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A machine learning framework is presented to assess post-earthquake structural safety.
We designed a machine learning framework to classify memes that have been labeled as trending on Twitter.
A machine learning framework is essentially adopted in which a vector quantizer is trained using data generated computationally or collected experimentally.
Additionally, 2,700 venture capital funds regularly submit updates and a machine learning framework crawls through the entire dataset to identify inaccurate posts.
The risk assessment utilizes 56 grayscale coronary features in a machine learning framework while linking information from carotid and coronary plaque burdens due to their common genetic makeup.
The aim of this study is to present a machine learning framework based on large margin ranking methods, which is able to infer a model of target specific sensitivity information from HTSs against multiple targets.
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Machine-learning-based methods do not perform direct structural comparison, but typically follow a machine-learning framework.
Graph theory descriptors can be used as predictor variables (i.e., features) in a machine-learning framework.
The transition rate can be inferred systematically using a machine-learning framework.
PIE the search is a machine-learning framework to classify PPI informative documents.
The differences we identified in characterizing various AMP families by these features are so pronounced that, in a machine-learning framework, they enable for most of the AMP families 100% specificity in separation of peptides of an AMP family from other AMPs, providing sensitivity that ranges from approximately 62% to 100%.
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