A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Filters
Unsupervised anomaly detection for discrete sequence healthcare data
[article]
2020
arXiv
pre-print
Preserved Fulltext
Other Versions
The models provide state-of-the-art results for unsupervised anomaly detection for fraud detection in healthcare. Our EDF approach further improves the quality of LSTM model. ...
We use real data on sequences of patients' visits data from Allianz company for the validation. ...
Acknowledgments We thank Martin Spindler for providing the data and Ivan Fursov for providing code for data processing. ...
arXiv:2007.10098v2
fatcat:zfstsmb5yjdjlhqwdcmasowaoq
Anomaly detection techniques for streaming data–An overview
2020
Malaya Journal of Matematik
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Detecting anomaly in right time facilitates the appropriate control actions for the anomaly in right time. There are several techniques for detecting anomaly. ...
In this paper, an overview of different techniques for detection of anomaly is presented. ...
Online eSNN Unsupervised Anomaly Detection (Oe SNN-UAD) OeSNN-UAD is mainly used in univariate time series data for online unsupervised anomaly detection [29] . ...
doi:10.26637/mjm0s20/0133
fatcat:blyjw2z4q5datacu7y4lavwchq
DeepFIB: Self-Imputation for Time Series Anomaly Detection
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Time series (TS) anomaly detection (AD) plays an essential role in various applications, e.g., fraud detection in finance and healthcare monitoring. ...
Due to the inherently unpredictable and highly varied nature of anomalies and the lack of anomaly labels in historical data, the AD problem is typically formulated as an unsupervised learning problem. ...
A deep neural network for unsupervised anomaly detection and diagnosis in multivariate
time series data. AAAI, abs/1811.08055, 2019.
R. Zhang, P. Isola, and A.A. Efros. ...
arXiv:2112.06247v1
fatcat:k7gmfcsnajafdp5qgwq4iuq4f4
Anomalous Example Detection in Deep Learning: A Survey
2020
IEEE Access
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
This survey tries to provide a structured and comprehensive overview of the research on anomaly detection for DL based applications. ...
To make DL more robust, several posthoc (or runtime) anomaly detection techniques to detect (and discard) these anomalous samples have been proposed in the recent past. ...
The metric data anomaly detection techniques consider the use of metrics like distance, correlation, and distribution. The evolving data include discrete sequences and time series. ...
doi:10.1109/access.2020.3010274
fatcat:3xjpfc64nvcbtfpwtwwbitjuvm
Anomalous Example Detection in Deep Learning: A Survey
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Other Versions
This survey tries to provide a structured and comprehensive overview of the research on anomaly detection for DL based applications. ...
To make DL more robust, several posthoc (or runtime) anomaly detection techniques to detect (and discard) these anomalous samples have been proposed in the recent past. ...
The metric data anomaly detection techniques consider the use of metrics like distance, correlation, and distribution. The evolving data include discrete sequences and time series. ...
arXiv:2003.06979v2
fatcat:4mogo75b4rbxrc6vph2xmllkue
Anomaly Detection
[chapter]
2016
Encyclopedia of Machine Learning and Data Mining
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Identifying such anomalies from observed data, or the task of anomaly detection, is an important and often critical analysis task. ...
for one domain would perform in another. ...
ACM Comput Surv 41(3):15:1-15:58 Chandola V, Banerjee A, Kumar V (2012) Anomaly detection for discrete sequences: a survey. ...
doi:10.1007/978-1-4899-7502-7_912-1
fatcat:mnxpfd3fg5et5htdqrmzrmrkhu
Big data analytics for preventive medicine
2019
Neural computing & applications (Print)
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
We summarize state-of-the-art data analytics algorithms used for classification of disease, clustering (unusually high incidence of a particular disease), anomalies detection (detection of disease) and ...
How can healthcare providers use modern data analytics tools and technologies to analyze and create value from complex data? ...
Unsupervised is the commonly used method for anomalies detection and it does not require data labeling. ...
doi:10.1007/s00521-019-04095-y
pmid:32205918
pmcid:PMC7088441
fatcat:x52upnuwbjdchkyb7hog5pvawm
Machine learning methods for detecting urinary tract infection and analysing daily living activities in people with dementia
2019
PLoS ONE
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
We have designed an algorithm to detect Urinary Tract Infections (UTI) which is one of the top five reasons of hospital admissions for people with dementia (around 9% of hospital admissions for people ...
In addition, we have designed an algorithm for detecting changes in activity patterns to identify early symptoms of cognitive decline or health decline in order to provide personalised and preventative ...
The data collected in the TIHM project will be available upon request and subject to ethics approval. All data requests to be sent to the sponsor's research department (research@sabp. nhs.uk). ...
doi:10.1371/journal.pone.0209909
pmid:30645599
pmcid:PMC6333356
fatcat:ymbof2altzf55cjunllyiafl6e
Anomaly Detection, Analysis and Prediction Techniques in IoT Environment: A Systematic Literature Review
2019
IEEE Access
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
anomaly detection, prediction, and analysis. ...
In this paper, we present the results of a systematic literature review about anomaly detection techniques except for these dominant research areas. ...
One is designed over the discrete sequences while the other is designed for continuous time series of data. ...
doi:10.1109/access.2019.2921912
fatcat:k7pmdn6ruzevrpyibo7dmqh3ee
A Sequence Mining-Based Novel Architecture for Detecting Fraudulent Transactions in Healthcare Systems
2022
IEEE Access
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
This identifies anomalies as both sequences would not be compliant with the rule engine's sequences. ...
This paper presents a novel process-based fraud detection methodology to detect insurance claim-related frauds in the healthcare system using sequence mining concepts. ...
Most of the researches related to anomaly detection in healthcare have considered the clinical processes for a particular disease and utilized prior knowledge and applied the unsupervised models [16] ...
doi:10.1109/access.2022.3170888
fatcat:tf6tm4o4znf6zfsprhup5cn2ci
Anomaly Detection for Symbolic Time Series Representations of Reduced Dimensionality
2020
Zenodo
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
From industrial to healthcare machines and wearable sensors, an unprecedented amount of data is becoming available for mining and information retrieval. ...
In this paper, we propose a computationally efficient, yet highly accurate, framework for anomaly detection of streaming data in lower-dimensional spaces, utilizing a modification of the symbolic aggregate ...
To address these issues, this work proposes an unsupervised, non-parametric method, characterized by low power and memory demands, for anomaly detection in unidimensional data, whereas guidelines for a ...
doi:10.5281/zenodo.4294535
fatcat:xgeuh4zx7fe5rnwpwjxjoyig24
A Unified Framework for Behaviour Monitoring and Abnormality Detection for Smart Home
2019
Wireless Communications and Mobile Computing
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
manually labelled classes along with the implementation of Local Outlier Factor method for detection of an abnormal pattern of the inhabitant of smart homes. ...
The resultant frequent device utilization patterns with anomaly score more than the threshold value, reflecting abnormal activity patterns, are found more in evening time data in comparison to other time ...
Data Discretization. Data discretization is data preprocessing technique [42] . ...
doi:10.1155/2019/1734615
fatcat:eg3wt5r5ireaxohssoejaubln4
Generating Artificial Sensor Data for the Comparison of Unsupervised Machine Learning Methods
2021
Sensors
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Neural Net in anomaly detection. ...
The data will pave the way for a comparison of selected machine learning methods in the exemplary field of unsupervised learning. ...
Data Availability Statement: The data presented in this study are openly available in https://www. hsu-hh.de/imb/en/projects/ArtDataGen. ...
doi:10.3390/s21072397
pmid:33808459
fatcat:jeqndieva5arpco4hc43xeifmi
Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges
2013
Sensors
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
In particular, the paper outlines the more common data mining tasks that have been applied such as anomaly detection, prediction and decision making when considering in particular continuous time series ...
This paper provides a recent review of the latest methods and algorithms used to analyze data from wearable sensors used for physiological monitoring of vital signs in healthcare services. ...
Acknowledgements The authors of this work are partially supported by SAAPHO (Secure Active Aging: Participation and Health for the Old). ...
doi:10.3390/s131217472
pmid:24351646
pmcid:PMC3892855
fatcat:fy4hhounsrgffno2fclhanqt7m
Application of Data Mining Techniques to Healthcare Data
2004
Infection control and hospital epidemiology
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
AbstractA high-level introduction to data mining as it relates to surveillance of healthcare data is presented. ...
A concrete example illustrates steps involved in the data mining process, and three successful data mining applications in the healthcare arena are described. ...
Decision trees build classification rules and other mechanisms for detecting anomalies. ...
doi:10.1086/502460
pmid:15357163
fatcat:nyhq5bn74nchjc5tlvbvb4cuoq
« Previous
Showing results 1 — 15 out of 831 results