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26921 |
Using weak signals to predict spontaneous breathing trial success: a machine learning approach
Enthalten in Intensive Care Medicine Experimental Bd. 13, 18.3.2025, Nr. 1, date:12.2025: 1-14
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26922 |
Using wearable sensors and machine learning to assess upper limb function in Huntington’s disease
Enthalten in Communications medicine Bd. 5, 25.2.2025, Nr. 1, date:12.2025: 1-9
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26923 |
Using XBGoost, an interpretable machine learning model, for diagnosing prostate cancer in patients with PSA < 20 ng/ml based on the PSAMR indicator
Enthalten in Scientific reports Bd. 15, 9.1.2025, Nr. 1, date:12.2025: 1-11
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26924 |
Utilising energy function and variational inference training for learning a graph neural network architecture
Enthalten in Machine learning Bd. 113, 23.1.2024, Nr. 3, date:3.2024: 1219-1241
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26925 |
Utilising machine learning algorithms to predict the Marshall characteristics of asphalt pavement layers
Enthalten in Innovative infrastructure solutions Bd. 9, 14.9.2024, Nr. 10, date:10.2024: 1-22
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26926 |
Utilising unsupervised machine learning and IoT for cost-effective anomaly detection in multi-layer wire arc additive manufacturing
Enthalten in The international journal of advanced manufacturing technology Bd. 135, 24.10.2024, Nr. 5-6, date:11.2024: 2957-2974
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26927 |
Utility of Environmental Complexity as a Predictor of Alzheimer’s Disease Diagnosis: A Big-Data Machine Learning Approach
Enthalten in The journal of prevention of Alzheimer's disease Bd. 10, 26.1.2023, Nr. 2, date:4.2023: 223-235
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26928 |
Utility of machine learning of apparent diffusion coefficient (ADC) and T2-weighted (T2W) radiomic features in PI-RADS version 2.1 category 3 lesions to predict prostate cancer diagnosis
Enthalten in Abdominal radiology Bd. 46, 31.8.2021, Nr. 12, date:12.2021: 5647-5658
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26929 |
Utility of pre-treatment FDG PET/CT–derived machine learning models for outcome prediction in classical Hodgkin lymphoma
Enthalten in European radiology Bd. 32, 25.8.2022, Nr. 10, date:10.2022: 7237-7247
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26930 |
Utilization of artificial intelligence and machine learning in chemistry education: a critical review
Enthalten in Discover education Bd. 3, 10.7.2024, Nr. 1, date:12.2024: 1-15
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Administration
