Radio modulation recognition with CNN, CLDNN, CGDNN and MCTransformer architectures. Best results were achieved with the CGDNN architecture, which has roughly 50,000 parameters, and the final model has a memory footprint of 636kB. More details can be found in my bachelor thesis linked in the readme file.

Brivez is a bioinformatic tool thought as Quality of Life's improvement, providing high quantity of data in a snap, giving you a quick view on what you could find inside your transcriptome/sequences' list.

A re-built convolutional neural network from paper「DeepSig: deep learning improves signal peptide detection in proteins」