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35781 |
Unsupervised machine learning approaches to the q-state Potts model
Enthalten in The European physical journal / B / Condensed matter and complex systems Bd. 95, 12.11.2022, Nr. 11, date:11.2022: 1-12
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35782 |
Unsupervised machine learning-based multi-attributes analysis for enhancing gas channel detection and facies classification in the serpent field, offshore Nile Delta, Egypt
Enthalten in Geomechanics and geophysics for geo-energy and geo-resources Bd. 10, 28.11.2024, Nr. 1, date:12.2024: 1-22
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35783 |
Unsupervised machine learning based on clinical factors for the detection of coronary artery atherosclerosis in type 2 diabetes mellitus
Enthalten in Cardiovascular diabetology Bd. 21, 28.11.2022, Nr. 1, date:12.2022: 1-10
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35784 |
Unsupervised machine learning clustering approach for hospitalized COVID-19 pneumonia patients
Enthalten in BMC pulmonary medicine Bd. 25, 8.2.2025, Nr. 1, date:12.2025: 1-9
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35785 |
Unsupervised machine learning combined with 4D scanning transmission electron microscopy for bimodal nanostructural analysis
Enthalten in Scientific reports Bd. 14, 5.2.2024, Nr. 1, date:12.2024: 1-10
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35786 |
Unsupervised machine learning for clustering forward head posture, protraction and retraction movement patterns based on craniocervical angle data in individuals with nonspecific neck pain
Enthalten in BMC musculoskeletal disorders Bd. 25, 13.5.2024, Nr. 1, date:12.2024: 1-10
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35787 |
Unsupervised machine learning for disease prediction: a comparative performance analysis using multiple datasets
Enthalten in Health and Technology Bd. 14, 29.12.2023, Nr. 1, date:1.2024: 141-154
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35788 |
Unsupervised machine learning for local stress identification in fatigue analysis of welded joints
Enthalten in Welding in the world Bd. 69, 19.11.2024, Nr. 1, date:1.2025: 213-226
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35789 |
Unsupervised machine learning identifies distinct ALS molecular subtypes in post-mortem motor cortex and blood expression data
Enthalten in Acta Neuropathologica Communications Bd. 11, 21.12.2023, Nr. 1, date:12.2023: 1-20
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35790 |
Unsupervised machine learning identifies distinct phenotypes in cardiac complications of pediatric patients treated with anthracyclines
Enthalten in Cardio-Oncology Bd. 10, 28.10.2024, Nr. 1, date:12.2024: 1-11
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