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Ergebnis der Suche nach: "Machine Learning"
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27681 - 27690 von 28135
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Artikel 27681 UPDATING ABOVEGROUND BIOMASS AT A PAN-EUROPEAN SCALE THROUGH SATELLITE DATA AND ARTIFICIAL INTELLIGENCE
Enthalten in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Bd. XLVIII-1/W2-2023, 2023: 1763-1769. 7 S.
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Artikel 27682 UPDATING STRATEGIES FOR DISTANCE BASED CLASSIFICATION MODEL WITH RECURSIVE LEAST SQUARES
Enthalten in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences Bd. V-3-2022, 2022: 163-170. 8 S.
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Artikel 27683 Updating the Characteristics of Hydroelectric Power Plant Pools Based on Statistical Data Using Machine Learning
Enthalten in Power technology and engineering Bd. 58, 21.2.2025, Nr. 5, date:1.2025: 743-749
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Artikel 27684 Upper and lower bounds for complete linkage in general metric spaces
Enthalten in Machine learning Bd. 113, 30.11.2023, Nr. 1, date:1.2024: 489-518
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Artikel 27685 Upscaling methane fluxes from peatlands across a drainage gradient in Ireland using PlanetScope imagery and machine learning tools
Enthalten in Scientific reports Bd. 13, 25.7.2023, Nr. 1, date:12.2023: 1-11
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Artikel 27686 Urban and rural disparities in stroke prediction using machine learning among Chinese older adults
Enthalten in Scientific reports Bd. 15, 25.2.2025, Nr. 1, date:12.2025: 1-9
Online Ressource
Artikel 27687 Urban areas extraction from multi sensor data based on machine learning and data fusion
Enthalten in Pattern recognition and image analysis Bd. 27, 15.6.2017, Nr. 2, date:4.2017: 326-337
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Artikel 27688 Urban change detection: assessing biophysical drivers using machine learning and Google Earth Engine
Enthalten in Environmental monitoring and assessment Bd. 197, 20.3.2025, Nr. 4, date:4.2025: 1-15
Online Ressource
Artikel 27689 Urban flood vulnerability assessment in a densely urbanized city using multi-factor analysis and machine learning algorithms
Enthalten in Theoretical and applied climatology Bd. 149, 4.5.2022, Nr. 1-2, date:7.2022: 639-659
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Artikel 27690 Urban ground subsidence monitoring and prediction using time-series InSAR and machine learning approaches: a case study of Tianjin, China
Enthalten in Environmental earth sciences Bd. 83, 9.8.2024, Nr. 16, date:8.2024: 1-19
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