Proceedings of the

The 33rd European Safety and Reliability Conference (ESREL 2023)
3 – 8 September 2023, Southampton, UK

On the Construction of Numerical Models through a Prime Convolutional Approach

Doaa Almhaithawi1,a, Massimo Bertini2,c, Stefano Cuomo2,d, Francesco Panelli3, Alessandro Bellini2,e and Tania Cerquitelli1,b

1Department of Control and Computer Engineering, Politecnico di Torino, Torino, Italy.

2Mathema, Italy.

3Independent researcher.

ABSTRACT

In this paper we apply neural network models to a set of natural numbers in order to classify the congruence classes modulo a given integer m ∈ {2, 3, …, 10}. We compare the performances of two kinds of architectures and of several input data representations. It turns out that these tasks are fully solved using a convolutional architecture and a special representation for the input data that exploits the prime factor decomposition of numbers.

Keywords: Neural networks, Natural numbers, Convolutional networks, Prime numbers, Congruence classes.


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