About this document

Journal Pone 0287025 by Abdarazak is a document available to read on EtoBox.

This research article presents a novel learned pseudo-random number generator (LPRNG) using a Wasserstein distance-based generative adversarial network (WGAN) that generates random numbers satisfying the NIST test suite. The LPRNG improves upon traditional methods by eliminating dropout layers to enhance learning from seed numbers with poor randomness properties, allowing for the creation of customizable PRNGs without deep mathematical knowledge. The findings indicate that the LPRNG can effectively democrat

Author

Abdarazak

Language

EN