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38531 |
Using visible and NIR hyperspectral imaging and machine learning for nondestructive detection of nutrient contents in sorghum
Enthalten in Scientific reports Bd. 15, 19.2.2025, Nr. 1, date:12.2025: 1-14
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38532 |
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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38533 |
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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38534 |
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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38535 |
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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38536 |
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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38537 |
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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38538 |
Utility of comprehensive genomic profiling combined with machine learning for prognostic stratification in stage II/III colorectal cancer after adjuvant chemotherapy
Enthalten in International journal of clinical oncology Bd. 30, 17.3.2025, Nr. 5, date:5.2025: 926-934
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38539 |
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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38540 |
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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