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13611 - 13620 von 14087

	13611	Towards smart scanning probe lithography: a framework accelerating nano-fabrication process with in-situ characterization via machine learning Enthalten in Microsystems & nanoengineering Bd. 9, 10.10.2023, Nr. 1, date:12.2023: 1-14
	13612	Towards sustainable construction: estimating compressive strength of waste foundry sand-blended green concrete using a hybrid machine learning approach Enthalten in Discover civil engineering Bd. 2, 3.3.2025, Nr. 1, date:12.2025: 1-25
	13613	Towards the application of machine learning in digital twin technology: a multi-scale review Enthalten in Discover applied sciences Bd. 6, 19.9.2024, Nr. 10, date:10.2024: 1-23
	13614	Towards the prediction of drug solubility in binary solvent mixtures at various temperatures using machine learning Enthalten in Journal of cheminformatics Bd. 16, 28.10.2024, Nr. 1, date:12.2024: 1-17
	13615	Towards the automation of systematic reviews using natural language processing, machine learning, and deep learning: a comprehensive review Enthalten in Artificial intelligence review Bd. 57, 9.7.2024, Nr. 8, date:8.2024: 1-60
	13616	Towards Transnational Fairness in Machine Learning: A Case Study in Disaster Response Systems Enthalten in Minds and machines Bd. 34, 9.5.2024, Nr. 2, date:6.2024: 1-26
	13617	‘Tracheid effect’ light scattering response of common commercial species of Malaysia using red laser (650 nm) for grain angle detection, and machine learning based performance prediction Enthalten in Wood science and technology Bd. 59, 7.5.2025, Nr. 4, date:7.2025: 1-21
	13618	Tracking and tracing water consumption for informed water sensitive intervention through machine learning approach Enthalten in Clean water Bd. 7, 9.4.2024, Nr. 1, date:12.2024: 1-19
	13619	Tracking antibiotic resistance gene pollution from different sources using machine-learning classification Enthalten in Microbiome Bd. 6, 24.5.2018, Nr. 1, date:12.2018: 1-12
	13620	Tracking historical changes in perceived trustworthiness in Western Europe using machine learning analyses of facial cues in paintings Enthalten in Nature Communications Bd. 11, 22.9.2020, Nr. 1, date:12.2020: 1-7

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