Analysis of Business Intelligence Strategies in SMEs using Neural Networks

Authors

  • Huayta-Meza Freddy Toribio
  • Pacheco-Moscoso Luis Enrique
  • Porras-Ccancce Luis Enrique
  • Arias-Valenzuela Edverd Nilton
  • López-Robles Zenón Manuel
  • Ureta-Orihuela Fredy Luis
  • Aquino-Sinche Noemí

DOI:

https://doi.org/10.59670/ml.v20iS7.4559

Abstract

A documentary review was carried out on the production and publication of research papers related to the study of the variables Business Intelligence and Neural Networks. The purpose of the bibliometric analysis proposed in this document was to know the main characteristics of the volume of publications registered in the Scopus database during the period 2017-2022 by Latin American institutions, achieving the identification of 29 publications. The information provided by this platform was organized through graphs and figures, categorizing the information by Year of Publication, Country of Origin, Area of Knowledge and Type of Publication. Once these characteristics have been described, the position of different authors on the proposed topic is referenced through a qualitative analysis. Among the main findings made through this research, it is found that Brazil, with 11 publications, was the country with the highest scientific production registered in the name of authors affiliated with institutions of that nation. The Area of Knowledge that made the greatest contribution to the construction of bibliographic material related to the study of Business Intelligence in SMEs using neural networks was Computer Science  with 23 published documents, and the most used Publication Type during the period indicated above were Conference Articles with 48% of the total scientific production.

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Published

2023-10-06 — Updated on 2023-10-06

How to Cite

Huayta-Meza Freddy Toribio, Pacheco-Moscoso Luis Enrique, Porras-Ccancce Luis Enrique, Arias-Valenzuela Edverd Nilton, López-Robles Zenón Manuel, Ureta-Orihuela Fredy Luis, & Aquino-Sinche Noemí. (2023). Analysis of Business Intelligence Strategies in SMEs using Neural Networks . Migration Letters, 20(S7), 1083–1094. https://doi.org/10.59670/ml.v20iS7.4559

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