Pattern Recognition and Deep Learning Technologies, Enablers of Industry 4.0, and Their Role in Engineering Research

Joel Serey, Miguel Alfaro, Guillermo Fuertes, Manuel Vargas, Claudia Durán, Rodrigo Ternero, Ricardo Rivera, Jorge Sabattin

Research output: Contribution to journalReview articlepeer-review

12 Citations (Scopus)

Abstract

The purpose of this study is to summarize the pattern recognition (PR) and deep learning (DL) artificial intelligence methods developed for the management of data in the last six years. The methodology used for the study of documents is a content analysis. For this study, 186 references are considered, from which 120 are selected for the literature review. First, a general introduction to artificial intelligence is presented, in which PR/DL methods are studied and their relevance to data management evaluated. Next, a literature review is provided of the most recent applications of PR/DL, and the capacity of these methods to process large volumes of data is evaluated. The analysis of the literature also reveals the main applications, challenges, approaches, advantages, and disadvantages of using these methods. Moreover, we discuss the main measurement instruments; the methodological contributions by study areas and research domain; and major databases, journals, and countries that contribute to the field of study. Finally, we identify emerging research trends, their limitations, and possible future research paths.

Original languageEnglish
Article number535
JournalSymmetry
Volume15
Issue number2
DOIs
Publication statusPublished - Feb 2023

Keywords

  • artificial intelligence
  • data management
  • deep learning
  • pattern recognition

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Chemistry (miscellaneous)
  • General Mathematics
  • Physics and Astronomy (miscellaneous)

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