| Issue |
E3S Web Conf.
Volume 413, 2023
XVI International Scientific and Practical Conference “State and Prospects for the Development of Agribusiness - INTERAGROMASH 2023”
|
|
|---|---|---|
| Article Number | 06009 | |
| Number of page(s) | 9 | |
| Section | Environment Protection Engineering | |
| DOI | https://doi.org/10.1051/e3sconf/202341306009 | |
| Published online | 11 August 2023 | |
Approximation model based on LSTM for predicting the next prime number in an infinite sequence
T.F. Gorbachev Kuzbass State Technical University, 28 Vesennya st., 650000 Kemerovo, Russia
* Corresponding author: pylovpa@kuzstu.ru
Prime numbers are a special set of natural numbers that have captured the attention of mathematicians since ancient times. As prime numbers are a fundamental component in many areas of mathematics, they have naturally found wide applications in various fields of knowledge, such as cryptography. The goal of all researchers is to discover the distributional relationships within this infinite set of numbers or, at the very least, to create a mathematical model for predicting the next prime number in a diverging sequence. This article is dedicated to an attempt at solving this problem based on a deep learning model -Long Short-Term Memory (LSTM) neural network.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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