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Ergebnis der Suche nach: "Machine Learning"
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Artikel 11461 Segmentation and Metallographic Evaluation of Aluminium Slurry Coatings Using Machine Learning Techniques
Enthalten in High temperature corrosion of materials Bd. 101, 26.10.2024, Nr. 6, date:12.2024: 1497-1512
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Artikel 11462 Segmentation characteristics of deep, low-frequency tremors in Shikoku, Japan using machine learning approaches
Enthalten in Earth, planets and space Bd. 75, 8.3.2023, Nr. 1, date:12.2023: 1-19
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Artikel 11463 SEGMENTATION OF INDUSTRIAL BURNER FLAMES: A COMPARATIVE STUDY FROM TRADITIONAL IMAGE PROCESSING TO MACHINE AND DEEP LEARNING
Enthalten in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences Bd. X-1/W1-2023, 2023: 953-960. 8 S.
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Artikel 11464 Segmentation of mature human oocytes provides interpretable and improved blastocyst outcome predictions by a machine learning model
Enthalten in Scientific reports Bd. 14, 8.5.2024, Nr. 1, date:12.2024: 1-14
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Artikel 11465 Segmenting functional tissue units across human organs using community-driven development of generalizable machine learning algorithms
Enthalten in Nature Communications Bd. 14, 3.8.2023, Nr. 1, date:12.2023: 1-11
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Artikel 11466 Seismic characterization of carbonate stringers using machine learning techniques: an example from the western flank of South Oman Salt Basin
Enthalten in Scientific reports Bd. 14, 28.12.2024, Nr. 1, date:12.2024: 1-18
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Artikel 11467 SeizyML: An Application for Semi-Automated Seizure Detection Using Interpretable Machine Learning Models
Enthalten in Neuroinformatics Bd. 23, 3.3.2025, Nr. 2, date:4.2025: 1-17
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Artikel 11468 Selected proceedings of Machine Learning in Systems Biology: MLSB 2016
Enthalten in BMC bioinformatics Bd. 17, 13.12.2016, Nr. 16, date:12.2016: 51-52
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Artikel 11469 Selecting critical features for data classification based on machine learning methods
Enthalten in Journal of Big Data Bd. 7, 23.7.2020, Nr. 1, date:12.2020: 1-26
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Artikel 11470 Selection of casting production parameters with the use of machine learning and data supplementation methods in order to obtain products with the assumed parameters
Enthalten in Archives of civil and mechanical engineering Bd. 23, 2.2.2023, Nr. 2, date:5.2023: 1-18
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