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Live American Sign Language Letter Classification with Convolutional Neural Networks
This project is centered around building a neural network that is able to recognize ASL letters in images, particularly within the scope of a live video feed.
Optimising a magnitude-limited spectroscopic training sample for photometric classification of supernovae
Classification performance noticeably improves when we combine the magnitude-limited training sample with a simulated realistic sample of faint, high-redshift supernovae observed from larger spectroscopic facilities; the algorithms' range of average area under ROC curve (AUC) scores over 10 runs increases from 0. 547-0. 628 to 0. 946-0. 969 and purity of the classified sample reaches 95 per cent in all runs for 2 of the 4 algorithms.
Instrumentation and Methods for Astrophysics
