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16601 - 16610 von 16734
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Artikel 16601 Using genetic algorithms to systematically improve the synthesis conditions of Al-PMOF
Enthalten in Communications chemistry Bd. 5, 10.12.2022, Nr. 1, date:12.2022: 1-8
Online Ressource
Artikel 16602 Using hybrid artificial intelligence approach based on a neuro-fuzzy system and evolutionary algorithms for modeling landslide susceptibility in East Azerbaijan Province, Iran
Enthalten in Earth science informatics Bd. 14, 1.7.2021, Nr. 4, date:12.2021: 1861-1882
Online Ressource
Artikel 16603 Using interpretable boosting algorithms for modeling environmental and agricultural data
Enthalten in Scientific reports Bd. 13, 7.8.2023, Nr. 1, date:12.2023: 1-16
Online Ressource
Artikel 16604 Using knowledge graphs and deep learning algorithms to enhance digital cultural heritage management
Enthalten in Heritage Science Bd. 11, 18.9.2023, Nr. 1, date:12.2023: 1-26
Online Ressource
Artikel 16605 Using Machine Learning Algorithms and Honeypot Systems to Detect Adversarial Attacks on Intrusion Detection Systems
Enthalten in Automatic control and computer sciences Bd. 58, 14.3.2025, Nr. 8, date:12.2024: 1226-1233
Online Ressource
Artikel 16606 Using Machine Learning Algorithms for Water Segmentation in Gas Diffusion Layers of Polymer Electrolyte Fuel Cells
Enthalten in Transport in porous media Bd. 144, 4.8.2022, Nr. 3, date:9.2022: 715-737
Online Ressource
Artikel 16607 Using machine learning algorithms to enhance IoT system security
Enthalten in Scientific reports Bd. 14, 27.5.2024, Nr. 1, date:12.2024: 1-19
Online Ressource
Artikel 16608 Using machine learning algorithms to guide rehabilitation planning for home care clients
Enthalten in BMC medical informatics and decision making Bd. 7, 20.12.2007, Nr. 1, date:12.2007: 1-13
Online Ressource
Artikel 16609 Using machine learning algorithms to identify genes essential for cell survival
Enthalten in BMC bioinformatics Bd. 18, 3.10.2017, Nr. 11, date:10.2017: 19-29
Online Ressource
Artikel 16610 Using machine learning and deep learning algorithms for downtime minimization in manufacturing systems: an early failure detection diagnostic service
Enthalten in The international journal of advanced manufacturing technology Bd. 128, 23.8.2023, Nr. 9-10, date:10.2023: 3857-3883
Online Ressource


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