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   Articles with "unsupervised machine" as a keyword



Exploring materials band structure space with unsupervised machine learning

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recommendations! Published in 2019 at "Computational Materials Science"

DOI: 10.1016/j.commatsci.2018.11.002

Abstract: Abstract An unsupervised machine learning algorithm is applied for the first time to explore the space of materials electronic band structures. T-student stochastic neighbor embedding (t-SNE), a state of the art algorithm for visualization of… read more here.

Keywords: space; structure space; unsupervised machine; machine learning ... See more keywords
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Channel head extraction based on fuzzy unsupervised machine learning method

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recommendations! Published in 2021 at "Geomorphology"

DOI: 10.1016/j.geomorph.2021.107888

Abstract: Abstract Channel head extraction is fundamental to understand the catchment hydrological processes, catchment origin, runoff generation and landscape evolution. A spatially constant threshold value such as upslope area, slope-area, and curvature have been widely used… read more here.

Keywords: fuzzy unsupervised; method; channel head; head extraction ... See more keywords

UNSUPERVISED MACHINE LEARNING CLUSTERING FOR STRATIFICATION OF CARDIAC RISK IN PATIENTS WITH EXERCISE ECHOCARDIOGRAPHY NEGATIVE FOR ISCHEMIA

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recommendations! Published in 2019 at "Journal of the American College of Cardiology"

DOI: 10.1016/s0735-1097(19)30718-1

Abstract: Exercise echocardiography (ESE) negative for ischemia is associated with an overall low risk of adverse events, but significant prognostic heterogeneity may exist within the referred population of patients. Unsupervised clustering can uncover latent clinical phenotypes… read more here.

Keywords: exercise echocardiography; negative ischemia; risk; unsupervised machine ... See more keywords

Unsupervised Machine Learning for Differential Analysis in Proteomics.

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recommendations! Published in 2025 at "Analytical chemistry"

DOI: 10.1021/acs.analchem.5c03117

Abstract: Differential analysis in proteomics is pivotal for biomarker discovery and disease mechanism elucidation, yet traditional statistical methods are constrained by distributional assumptions and empirical fold change threshold dependencies. This study systematically evaluates 18 unsupervised anomaly… read more here.

Keywords: analysis; differential analysis; machine learning; analysis proteomics ... See more keywords

A robust unsupervised machine-learning method to quantify the morphological heterogeneity of cells and nuclei.

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recommendations! Published in 2021 at "Nature protocols"

DOI: 10.1038/s41596-020-00432-x

Abstract: Cell morphology encodes essential information on many underlying biological processes. It is commonly used by clinicians and researchers in the study, diagnosis, prognosis, and treatment of human diseases. Quantification of cell morphology has seen tremendous… read more here.

Keywords: machine learning; robust unsupervised; unsupervised machine; morphological heterogeneity ... See more keywords

Unsupervised machine-learning classification of electrophysiologically active electrodes during human cognitive task performance

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recommendations! Published in 2019 at "Scientific Reports"

DOI: 10.1038/s41598-019-53925-5

Abstract: Identification of active electrodes that record task-relevant neurophysiological activity is needed for clinical and industrial applications as well as for investigating brain functions. We developed an unsupervised, fully automated approach to classify active electrodes showing… read more here.

Keywords: classification; machine learning; task; active electrodes ... See more keywords

Unraveling the origin of reductive stability of super-concentrated electrolytes from first principles and unsupervised machine learning

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recommendations! Published in 2022 at "Chemical Science"

DOI: 10.1039/d2sc04025e

Abstract: Developing electrolytes with excellent electrochemical stability is critical for next-generation rechargeable batteries. Super-concentrated electrolytes (SCEs) have attracted great interest due to their high electrochemical performances and stability. Previous studies have revealed changes in solvation structures… read more here.

Keywords: super concentrated; unsupervised machine; concentrated electrolytes; machine learning ... See more keywords

Visualizing nanoscale heterogeneity in perylene thin films via tip-enhanced photoluminescence with unsupervised machine learning.

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recommendations! Published in 2024 at "Chemical communications"

DOI: 10.1039/d4cc01808g

Abstract: We investigate the properties of ultrathin 3,4,9,10-perylenetetracarboxylic diimide (PTCDI) films using a combination of tip-enhanced photoluminescence and unsupervised machine learning. We expose nanoscale spectral heterogeneities that can be understood on the basis of the interplay… read more here.

Keywords: photoluminescence unsupervised; machine learning; unsupervised machine; tip enhanced ... See more keywords

Empirically derived symptom profiles in adults with attention-Deficit/hyperactivity disorder: An unsupervised machine learning approach.

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recommendations! Published in 2024 at "Applied neuropsychology. Adult"

DOI: 10.1080/23279095.2024.2343022

Abstract: Abstract Background Attention-deficit/hyperactivity disorder (ADHD) is associated with various cognitive, behavioral, and mood symptoms that complicate diagnosis and treatment. The heterogeneity of these symptoms may also vary depending on certain sociodemographic factors. It is therefore… read more here.

Keywords: symptom profiles; symptom; machine learning; unsupervised machine ... See more keywords

Identifying Outliers in Astronomical Images with Unsupervised Machine Learning

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recommendations! Published in 2022 at "Research in Astronomy and Astrophysics"

DOI: 10.1088/1674-4527/ac7386

Abstract: Astronomical outliers, such as unusual, rare or unknown types of astronomical objects or phenomena, constantly lead to the discovery of genuinely unforeseen knowledge in astronomy. More unpredictable outliers will be uncovered in principle with the… read more here.

Keywords: unsupervised machine; machine learning; attcae knn; cae knn ... See more keywords

Anomaly detection for atomic clocks using unsupervised machine learning algorithms

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recommendations! Published in 2024 at "Metrologia"

DOI: 10.1088/1681-7575/ad6b30

Abstract: Atomic clocks are the most accurate timekeeping instruments in the world and play a crucial role in numerous applications, notably in satellite navigation. Nonetheless, the accuracy of these clock signals is subject to anomalies, highlighting… read more here.

Keywords: detection; anomaly detection; machine learning; atomic clocks ... See more keywords