Supervised learning with artificial hydrocarbon networks: an open source implementation and its applications

TL;DR

This paper introduces an open-source R package for Artificial Hydrocarbon Networks (AHN), a novel supervised learning method inspired by organic chemistry, providing tools for creation, training, testing, and visualization of AHN models with practical engineering applications.

Contribution

It presents the ahnr package for R, enabling easier implementation and application of AHN, addressing previous challenges of encoding time and integration with other technologies.

Findings

Provides a comprehensive R package for AHN

Demonstrates applications in engineering

Facilitates machine learning research

Abstract

Artificial hydrocarbon networks (AHN) is a novel supervised learning method inspired on the structure and the inner chemical mechanisms of organic compounds. As any other cutting-edge algorithm, there are two challenges to be faced: time-consuming for encoding and complications to connect with other technologies. Large and open source platforms have proved to be an alternative solution to the latter challenges. In that sense, this paper aims to introduce the ahnr package for R that implements AHN. It provides several functions to create, train, test and visualize AHN. It also includes conventional functions to easily interact with the trained models. For illustration purposes, it presents several examples about the applications of AHN in engineering, as well as, the way to use it. This package is intended to be very useful for scientists and applied researchers interested in machine learning and data modeling. Package availability is in the Comprehensive R Archive Network.