High-entropy alloys are promising advanced materials for demanding applications, but discovering useful compositions is difficult and expensive due to the vast number of possible element combinations.
Metals are made of randomly oriented crystals at the microscopic-length scale. The alignment of the crystal faces creates an infinite number of configurations and complex patterns, making simulations ...
Keeping high-power particle accelerators at peak performance requires advanced and precise control systems. For example, the primary research machine at the U.S. Department of Energy's Thomas ...
Machine learning for health data science, fuelled by proliferation of data and reduced computational costs, has garnered ...
A machine learning model incorporating functional assessments predicts one-year mortality in older patients with HF and improves risk stratification beyond established scores. Functional status at ...
ABSTRACT: Mathematical optimization is a fundamental aspect of machine learning (ML). An ML task can be conceptualized as optimizing a specific objective using the training dataset to discern patterns ...
ABSTRACT: The purpose of this study was to address the challenges in predicting and classifying accuracy in modeling Container Dwell Time (CDT) using Artificial Neural Networks (ANN). This objective ...
Evolutune, implements a hyperparameter tuner based on the principles of a genetic algorithm. The genetic algorithm evolves a population of hyperparameter sets over several generations, aiming to find ...
In machine learning, finding the perfect settings for a model to work at its best can be like looking for a needle in a haystack. This process, known as hyperparameter optimization, involves tweaking ...
In machine learning, algorithms harness the power to unearth hidden insights and predictions from within data. Central to the effectiveness of these algorithms are hyperparameters, which can be ...
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