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32851 - 32860 von 33175

	32851	Using machine learning with case studies to identify practices that reduce greenhouse gas emissions across Australian grain production regions Enthalten in Agronomy for sustainable development Bd. 43, 20.3.2023, Nr. 2, date:4.2023: 1-11
	32852	Using matrix assisted laser desorption ionisation mass spectrometry combined with machine learning for vaccine authenticity screening Enthalten in npj vaccines Bd. 9, 28.8.2024, Nr. 1, date:12.2024: 1-14
	32853	Using mid-infrared spectroscopy and supervised machine-learning to identify vertebrate blood meals in the malaria vector, Anopheles arabiensis Enthalten in Malaria journal Bd. 18, 30.5.2019, Nr. 1, date:12.2019: 1-9
	32854	Using multiple machine learning algorithms to predict spinal cord injury in patients with cervical spondylosis: a multicenter study Enthalten in Scientific reports Bd. 15, 27.10.2025, Nr. 1, date:12.2025: 1-13
	32855	Using music to soothe our patients, measuring hair cortisol, and machine learning in diabetes: Prof. Batrinos would have been very happy! Enthalten in Hormones Bd. 22, 10.11.2023, Nr. 4, date:12.2023: 533-535
	32856	Using natural history to guide supervised machine learning for cryptic species delimitation with genetic data Enthalten in Frontiers in zoology Bd. 19, 22.2.2022, Nr. 1, date:12.2022: 1-15
	32857	Using natural language processing and machine learning to identify breast cancer local recurrence Enthalten in BMC bioinformatics Bd. 19, 28.12.2018, Nr. 17, date:12.2018: 65-74
	32858	Using of Machine Learning Capabilities to Predict Double Phosphate Structures for Biomedical Applications Enthalten in Journal of surface investigation Bd. 18, 15.7.2024, Nr. 3, date:6.2024: 633-640
	32859	USING OPEN DATA CUBE ON THE CLOUD TO INVESTIGATE FOOD SECURITY BY MEANS OF CROPLAND CHANGES IN DJIBOUTI Enthalten in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Bd. XLIII-B3-2022, 2022: 1039-1044. 6 S.
	32860	Using Optimized Deep Learning to Predict Daily Streamflow: A Comparison to Common Machine Learning Algorithms Enthalten in Water resources management Bd. 36, 17.1.2022, Nr. 2, date:1.2022: 699-716

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