Katalog der Deutschen Nationalbibliothek

Ergebnis der Suche nach: "Machine Learning"
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41921 - 41930 von 41937

	41921	XEMLPD: an explainable ensemble machine learning approach for Parkinson disease diagnosis with optimized features Enthalten in International journal of speech technology Bd. 27, 16.10.2024, Nr. 4, date:12.2024: 1055-1083
	41922	XGBoost-based machine learning test improves the accuracy of hemorrhage prediction among geriatric patients with long-term administration of rivaroxaban Enthalten in BMC geriatrics Bd. 23, 10.7.2023, Nr. 1, date:12.2023: 1-12
	41923	XGBoost model for predicting erectile dysfunction risk after radical prostatectomy: development and validation using machine learning Enthalten in Discover oncology Bd. 16, 19.5.2025, Nr. 1, date:12.2025: 1-16
	41924	XGBoosting Cricket: Enhancing Predictive Modeling for Twenty20 Match Results Using Machine Learning and Statistical Techniques Enthalten in SN Computer Science Bd. 5, 9.11.2024, Nr. 8, date:12.2024: 1-7
	41925	XMID-MQTT: explaining machine learning-based intrusion detection system for MQTT protocol in IoT environment Enthalten in International journal of information security Bd. 24, 14.5.2025, Nr. 3, date:6.2025: 1-22
	41926	Year-round behavioural time budgets of common woodpigeons inferred from acceleration data using machine learning Enthalten in Behavioral ecology and sociobiology Bd. 77, 31.3.2023, Nr. 4, date:4.2023: 1-12
	41927	Year-round daily wildfire prediction and key factor analysis using machine learning: a case study of Gangwon State, South Korea Enthalten in Scientific reports Bd. 15, 15.8.2025, Nr. 1, date:12.2025: 1-12
	41928	YI 2.1 Pulse Wave Velocity Estimation from the Radial Pulse Waveform using Gaussian Process Regression: A Machine Learning Based Study Enthalten in Artery research Bd. 26, 31.12.2020, Nr. 1, date:12.2020: S12
	41929	Yield prediction by machine learning from UAS-based multi-sensor data fusion in soybean Enthalten in Plant methods Bd. 16, 1.6.2020, Nr. 1, date:12.2020: 1-16
	41930	Yield prediction, pest and disease diagnosis, soil fertility mapping, precision irrigation scheduling, and food quality assessment using machine learning and deep learning algorithms Enthalten in Discover food Bd. 5, 20.3.2025, Nr. 1, date:12.2025: 1-23

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