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32441 - 32450 von 32671

	32441	Utilizing social media and machine learning for personality and emotion recognition using PERS Enthalten in Neural computing & applications Bd. 35, 5.9.2023, Nr. 33, date:11.2023: 23927-23941
	32442	Utilizing Supervised Machine Learning and Technical Indicators for Quantitative Trading to Improve Stock Market Trading Decisions Enthalten in SN Computer Science Bd. 6, 26.5.2025, Nr. 5, date:6.2025: 1-17
	32443	UWB anchor nodes self-calibration in NLOS conditions: a machine learning and adaptive PHY error correction approach Enthalten in Wireless networks Bd. 27, 7.5.2021, Nr. 4, date:5.2021: 3007-3023
	32444	VacPred: Sequence-based prediction of plant vacuole proteins using machine-learning techniques Enthalten in Journal of biosciences Bd. 45, 27.8.2020, Nr. 1, date:12.2020: 1-9
	32445	Vaginal Microenvironment-Driven neural network model for predicting multiple HPV co-infections: a machine learning clinical decision framework Enthalten in BMC women's health Bd. 25, 14.10.2025, Nr. 1, date:12.2025: 1-11
	32446	Valid inference for machine learning-assisted genome-wide association studies Enthalten in Nature genetics Bd. 56, 30.9.2024, Nr. 11, date:11.2024: 2361-2369
	32447	Validating administrative data to identify complex surgical site infections following cardiac implantable electronic device implantation: a comparison of traditional methods and machine learning Enthalten in Antimicrobial resistance and infection control Bd. 11, 10.11.2022, Nr. 1, date:12.2022: 1-9
	32448	Validating The Philippine Mangrove Map 2023 through Capacity Building and Crowdsourcing Enthalten in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences Bd. X-5-2024, 2024: 243-250. 8 S.
	32449	Validation of a blood biomarker panel for machine learning-based radiation biodosimetry in juvenile and adult C57BL/6 mice Enthalten in Scientific reports Bd. 14, 12.10.2024, Nr. 1, date:12.2024: 1-10
	32450	Validation of a machine learning algorithm for early severe sepsis prediction: a retrospective study predicting severe sepsis up to 48 h in advance using a diverse dataset from 461 US hospitals Enthalten in BMC medical informatics and decision making Bd. 20, 27.10.2020, Nr. 1, date:12.2020: 1-10

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