Results 81 to 90 of about 6,098 (213)
GAMM-Mitteilungen, Volume 49, Issue 4, November 2026.Abstract In mechanics of materials, many powerful methods are accessible only to experts. Constitutive models, for example, describe the relation between stress and strain in materials and are a core ingredient of simulations from structural engineering to biomechanics. Yet building and using such models requires specialized expertise—a barrier for the
Marius Tacke +4 more
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Evolutionary neural AutoML for deep learning [PDF]
Proceedings of the Genetic and Evolutionary Computation Conference, 2019 8 ...
Jason Zhi Liang +5 more
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Evaluation of Representation Models for Text Classification with AutoML Tools
, 2023 310322Automated Machine Learning (AutoML) has gained increasing success on tabular data in recent years. However, processing unstructured data like text is a challenge and not widely supported by open-source AutoML tools.
Blohm, Matthias +3 more
core +1 more source
An Empirical Study of Automated Machine Learning Python Libraries Using Source Code Analysis
Applied Computer SystemsThe growth of Automated Machine Learning (AutoML) has expanded access to machine learning workflows by enabling the automation of tasks and reducing the technical barrier to entry. However, the reliability and maintainability of these libraries depend on
O’Leary Christian +2 more
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Computational and Structural Biotechnology JournalComputational modeling has earned significant interest as an alternative to animal testing of toxicity assessment. However, the process of selecting an appropriate algorithm and fine-tuning hyperparameters for the developing of optimized models takes ...
Xiao Xiao +4 more
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Recent Advances in Biogas Production and Upgrading Technologies for Sustainable Energy Systems
GCB Bioenergy, Volume 18, Issue 11, November 2026.Biogas technology is evolving from waste treatment into an intelligent renewable energy platform. Recent advances in anaerobic digestion, pretreatment, reactor design, biomethane upgrading, and AI‐driven process control improve methane recovery, stability, and gas purity.
Temechew Dessie +2 more
wiley +1 more source
AutoML: Exploration v.s. Exploitation
CoRR, 2019 Building a machine learning (ML) pipeline in an automated way is a crucial and complex task as it is constrained with the available time budget and resources. This encouraged the research community to introduce several solutions to utilize the available time and resources.
Eldeeb, Hassan, Eldallal, Abdelrhman
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Understanding the Rise of Automated Machine Learning: A Global Overview and Topic Analysis
InformationAutomated Machine Learning (AutoML) has become an important area of modern artificial intelligence, enabling computers to automate the selection, training, and tuning of machine learning models and offering exciting opportunities for enhanced decision ...
George-Cristian Tătaru +6 more
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Lithium-ion battery state of health estimation using automated machine learning (AutoML): performance comparison [PDF]
PeerJ Computer ScienceAccurate estimation of the state of health (SoH) of lithium-ion batteries is essential for ensuring the safety, longevity, and performance of battery-powered systems, especially in electric vehicles and portable electronics.
Hasibe Candan Kadem, Onur Kadem
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Journal of Geophysical Research: Atmospheres, Volume 131, Issue 20, 28 October 2026.Abstract Turbulence dissipation rate (EDR) governs the conversion of turbulent kinetic energy to heat and thereby controls vertical mixing, transport, and energy exchange in the convective boundary layer (CBL). The spatiotemporal structure of EDR remains poorly constrained, as observations are limited to point measurements or sparse vertical profiles ...
Yufei Chu +10 more
wiley +1 more source