Skip to main navigation menu Skip to main content Skip to site footer
Español (España) | English
Editorial
Revistas/Journals
Salud, Ciencia y Tecnología Metaverse Basic and Applied Research Data & Metadata Interdisciplinary Rehabilitation Salud, Ciencia y Tecnología - Serie de Conferencias Community and Interculturality in Dialogue Seminars in Medical Writing and Education Health Leadership and Quality of Life Gamification and Augmented Reality Southern perspective / Perspectiva austral
Home
Issue
Current Issue Archives Special Issue
About
About the Journal Editorial Team Privacy Statement Digital preservation policy Contact
Author Guidelines
Guidelines for authors Peer Review Process Open Access Policy and Copyright Ethical aspects Anti-plagiarism policy Crossmark policy page
Indexing
×
Español (España) | English
Editorial

Salud, Ciencia y Tecnología Metaverse Basic and Applied Research Data & Metadata Interdisciplinary Rehabilitation Salud, Ciencia y Tecnología - Serie de Conferencias Community and Interculturality in Dialogue Seminars in Medical Writing and Education Health Leadership and Quality of Life Gamification and Augmented Reality Southern perspective / Perspectiva austral
Home

Current Issue Archives Special Issue

About the Journal Editorial Team Privacy Statement Digital preservation policy Contact

Guidelines for authors Peer Review Process Open Access Policy and Copyright Ethical aspects Anti-plagiarism policy Crossmark policy page
Indexing
Original
Published: 2024-05-28
DOI: https://doi.org/10.56294/saludcyt20241068

Machine Learning in Industry 4.0: a systematic review

By
Luis Stalin López Telenchana ,
Luis Stalin López Telenchana

Universidad Nacional de Chimborazo, Ingeniería Industrial. Chimborazo, Ecuador

Search this author on:

PubMed | Google Scholar
Gabriela Joseth Serrano Torres ,
Gabriela Joseth Serrano Torres

Universidad Nacional de Chimborazo, Ingeniería Industrial. Chimborazo, Ecuador

Search this author on:

PubMed | Google Scholar
Ximena Alexandra Quintana López ,
Ximena Alexandra Quintana López

Universidad Nacional de Chimborazo, Ingeniería Industrial. Chimborazo, Ecuador

Search this author on:

PubMed | Google Scholar
Diego Marcelo Reina Haro ,
Diego Marcelo Reina Haro

Universidad Nacional de Chimborazo, Ingeniería Industrial. Chimborazo, Ecuador

Search this author on:

PubMed | Google Scholar

Abstract

The purpose of this article is to expose the processes of automatic learning or Machine Learning in Industry 4.0 based on a systematic review that allows the search for studies and research on the use of innovation technologies. To that extent, the proposed objective is developed through a mixed approach, that is, it integrates both a quantitative proposal regarding previous studies on the subject and also, consolidating a qualitative analysis that critically determines the effectiveness of machine learning in the development industrial and commercial, performance and well-being. In this way, the search is carried out in academic databases such as Scopus, Scielo, Dialnet and Redalyc so that research studies or degree works that propose appropriate implementation strategies and help to consolidate a discussion about the machine are linked. Learning in the fourth industrial revolution.

Keywords:

Machine Learning,
Industry 4.0,
technologies,
Innovation ,
productivity,
well-being,

How to Cite

1.
López Telenchana LSL, Serrano Torres GJ, Quintana López XA, Reina Haro DM. Machine Learning in Industry 4.0: a systematic review. Salud, Ciencia y Tecnología [Internet]. 2024 May 28 [cited 2024 Jul. 27];4:1068. Available from: https://revista.saludcyt.ar/ojs/index.php/sct/article/view/1068

Copyright Notice

The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.

Article metrics

Google scholar: See link

Metrics

Metrics Loading ...

Vol. 4 (2024)
See full issue

The statements, opinions and data contained in the journal are solely those of the individual authors and contributors and not of the publisher and the editor(s). We stay neutral with regard to jurisdictional claims in published maps and institutional affiliations.