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Ergebnis der Suche nach: "Machine Learning"
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15631 - 15640 von 17052

	15631	Prop3D: A flexible, Python-based platform for machine learning with protein structural properties and biophysical data Enthalten in BMC bioinformatics Bd. 25, 4.1.2024, Nr. 1, date:12.2024: 1-24
	15632	Proportion-based normalizations outperform compositional data transformations in machine learning applications Enthalten in Microbiome Bd. 12, 5.3.2024, Nr. 1, date:12.2024: 1-13
	15633	Proportional impact prediction model of coating material on nitrate leaching of slow-release Urea Super Granules (USG) using machine learning and RSM technique Enthalten in Scientific reports Bd. 14, 6.2.2024, Nr. 1, date:12.2024: 1-18
	15634	Proposal of a new equation for estimating resting energy expenditure of acute kidney injury patients on dialysis: a machine learning approach Enthalten in Nutrition & metabolism Bd. 17, 17.11.2020, Nr. 1, date:12.2020: 1-8
	15635	Proposing a machine-learning based method to predict stillbirth before and during delivery and ranking the features: nationwide retrospective cross-sectional study Enthalten in BMC pregnancy and childbirth Bd. 21, 12.3.2021, Nr. 1, date:12.2021: 1-17
	15636	Proposing a machine learning-based model for predicting nonreassuring fetal heart Enthalten in Scientific reports Bd. 15, 6.3.2025, Nr. 1, date:12.2025: 1-8
	15637	Proposing a short version of the Unesp-Botucatu pig acute pain scale using a novel application of machine learning technique Enthalten in Scientific reports Bd. 15, 28.2.2025, Nr. 1, date:12.2025: 1-11
	15638	Proposing an ensemble machine learning based drought vulnerability index using M5P, dagging, random sub-space and rotation forest models Enthalten in Stochastic environmental research and risk assessment Bd. 37, 6.3.2023, Nr. 7, date:7.2023: 2513-2540
	15639	Prospective prediction of PTSD diagnosis in a nationally representative sample using machine learning Enthalten in BMC psychiatry Bd. 20, 10.11.2020, Nr. 1, date:12.2020: 1-10
	15640	Prospects of non-resonant di-Higgs searches and Higgs boson self-coupling measurement at the HE-LHC using machine learning techniques Enthalten in Journal of high energy physics Bd. 2020, 28.12.2020, Nr. 12, date:12.2020: 1-47

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