Katalog der Deutschen Nationalbibliothek

Ergebnis der Suche nach: "Machine Learning"
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	401	A Review of Data-Driven Machine Learning Applications in Reservoir Petrophysics Enthalten in The Arabian journal for science and engineering 4.6.2025: 1-35
	402	Advanced sound-based pest detection in agriculture using deep learning and adaptive optimization Enthalten in International journal of information technology 4.6.2025: 1-11
	403	Advancements and future trends in machine learning for lung cancer: a comprehensive bibliometric analysis Enthalten in Clinical and translational oncology 4.6.2025: 1-12
	404	An approach based on machine learning and mel-frequency cepstral coefficients for locating faults in transmission lines Enthalten in Energy systems 4.6.2025: 1-28
	405	Anoikis-Related Genes Can Accurately Predict the Occurrence of Endometriosis: A Retrospective Cohort Study via Machine Learning Analysis Enthalten in Biochemical genetics 4.6.2025: 1-20
	406	Answer to the Letter to the Editor of H. Daungsupawong, et al. concerning “A multimodal machine learning model integrating clinical and MRI data for predicting neurological outcomes following surgical treatment for cervical spinal cord injury” by Shimizu T, et al. (Eur Spine J [2025]: doi: 10.1007/s00586-025-08873-2) Enthalten in European spine journal 4.6.2025: 1
	407	Answer to the Letter to the Editor of W. Yue, et al. concerning “A multimodal machine learning model integrating clinical and MRI data for predicting neurological outcomes following surgical treatment for cervical spinal cord injury” by Shimizu T, et al. (Eur Spine J [2025]: doi: 10.1007/s00586-025-08873-2) Enthalten in European spine journal 4.6.2025: 1-2
	408	CLRD-GLPS: A Long-term Seasonal Dataset of Ruminant Livestock Distribution in China's Grazing Production Systems (2000–2021) Using Stacking-based Interpretable Machine Learning Enthalten in Earth system science data discussions 04.06.2025: 1-45. 45 S.
	409	Investigating the association between groundwater contaminants and hypertension risk in India: a machine learning-based analysis Enthalten in Journal of exposure science & environmental epidemiology 4.6.2025: 1-18
	410	Letter to the Editor concerning “A multimodal machine learning model integrating clinical and MRI data for predicting neurological outcomes following surgical treatment for cervical spinal cord injury” by Shimizu T, et al. (Eur Spine J [2025]: doi: 10.1007/s00586-025-08873-2) Enthalten in European spine journal 4.6.2025: 1-2

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