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27171 - 27180 von 28816

	27171	Signature-based Adaptive Cloud Resource Usage Prediction Using Machine Learning and Anomaly Detection Enthalten in Journal of grid computing Bd. 22, 23.4.2024, Nr. 2, date:6.2024: 1-15
	27172	Signature-based intrusion detection using machine learning and deep learning approaches empowered with fuzzy clustering Enthalten in Scientific reports Bd. 15, 11.1.2025, Nr. 1, date:12.2025: 1-33
	27173	Signatures of paracrystallinity in amorphous silicon from machine-learning-driven molecular dynamics Enthalten in Nature Communications Bd. 16, 10.3.2025, Nr. 1, date:12.2025: 1-8
	27174	Significance of EMHD graphene oxide (GO) water ethylene glycol nanofluid flow in a Darcy–Forchheimer medium by machine learning algorithm Enthalten in The European physical journal / Plus Bd. 138, 7.3.2023, Nr. 3, date:3.2023: 1-15
	27175	Significance of gender, brain region and EEG band complexity analysis for Parkinson’s disease classification using recurrence plots and machine learning algorithms Enthalten in Physical and engineering sciences in medicine Bd. 48, 27.1.2025, Nr. 1, date:3.2025: 391-407
	27176	Significance of Sequence Features in Classification of Protein–Protein Interactions Using Machine Learning Enthalten in The protein journal Bd. 43, 19.12.2023, Nr. 1, date:2.2024: 72-83
	27177	Silent no more: a comprehensive review of artificial intelligence, deep learning, and machine learning in facilitating deaf and mute communication Enthalten in Artificial intelligence review Bd. 57, 26.6.2024, Nr. 7, date:7.2024: 1-46
	27178	Silica fume as a supplementary cementitious material in pervious concrete: prediction of compressive strength through a machine learning approach Enthalten in Asian journal of civil engineering Bd. 25, 2.1.2024, Nr. 3, date:4.2024: 2963-2977
	27179	Silica sources for arsenic mitigation in rice: machine learning-based predictive modeling and risk assessment Enthalten in Environmental science and pollution research Bd. 30, 18.10.2023, Nr. 53, date:11.2023: 113660-113673
	27180	Similarity of fast and slow earthquakes illuminated by machine learning Enthalten in Nature geoscience Bd. 12, 17.12.2018, Nr. 1, date:1.2019: 69-74

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