimaging group studies have a complex structure, as they exhibit non-stationary statistical properties across regions and display various artifacts.
, +21 more
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Model-free detection of unique events in time series
Scientific Reports, 2022 Recognition of anomalous events is a challenging but critical task in many scientific and industrial fields, especially when the properties of anomalies are unknown.
Zsigmond Benkő +2 more
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