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Ergebnis der Suche nach: "Machine Learning"
im Bestand: Gesamter Bestand

26271 - 26280 von 29411
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Artikel 26271 Prediction of circularity of the holes in electrical discharge machining of inconel alloy using machine learning technique
Enthalten in Interactions Bd. 245, 26.8.2024, Nr. 1, date:12.2024: 1-10
Online Ressource
Artikel 26272 Prediction of clinical stages of cervical cancer via machine learning integrated with clinical features and ultrasound-based radiomics
Enthalten in Scientific reports Bd. 15, 29.5.2025, Nr. 1, date:12.2025: 1-8
Online Ressource
Artikel 26273 Prediction of CO2 solubility in aqueous and organic solvent systems through machine learning techniques
Enthalten in Modeling earth systems and environment Bd. 11, 16.12.2024, Nr. 1, date:2.2025: 1-9
Online Ressource
Artikel 26274 Prediction of CO2 storage site integrity with rough set-based machine learning
Enthalten in Clean technologies and environmental policy Bd. 21, 23.7.2019, Nr. 8, date:10.2019: 1655-1664
Online Ressource
Artikel 26275 Prediction of coastal erosion susceptible areas of Quang Nam Province, Vietnam using machine learning models
Enthalten in Earth science informatics Bd. 17, 5.12.2023, Nr. 1, date:2.2024: 401-419
Online Ressource
Artikel 26276 Prediction of coating degradation based on “Environmental Factors–Physical Property–Corrosion Failure” two-stage machine learning
Enthalten in npj Materials degradation Bd. 9, 10.6.2025, Nr. 1, date:12.2025: 1-13
Online Ressource
Artikel 26277 Prediction of compressive strength of concrete doped with waste plastic using machine learning-based advanced regularized regression models
Enthalten in Asian journal of civil engineering Bd. 26, 14.2.2025, Nr. 4, date:4.2025: 1723-1741
Online Ressource
Artikel 26278 Prediction of compressive strength of concrete under various curing conditions: a comparison of machine learning models and empirical mathematical models
Enthalten in Innovative infrastructure solutions Bd. 9, 21.6.2024, Nr. 7, date:7.2024: 1-15
Online Ressource
Artikel 26279 Prediction of compressive strength of fiber-reinforced concrete containing silica (SiO2) based on metaheuristic optimization algorithms and machine learning techniques
Enthalten in Scientific reports Bd. 15, 4.6.2025, Nr. 1, date:12.2025: 1-16
Online Ressource
Artikel 26280 Prediction of compressive strength of granite: use of machine learning techniques and intelligent system
Enthalten in Earth science informatics Bd. 16, 15.11.2023, Nr. 4, date:12.2023: 4113-4129
Online Ressource


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