Katalog der Deutschen Nationalbibliothek

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32661 - 32670 von 33404

	32661	Two-stage machine learning models for bowel lesions characterisation using self-propelled capsule dynamics Enthalten in Nonlinear dynamics Bd. 111, 8.9.2023, Nr. 20, date:10.2023: 19387-19410
	32662	Two-tier network anomaly detection model: a machine learning approach Enthalten in Journal of intelligent information systems Bd. 48, 19.11.2015, Nr. 1, date:2.2017: 61-74
	32663	Type 2 diabetes and susceptibility to COVID-19: a machine learning analysis Enthalten in BMC endocrine disorders Bd. 24, 21.10.2024, Nr. 1, date:12.2024: 1-9
	32664	Type2 Diabetes Classification from Short Photoplethysmogram Signal Using Multiple Domain Features and Machine Learning Techniques Enthalten in Research on biomedical engineering Bd. 39, 22.6.2023, Nr. 3, date:9.2023: 541-560
	32665	UAV and AI for large-scale monitoring of the effects of climate change on mountain lake areas: the ACLIMO cross-border cooperation project Enthalten in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Bd. XLVIII-2/W11-2025, 2025: 203-210. 8 S.
	32666	UAV-BASED RIVER PLASTIC DETECTION WITH A MULTISPECTRAL CAMERA Enthalten in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Bd. XLIII-B3-2022, 2022: 855-861. 7 S.
	32667	UAV imaging for spectral characterization of Coffee Leaf Miner (<i>Leucoptera coffeella</i>) infestation in the Cerrado Mineiro region Enthalten in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences Bd. X-3-2024, 2024: 285-291. 7 S.
	32668	Ubiquitous Health Technology Management (uHTM) Enthalten in Polytechnica Bd. 4, 26.4.2021, Nr. 1, date:4.2021: 47-57
	32669	UbiSite: incorporating two-layered machine learning method with substrate motifs to predict ubiquitin-conjugation site on lysines Enthalten in BMC systems biology Bd. 10, 11.1.2016, Nr. 1, date:12.2016: 49-61
	32670	UCoDe: unified community detection with graph convolutional networks Enthalten in Machine learning Bd. 112, 20.9.2023, Nr. 12, date:12.2023: 5057-5080

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