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35221 |
The Use of Machine Learning for Predicting Complications of Free Flap Head and Neck Reconstruction: Caution Needed
Enthalten in Annals of surgical oncology Bd. 30, 30.3.2023, Nr. 7, date:7.2023: 4232-4233
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35222 |
The use of machine learning in process–structure–property modeling for material extrusion additive manufacturing: a state-of-the-art review
Enthalten in Associação Brasileira de Engenharia e Ciências Mecânicas: Journal of the Brazilian Society of Mechanical Sciences and Engineering Bd. 46, 11.1.2024, Nr. 2, date:2.2024: 1-24
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35223 |
The use of machine learning in rare diseases: a scoping review
Enthalten in Orphanet journal of rare diseases Bd. 15, 9.6.2020, Nr. 1, date:12.2020: 1-10
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35224 |
The Use of Machine Learning in the Analytical Control of the Preparations of Medicinal Plants
Enthalten in Inorganic materials Bd. 55, 25.2.2020, Nr. 14, date:12.2019: 1428-1438
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35225 |
The use of machine learning methods in classification of pumpkin seeds (Cucurbita pepo L.)
Enthalten in Genetic resources and crop evolution Bd. 68, 25.6.2021, Nr. 7, date:10.2021: 2713-2726
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35226 |
The use of machine learning modeling, virtual screening, molecular docking, and molecular dynamics simulations to identify potential VEGFR2 kinase inhibitors
Enthalten in Scientific reports Bd. 12, 5.11.2022, Nr. 1, date:12.2022: 1-14
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35227 |
The use of machine learning to predict acute hepatopancreatic necrosis disease (AHPND) in shrimp farmed on the east coast of the Mekong Delta of Vietnam
Enthalten in Fisheries science Bd. 86, 6.5.2020, Nr. 4, date:7.2020: 673-683
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35228 |
The use of machine learning to predict the effects of cryoprotective agents on the GelMA-based bioinks used in extrusion cryobioprinting
Enthalten in Bio-design and manufacturing Bd. 6, 14.6.2023, Nr. 4, date:7.2023: 464-477
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35229 |
The use of Multispectral Radio-Meter (MSR5) data for wheat crop genotypes identification using machine learning models
Enthalten in Scientific reports Bd. 13, 14.11.2023, Nr. 1, date:12.2023: 1-15
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35230 |
The Utility of Machine Learning Models for Predicting Chemical Contaminants in Drinking Water: Promise, Challenges, and Opportunities
Enthalten in Current environmental health reports Bd. 10, 17.12.2022, Nr. 1, date:3.2023: 45-60
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