AIMC Topic: Unsupervised Machine Learning

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Anomaly-based threat detection in smart health using machine learning.

BMC medical informatics and decision making Nov 19, 2024
BACKGROUND: Anomaly detection is crucial in healthcare data due to challenges associated with the integration of smart technologies and healthcare. Anomaly in electronic health record can be associated with an insider trying to access and manipulate ...
Unsupervised Machine Learning Computer Security Machine Learning Electronic Health Records Support Vector Machine Humans
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Smart data-driven medical decisions through collective and individual anomaly detection in healthcare time series.

International journal of medical informatics Nov 17, 2024
BACKGROUND: Anomalies in healthcare refer to deviation from the norm of unusual or unexpected patterns or activities related to patients, diseases or medical centres. Detecting these anomalies is crucial for timely interventions and efficient decisio...
United Kingdom Electronic Health Records Humans Clinical Decision-Making Delivery of Health Care Unsupervised Machine Learning
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Prognostic Significance and Associations of Neural Network-Derived Electrocardiographic Features.

Circulation. Cardiovascular quality and outcomes Nov 14, 2024
BACKGROUND: Subtle, prognostically important ECG features may not be apparent to physicians. In the course of supervised machine learning, thousands of ECG features are identified. These are not limited to conventional ECG parameters and morphology. ...
Predictive Value of Tests Heart Rate Humans Neural Networks, Computer Phenotype Prognosis Middle Aged Risk Assessment Time Factors United States Aged Female Electrocardiography Male Reproducibility of Results Unsupervised Machine Learning Cardiovascular Diseases Risk Factors
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Discovery of key molecular signatures for diagnosis and therapies of glioblastoma by combining supervised and unsupervised learning approaches.

Scientific reports Nov 11, 2024
Glioblastoma (GBM) is the most malignant brain cancer and one of the leading causes of cancer-related death globally. So, identifying potential molecular signatures and associated drug molecules are crucial for diagnosis and therapies of GBM. This st...
Unsupervised Machine Learning Glioblastoma Supervised Machine Learning Molecular Docking Simulation Gene Expression Profiling Transcriptome Gene Regulatory Networks Gene Expression Regulation, Neoplastic DNA Methylation Humans Biomarkers, Tumor Brain Neoplasms
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Tensorial multiview low-rank high-order graph learning for context-enhanced domain adaptation.

Neural networks : the official journal of the International Neural Network Society Nov 2, 2024
Unsupervised Domain Adaptation (UDA) is a machine learning technique that facilitates knowledge transfer from a labeled source domain to an unlabeled target domain, addressing distributional discrepancies between these domains. Existing UDA methods o...
Humans Neural Networks, Computer Machine Learning Unsupervised Machine Learning Algorithms Image Processing, Computer-Assisted Cluster Analysis
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Unsupervised learning for real-time and continuous gait phase detection.

PloS one Nov 1, 2024
Individuals with lower limb impairment after a stroke or spinal cord injury require rehabilitation, but traditional methods can be challenging for both patients and therapists. Robotic systems have been developed to help; however, they currently cann...
Male Algorithms Fuzzy Logic Humans Walking Female Unsupervised Machine Learning Gait Neural Networks, Computer Adult
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A general adaptive unsupervised feature selection with auto-weighting.

Neural networks : the official journal of the International Neural Network Society Oct 31, 2024
Feature selection (FS) is essential in machine learning and data mining as it makes handling high-dimensional data more efficient and reliable. More attention has been paid to unsupervised feature selection (UFS) due to the extra resources required t...
Algorithms Cluster Analysis Unsupervised Machine Learning Neural Networks, Computer Data Mining Humans Machine Learning
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Enhancing long-term water quality modeling by addressing base demand, demand patterns, and temperature uncertainty using unsupervised machine learning techniques.

Water research Oct 25, 2024
Water quality modelling in Water Distribution systems (WDS) is frequently affected by uncertainties in input variables such as base demand and decay constants. When utilizing simulation tools like EPANET, which necessitate exact numerical inputs, the...
Temperature Models, Theoretical Water Quality Uncertainty Unsupervised Machine Learning Chlorine
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A comparative analysis of unsupervised machine-learning methods in PSG-related phenotyping.

Journal of sleep research Oct 24, 2024
Obstructive sleep apnea is a heterogeneous sleep disorder with varying phenotypes. Several studies have already performed cluster analyses to discover various obstructive sleep apnea phenotypic clusters. However, the selection of the clustering metho...
Female Adult Male Humans Phenotype Unsupervised Machine Learning Sleep Apnea, Obstructive Polysomnography Middle Aged Fuzzy Logic Cluster Analysis
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Structure preservation constraints for unsupervised domain adaptation intracranial vessel segmentation.

Medical & biological engineering & computing Oct 21, 2024
Unsupervised domain adaptation (UDA) has received interest as a means to alleviate the burden of data annotation. Nevertheless, existing UDA segmentation methods exhibit performance degradation in fine intracranial vessel segmentation tasks due to th...
Neural Networks, Computer Computed Tomography Angiography Algorithms Brain Image Processing, Computer-Assisted Unsupervised Machine Learning Magnetic Resonance Angiography Humans
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