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Ergebnis der Suche nach: "Machine Learning"
im Bestand: Gesamter Bestand

40971 - 40980 von 41204

	40971	Utilizing Ni(II) complex for metal drug-gel particles in cervical cancer treatment and designing novel drugs through machine learning methods Enthalten in Scientific reports Bd. 14, 5.3.2024, Nr. 1, date:12.2024: 1-9
	40972	Utilizing reinforcement learning for de novo drug design Enthalten in Machine learning Bd. 113, 8.4.2024, Nr. 7, date:7.2024: 4811-4843
	40973	Utilizing SMOTE-TomekLink and machine learning to construct a predictive model for elderly medical and daily care services demand Enthalten in Scientific reports Bd. 15, 11.3.2025, Nr. 1, date:12.2025: 1-12
	40974	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
	40975	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
	40976	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
	40977	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
	40978	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
	40979	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
	40980	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