Results 41 to 50 of about 225,629 (280)
Log-based Anomaly Detection of CPS Using a Statistical Method
, 2017 Detecting anomalies of a cyber physical system (CPS), which is a complex system consisting of both physical and software parts, is important because a CPS often operates autonomously in an unpredictable environment.
Choi, Eun-Hye +3 more
core +1 more source
Probabilistic Anomaly Detection in Natural Gas Time Series Data [PDF]
, 2016 This paper introduces a probabilistic approach to anomaly detection, specifically in natural gas time series data. In the natural gas field, there are various types of anomalies, each of which is induced by a range of causes and sources.
Akouemo Kengmo Kenfack, Hermine Nathalie +1 more
core +2 more sources
Detection of Outliers in Univariate Circular Data by Means of the Outlier Local Factor (LOF) [PDF]
Statistics in Transition New Series, 2020 Abstract The problem of outlier detection in univariate circular data was the object of increased interest over the last decade. New numerical and graphical methods were developed for samples from different circular probability distributions.
openaire +2 more sources
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
core +1 more source
Molecular Oncology, EarlyView.Nanosecond infrared laser (NIRL) low‐volume sampling combined with shotgun lipidomics uncovers distinct lipidome alterations in oropharyngeal squamous cell carcinoma (OPSCC) of the palatine tonsil. Several lipid species consistently differentiate tumor from healthy tissue, highlighting their potential as diagnostic markers.
Leonard Kerkhoff +11 more
wiley +1 more source
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
doaj +1 more source
Unsupervised Outlier Detection Mechanism for Tea Traceability Data
IEEE Access, 2022 The presence of outliers in tea traceability data can mislead customers and have a significant impact on the reputation and profits of tea companies. To solve this problem, an unsupervised outlier detection mechanism for tea traceability data is proposed.
Honggang Yang +4 more
doaj +1 more source
Improving Influenced Outlierness(INFLO) Outlier Detection Method [PDF]
, 2013 Anomaly detection refers to the process of finding outlying records from a given dataset.This process is a subject of increasing interest among analysts. Anomaly detection is a subject of interest in various knowledge domains.
Suman, Shashwat
core
Supervised detection of anomalous light-curves in massive astronomical catalogs
, 2014 The development of synoptic sky surveys has led to a massive amount of data for which resources needed for analysis are beyond human capabilities. To process this information and to extract all possible knowledge, machine learning techniques become ...
Kim, Dae-Won +3 more
core +1 more source
Subtype‐specific enhancer RNAs define transcriptional regulators and prognosis in breast cancers
Molecular Oncology, EarlyView.This study employed machine learning methodologies to perform the subtype‐specific classification of RNA‐seq data sets, which are mapped on enhancers from TCGA‐derived breast cancer patients. Their integration with gene expression (referred to as ProxCReAM eRNAs) and chromatin accessibility profiles has the potential to identify lineage‐specific and ...
Aamena Y. Patel +6 more
wiley +1 more source