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Ergebnis der Suche nach: "Machine Learning"
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Artikel 391 Impact assessment of land-use and land-cover on urban heat waves using remote sensing and machine learning algorithms
Enthalten in International journal of environmental science and technology 5.6.2025: 1-14
Online Ressource
Artikel 392 Reappraising machine learning models for vascular calcification in CKD: methodological concerns and clinical gaps
Enthalten in International urology and nephrology 5.6.2025: 1-2
Online Ressource
Artikel 393 Topology in Biological Piezoelectric Materials
Enthalten in Advanced materials 04.06.2025. 33 S.
Online Ressource
Artikel 394 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 395 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 396 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 397 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 398 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 399 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 400 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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