Purpose -The purpose of this article is to present the results obtained from literary research which aims to determine if the implementation of an ERP system of industry 4.0 can improve the processes of academic management of universities. Methodology -This research article has a scope that includes a critical analysis, search in database engines and review of scientific journals that are derived from the Technological Surveillance Service of the National University of Trujillo on the specific topic taken as the object of investigation. Findings -According to the exploration carried out of the cases of implementation of ERP software for academic management, several options were found according to each need of the companies (universities, institutes, schools), where the improvement of academic management in general for each part of the organization thanks to the implementation of an ERP of industry 4.0. Value -The research reviews and analyzes the different contexts in which these systems are implemented, the main solutions proposed and which are the most appropriate tools for each context. It also aims to promote the implementation of this type of system by, not only universities, but any type of institution that provides academic services, in order to improve the organization's processes and strengthen its value chain.

El presente artículo tiene como objetivo principal el desarrollo de un sistema que permita el reconocimiento facial de una persona para el control de accesos mediante Inteligencia Artificial. Para el desarrollo del sistema se tuvo como algoritmo Redes Neuronales Convolucionales, el cual es un modelo de reconocimiento. Así mismo se utilizó el lenguaje de programación Python y las librerías siguientes como Numpy, Os, OpenCV e Imutils para su implementación. Los resultados obtenidos según el acierto y utilizando un dataset de 450 imágenes por individuo son de un 88% aproximadamente en cuanto la predicción por persona, concluyendo que el sistema de reconocimiento es eficaz y tiene mayor eficiencia incrementando el tamaño de datasets generados por individuos.