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Detecting Unusual Intravenous Infusion Alerting Patterns with Machine Learning Algorithms
2022
Biomedical Instrumentation & Technology
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Discussion These findings show that ML-based algorithms are more robust than control charts in detecting unusual alerting patterns. ...
Three different unsupervised multivariate ML-based anomaly detection algorithms (Local Outlier Factor, Isolation Forest, and k-Nearest Neighbors) were used for the same purpose. ...
Acknowledgments The authors thank all REMEDI clinicians involved in the study, especially Andrew Lodolo and Naomi Barasch, for their invaluable insights and expertise. ...
doi:10.2345/1943-5967-56.2.58
pmid:35749264
pmcid:PMC9767430
fatcat:c2mj3d7hzffmvfvg774d2pw7yy
Autoencoder-Based Visual Anomaly Localization for Manufacturing Quality Control
[article]
2023
arXiv
pre-print
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2023 pre-print arXiv:2309.06884v1 fatcat:o5wxmln6wzheflmxi757kwrera
In the context of Industry 4.0, visual anomaly detection poses an optimistic solution for automatically controlled product quality with high precision. ...
Incorporating artificial defects into the training data shows significant potential for practical implementation in real-world quality control scenarios. ...
quality control standards within the manufacturing industry. ...
arXiv:2309.06884v2
fatcat:2net3botwzeethdpzrwd4xuzdm
Identifying High-Risk Patients without Labeled Training Data: Anomaly Detection Methodologies to Predict Adverse Outcomes
2010
AMIA Annual Symposium Proceedings
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In this paper, we explore different anomaly detection approaches to identify high-risk patients as cases that lie in sparse regions of the feature space. ...
We study three broad categories of anomaly detection methods: classification-based, nearest neighbor-based, and clustering-based techniques. ...
Figure 1 presents the AUROC for the different morbidity endpoints and mortality in the NSQIP data for each of the unsupervised anomaly detection approaches. ...
pmid:21347083
pmcid:PMC3041411
fatcat:shgyincopzfybfntc43wpqpydm
Anomaly Detection with Unknown Anomalies: Application to Maritime Machinery
2021
IFAC-PapersOnLine
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The proposed approach is comprised of two phases: unsupervised discovery of anomalies, and semi-supervised construction and tuning of the anomaly detection algorithm. ...
The framework is suited for problems in which individual machine learning paradigms cannot be directly implemented: supervised learning is not applicable due to the lack of labelled data, unsupervised ...
ACKNOWLEDGEMENTS This work was funded by the Research Council of Norway via the MAROFF-2 project number 296465 and the data provided by Brunvoll AS. ...
doi:10.1016/j.ifacol.2021.10.080
fatcat:blzcwm5pnjejpcveznqyyrejdu
Learning Cortical Anomaly through Masked Encoding for Unsupervised Heterogeneity Mapping
[article]
2024
arXiv
pre-print
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This paper introduces CAM (Cortical Anomaly Detection through Masked Image Modeling), a novel self-supervised framework designed for the unsupervised detection of complex brain disorders using cortical ...
Altogether, we demonstrate a scalable approach for anomaly detection of complex brain disorders based on cortical abnormalities. The code will be made available at https://github.com/chadHGY/CAM. ...
Iterative Masked Anomaly Detection After pre-training the encoder f θ , we applied it for unsupervised anomaly detection on new data samples X. ...
arXiv:2312.02762v3
fatcat:eetsto442fefffmw5llbmltkfa
Anomaly segmentation model for defects detection in electroluminescence images of heterojunction solar cells
[article]
2022
arXiv
pre-print
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The core of the model is an anomaly detection algorithm based on Mahalanobis distance that can be trained in a semi-supervised manner on imbalanced data with small number of digital electroluminescence ...
This paper presents a deep-learning-based automatic detection model SeMaCNN for classification and semantic segmentation of electroluminescent images for solar cell quality evaluation and anomalies detection ...
Acknowledgments The authors thank the Sber ESG Direction and Sber CIB (Key Clients department, Power and Utilities Coverage) for providing expert consulting in the domain of research. ...
arXiv:2208.05994v3
fatcat:s5gvgyfnzraxfgokvdelc47iaq
Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges
[article]
2017
arXiv
pre-print
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detection, Internet traffic classification, and quality of service optimization. ...
Recently there has been a rising trend of employing unsupervised machine learning using unstructured raw network data to improve network performance and provide services such as traffic engineering, anomaly ...
in detecting anomalies. ...
arXiv:1709.06599v1
fatcat:llcg6gxgpjahha6bkhsitglrsm
Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges
2019
IEEE Access
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anomaly detection, Internet traffic classification, and quality of service optimization. ...
In addition, unsupervised learning can unconstrain us from the need for labeled data and manual handcrafted feature engineering, thereby facilitating flexible, general, and automated methods of machine ...
in detecting anomalies. ...
doi:10.1109/access.2019.2916648
fatcat:xutxh3neynh4bgcsmugxsclkna
Autoencoder-Based Visual Anomaly Localization for Manufacturing Quality Control
2023
Machine Learning and Knowledge Extraction
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In the context of Industry 4.0, visual anomaly detection poses an optimistic solution for automatically controlled product quality with high precision. ...
Incorporating artificial defects into the training data shows significant potential for practical implementation in real-world quality control scenarios. ...
quality control standards within the manufacturing industry. ...
doi:10.3390/make6010001
fatcat:zbvljlmdrrfmdibobecm52r2yu
Machine Learning for Anomaly Detection: A Systematic Review
2021
IEEE Access
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We are also grateful to our research assistants who helped in collecting, summarizing, and analyzing the research articles for this SLR study. ...
A284 "Optimal virtual machine selection for anomaly detection using a swarm intelligence approach" Jour. [307]
A285 "Anomaly Detection in Power Quality Measurements Using Proximity-Based Unsupervised ...
Jour. [310]
A288 "Anomaly detection based on machine learning in IoT-based vertical plant wall for indoor climate control. " Jour. [311]
A289 "Anomaly detection in electronic invoice systems based ...
doi:10.1109/access.2021.3083060
fatcat:vv7qthbvqjdz7ksm3yosulk22q
Revisit network anomaly ranking in datacenter network using re-ranking
2015
2015 IEEE 4th International Conference on Cloud Networking (CloudNet)
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In this situation, system monitoring and intrusion detection become essential to control the risks of such networks. ...
Our experimental results based on real datacenter network data demonstrate that the proposed reranking model improves the ranking quality over the unsupervised method, especially for insignificant outliers ...
The training data is generated form the output of unsupervised anomaly detection system on old traffic capturing. Then we detect new outliers based on this model. ...
doi:10.1109/cloudnet.2015.7335302
dblp:conf/cloudnet/HuangoFWYLQ15
fatcat:hupubrzqrnbpxf5csxmmh6rhyi
Detecting abnormal electricity usage using unsupervised learning model in unlabeled data
2021
International Journal of Advanced and Applied Sciences
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In this paper, abnormalities are detected in electricity usage using unsupervised learning and evaluated using Excess Mass. ...
The unsupervised anomaly detection model is based on Gaussian Mixture Model (GMM) and Isolation Forest (iForest). ...
Moreover, it was reported in Malaysia and in different places in the world; during the Movement Control Order (MCO) and Conditional Movement Control Order (CMCO) period in COVID-19, electricity consumption ...
doi:10.21833/ijaas.2021.09.014
fatcat:gimmtcnmpjaojgqrvdjhqqpire
SMILE: Smart Monitoring IoT Learning Ecosystem
2020
International Journal on Advanced Science, Engineering and Information Technology
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The classic cause-effect association process in the field of industrial monitoring requires thorough understanding of the monitored ecosystem and the main characteristics triggering the detected anomalies ...
In industrial contexts to date, there are several solutions to monitor and intervene in case of anomalies and/or failures. ...
; • detection and prediction of anomalies and/or failures in data centers/cloud environments. ...
doi:10.18517/ijaseit.10.1.11144
fatcat:fgsr5p7ky5ecpdjh4pn4mheucy
A Machine Learning-based Approach for Anomaly Detection in IoT Systems
2020
Turkish Journal of Computer and Mathematics Education
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The increased use of IoT devices has created new hurdles in the detection of anomalies. ...
However, there are drawbacks to these approaches, such as data quality difficulties, the necessity for real-time analysis, and the possibility of false positives and false negatives. ...
SWaT dataset: This dataset contains operational data from a water treatment plant and is intended for use in industrial control systems to detect anomalies. ...
doi:10.17762/turcomat.v11i3.13607
fatcat:7q6gygmo2vc5zcsaaeys434dum
Unsupervised 3D Brain Anomaly Detection
[article]
2020
arXiv
pre-print
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Anomaly detection (AD) is the identification of data samples that do not fit a learned data distribution. ...
To the best of our knowledge, this study exemplifies the first AD approach that can efficiently handle volumetric data and detect 3D brain anomalies in one single model. ...
AD for quality control. Given that an anomaly is defined as any type of data unrepresented by the normal data distribution, we can extend our AD model to detect any kind of outlier sample. ...
arXiv:2010.04717v1
fatcat:mi7nin7i5jgxrjdcflrwx6ecay
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