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26941 - 26950 von 32527

	26941	Mapping the physics research space: a machine learning approach Enthalten in EPJ Data Science Bd. 8, 6.11.2019, Nr. 1, date:12.2019: 1-18
	26942	Mapping the seamless hourly surface visibility in China: a real-time retrieval framework using a machine-learning-based stacked ensemble model Enthalten in npj climate and atmospheric science Bd. 7, 13.3.2024, Nr. 1, date:12.2024: 1-12
	26943	MAPPING VEGETATION AND MEASURING THE PERFORMANCE OF MACHINE LEARNING ALGORITHM IN LULC CLASSIFICATION IN THE LARGE AREA USING SENTINEL-2 AND LANDSAT-8 DATASETS OF DEHRADUN AS A TEST CASE Enthalten in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Bd. XLIII-B3-2022, 2022: 529-535. 7 S.
	26944	Mapping Vegetation Dynamics in Wyoming: A Multi-Temporal Analysis using Landsat NDVI and Clustering Enthalten in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Bd. XLVIII-M-5-2024, 2025: 87-94. 8 S.
	26945	Mapping wildfire susceptibility in the tropical region of Brunei: a machine learning and explainable AI approach using google earth engine with remote sensing data Enthalten in Earth science informatics Bd. 18, 14.5.2025, Nr. 2, date:6.2025: 1-22
	26946	Mapping wind erosion hazard with regression-based machine learning algorithms Enthalten in Scientific reports Bd. 10, 24.11.2020, Nr. 1, date:12.2020: 1-16
	26947	MapReduce model for efficient image retrieval: a Hadoop-based framework Enthalten in International journal of information technology Bd. 17, 7.12.2024, Nr. 2, date:3.2025: 925-939
	26948	MAPS: pathologist-level cell type annotation from tissue images through machine learning Enthalten in Nature Communications Bd. 15, 2.1.2024, Nr. 1, date:12.2024: 1-11
	26949	MAQML: a Meta-approach to Quantum Machine Learning with Accentuated Sample Variations for Unobtrusive Mental Health Monitoring Enthalten in Quantum machine intelligence Bd. 5, 5.4.2023, Nr. 1, date:6.2023: 1-17
	26950	Marcos López de Prado: Advances in financial machine learning Enthalten in Financial markets and portfolio management Bd. 33, 27.11.2019, Nr. 4, date:12.2019: 491-493

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