Analysis of Deep Learning models for anomaly detection in virtualized infrastructures: A systematic review

Authors

  • Jaime David Camacho Castillo Escuela Superior Politécnica del Chimborazo image/svg+xml
  • Ángel Patricio Flores Orozco Escuela Superior Politécnica del Chimborazo image/svg+xml
  • Enrique Marcelo Baño Leon Universidad Estatal de Bolívar image/svg+xml
  • Teresita Lourdes Argüello Estrella Investigador Independiente. Ecuador

DOI:

https://doi.org/10.55204/trc.v6i1.e681

Keywords:

Health in older adults, Nutritional assessment, Web application, XP methodology, Software efficiency

Abstract

This article proposes the development of a web application for the management of physical health and nutrition of older adults in the Guano Gerontological Center. Since there is an increase of this population in Ecuador, it is important to address mobility problems and nutritional deficiencies. The application allows the evaluation of the physical health status through the use of functional tests and questionnaires, offering personalized nutritional recommendations according to the needs. Extreme Programming (XP) methodology was used to ensure agile development. The technological architecture includes a backend in Django, frontend in React and database in PostgreSQL. Finally, the efficiency of the system was evaluated according to the ISO 25010 standard, with positive results in response times and use of resources.

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References

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Published

2026-06-15

Issue

Section

Original Research Articles

How to Cite

Camacho Castillo, J. D., Flores Orozco, Ángel P., Baño Leon, E. M., & Argüello Estrella, T. L. (2026). Analysis of Deep Learning models for anomaly detection in virtualized infrastructures: A systematic review. Tesla Revista Científica, 6(1), e681. https://doi.org/10.55204/trc.v6i1.e681