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15801 |
The interpretable machine learning model for depression associated with heavy metals via EMR mining method
Enthalten in Scientific reports Bd. 15, 28.3.2025, Nr. 1, date:12.2025: 1-11
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15802 |
The K-functional and saturated linear approximation processes
Enthalten in Sampling theory, signal processing, and data analysis Bd. 21, 13.6.2023, Nr. 1, date:6.2023: 1-11
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15803 |
The machine learning horizon in cardiac hybrid imaging
Enthalten in European journal of hybrid imaging Bd. 2, 23.7.2018, Nr. 1, date:12.2018: 1-15
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15804 |
The machine learning methods to analyze the using strategy of antiplatelet drugs in ischaemic stroke patients with gastrointestinal haemorrhage
Enthalten in BMC neurology Bd. 23, 13.10.2023, Nr. 1, date:12.2023: 1-8
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15805 |
The mastery of details in the workflow of materials machine learning
Enthalten in npj computational materials Bd. 10, 2.7.2024, Nr. 1, date:12.2024: 1-17
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15806 |
The mechanistic functional landscape of retinitis pigmentosa: a machine learning-driven approach to therapeutic target discovery
Enthalten in Journal of translational medicine Bd. 22, 6.2.2024, Nr. 1, date:12.2024: 1-24
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15807 |
The metabolic clock of ketamine abuse in rats by a machine learning model
Enthalten in Scientific reports Bd. 14, 14.8.2024, Nr. 1, date:12.2024: 1-12
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15808 |
The performance of some machine learning approaches and a rich context model in student answer prediction
Enthalten in Research and practice in technology enhanced learning Bd. 16, 26.4.2021, Nr. 1, date:12.2021: 1-16
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15809 |
The performance of VCS(volume, conductivity, light scatter) parameters in distinguishing latent tuberculosis and active tuberculosis by using machine learning algorithm
Enthalten in BMC infectious diseases Bd. 23, 16.12.2023, Nr. 1, date:12.2023: 1-9
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|
15810 |
The power of one clean qubit in supervised machine learning
Enthalten in Scientific reports Bd. 13, 15.11.2023, Nr. 1, date:12.2023: 1-11
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Administration
