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35801 |
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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35802 |
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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35803 |
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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35804 |
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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35805 |
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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35806 |
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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35807 |
Utilization of machine learning algorithm in the prediction of rehospitalization during one-year post traumatic spinal cord injury
Enthalten in Spinal cord Bd. 63, 15.2.2025, Nr. 4, date:4.2025: 214-221
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35808 |
Utilization of machine learning approach for production of optimized PLGA nanoparticles for drug delivery applications
Enthalten in Scientific reports Bd. 15, 14.3.2025, Nr. 1, date:12.2025: 1-14
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35809 |
Utilization of machine learning for dengue case screening
Enthalten in BMC public health Bd. 24, 11.6.2024, Nr. 1, date:12.2024: 1-9
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35810 |
Utilization of machine learning for the estimation of production rates in wells operated by electrical submersible pumps
Enthalten in Journal of petroleum exploration and production technology Bd. 14, 22.2.2024, Nr. 5, date:5.2024: 1205-1233
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Administration
