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Ergebnis der Suche nach: "Machine Learning"
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Artikel 371 Data‐Driven Review on Aerogels and Polymeric Aerogels
Enthalten in Angewandte Chemie / International edition 05.06.2025. 28 S.
Online Ressource
Artikel 372 Forest Fire Risk Assessment using Machine Learning and Earth Observation Technique in Himalayan Regions: Insights from Rasuwa District, Nepal
Enthalten in EGUsphere 05.06.2025: 1-27. 27 S.
Online Ressource
Artikel 373 Topology in Biological Piezoelectric Materials
Enthalten in Advanced materials 04.06.2025. 33 S.
Online Ressource
Artikel 374 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
Online Ressource
Artikel 375 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
Online Ressource
Artikel 376 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
Online Ressource
Artikel 377 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
Online Ressource
Artikel 378 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
Online Ressource
Artikel 379 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
Online Ressource
Artikel 380 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
Online Ressource


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