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27081 |
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
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27082 |
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
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27083 |
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
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27084 |
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
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27085 |
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
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27086 |
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
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27087 |
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
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27088 |
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
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27089 |
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
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27090 |
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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