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1501 - 1510 von 1567

	1501	Symptom clusters among cancer survivors: what can machine learning techniques tell us? Enthalten in BMC medical research methodology Bd. 21, 16.8.2021, Nr. 1, date:12.2021: 1-12
	1502	Synergetic use of geospatial and machine learning techniques in modelling landslide susceptibility in parts of Shimla to Kinnaur National Highway, Himachal Pradesh Enthalten in Modeling earth systems and environment Bd. 10, 18.4.2024, Nr. 3, date:6.2024: 4163-4183
	1503	Synergistic integration of Multi-View Brain Networks and advanced machine learning techniques for auditory disorders diagnostics Enthalten in Brain Informatics Bd. 11, 14.1.2024, Nr. 1, date:12.2024: 1-13
	1504	Synergizing multiple machine learning techniques and remote sensing for advanced landslide susceptibility assessment: a case study in the Three Gorges Reservoir Area Enthalten in Environmental earth sciences Bd. 83, 2.4.2024, Nr. 8, date:4.2024: 1-20
	1505	Synergizing quantum techniques with machine learning for advancing drug discovery challenge Enthalten in Scientific reports Bd. 14, 28.12.2024, Nr. 1, date:12.2024: 1-12
	1506	Temporal remote sensing based soil salinity mapping in Indo-Gangetic plain employing machine-learning techniques Enthalten in Indian National Science Academy: Proceedings of the Indian National Science Academy Bd. 89, 20.3.2023, Nr. 2, date:6.2023: 290-305
	1507	Texture feature analysis of MRI-ADC images to differentiate glioma grades using machine learning techniques Enthalten in Scientific reports Bd. 13, 22.9.2023, Nr. 1, date:12.2023: 1-15
	1508	The application of machine learning techniques in posttraumatic stress disorder: a systematic review and meta-analysis Enthalten in npj digital medicine Bd. 7, 9.5.2024, Nr. 1, date:12.2024: 1-13
	1509	The atmospheric boundary layer: a review of current challenges and a new generation of machine learning techniques Enthalten in Artificial intelligence review Bd. 57, 17.10.2024, Nr. 12, date:12.2024: 1-51
	1510	The effect of resampling techniques on the performances of machine learning clinical risk prediction models in the setting of severe class imbalance: development and internal validation in a retrospective cohort Enthalten in Discover artificial intelligence Bd. 4, 26.11.2024, Nr. 1, date:12.2024: 1-17

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