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Ergebnis der Suche nach: "Machine Learning"
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Artikel 26641 Religious Affiliation in the Twenty-First Century: A Machine Learning Perspective on the World Value Survey
Enthalten in Society Bd. 60, 25.8.2023, Nr. 5, date:10.2023: 733-749
Online Ressource
Artikel 26642 Remaining useful lifetime estimation for metal-bonded grinding tools using hybrid machine learning
Enthalten in The international journal of advanced manufacturing technology Bd. 123, 10.11.2022, Nr. 9-10, date:12.2022: 3243-3260
Online Ressource
Artikel 26643 Remote collaborative process optimization in research and design of industrial manufacturing
Enthalten in Autonomous intelligent systems Bd. 3, 20.11.2023, Nr. 1, date:12.2023: 1-7
Online Ressource
Artikel 26644 Remote Health Monitoring in Clinical Trial using Machine Learning Techniques: A Conceptual Framework
Enthalten in Health and Technology Bd. 12, 11.3.2022, Nr. 2, date:3.2022: 359-364
Online Ressource
Artikel 26645 Remote monitoring of agricultural systems using NDVI time series and machine learning methods: a tool for an adaptive agricultural policy
Enthalten in Arabian journal of geosciences Bd. 13, 13.8.2020, Nr. 16, date:8.2020: 1-14
Online Ressource
Artikel 26646 Remote monitoring of Tai Chi balance training interventions in older adults using wearable sensors and machine learning
Enthalten in Scientific reports Bd. 15, 26.3.2025, Nr. 1, date:12.2025: 1-12
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Artikel 26647 Remote sensing and machine learning algorithms to predict soil salinity in southern Kazakhstan
Enthalten in Discover sustainability Bd. 5, 28.10.2024, Nr. 1, date:12.2024: 1-14
Online Ressource
Artikel 26648 Remote sensing and machine learning applications for aboveground biomass estimation in agroforestry systems: a review
Enthalten in Agroforestry systems Bd. 97, 10.5.2023, Nr. 6, date:8.2023: 1097-1111
Online Ressource
Artikel 26649 Remote sensing based forest cover classification using machine learning
Enthalten in Scientific reports Bd. 14, 2.1.2024, Nr. 1, date:12.2024: 1-17
Online Ressource
Artikel 26650 Remote sensing drought factor integration based on machine learning can improve the estimation of drought in arid and semi-arid regions
Enthalten in Theoretical and applied climatology Bd. 151, 6.1.2023, Nr. 3-4, date:2.2023: 1753-1770
Online Ressource


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