Deep Learning Architectures for Approximating Goldbach’s Function in New Regions-Reference-Cited by-同舟云学术

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Deep Learning Architectures for Approximating Goldbach’s Function in New Regions

Published:2022-05-01 Issue:3 Volume:37 Page:27-35
ISSN:1541-1672
Container-title:IEEE Intelligent Systems
language:
Short-container-title:IEEE Intell. Syst.

Author:

Stekel Avigail¹,Azaria Amos¹^ORCID

Affiliation:

1. Ariel University, Ariel, Israel

Funder

The Ministry of Science and Technology Israel

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Computer Networks and Communications

Link

http://xplorestaging.ieee.org/ielx7/9670/9839421/09761945.pdf?arnumber=9761945

Reference21 articles.

1. New discovery on Goldbach;bado;Int J Progress Sci Technol,2019

2. A study of relationship among Goldbach conjecture, twin prime and Fibonacci number;liu;IJ Netw Secur,2017

3. The importance of finding the upper bounds for prime gaps in order to solve the twin primes conjecture and the Goldbach conjecture;berdondini,2020

4. Refinements of Goldbach's conjecture,and the generalized Riemann hypothesis

5. Factoring integers with the number field sieve

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Optimization applications of Goldbach's conjecture;Heliyon;2023-10

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