TY - JOUR
T1 - Aplicación Web Basada en Redes Neuronales para el Control de Asistencias con Reconocimiento Facial
T2 - Un Estudio de Caso en una Institución Educativa de La Esperanza, Trujillo-Perú
AU - Carrera-Ponce, Ritz G.
AU - Gonzales-Espinola, Adrian J.
AU - Cieza-Mostacero, Segundo E.
AU - Vega-Gavidia, Edward A.
AU - Bravo-Huivin, Elizabeth K.
N1 - Publisher Copyright:
© 2024, Associacao Iberica de Sistemas e Tecnologias de Informacao. All rights reserved.
PY - 2024
Y1 - 2024
N2 - The study sought to improve attendance control in an educational institution through a web application based on neural networks. Under an applied research approach with a pure experimental design, direct observation was used to evaluate indicators related to the registration and generation of attendance reports, considering 30 records selected through simple random probabilistic sampling. The XP methodology was implemented, which includes the planning, design, development and testing phases. The experimental group showed notable advances compared to the control group: the average attendance registration time decreased by 53.33%, 60% in the generation of reports, and 80% exceeded the average precision of the information. Additionally, 96.67% exceeded the accuracy of the control group. It follows that the digital tool, backed by neural networks, has been essential to enhance the control of attendance in the institution. It was concluded that for the development of the results the Mann-Whitney U and Student’s T statistical tests were used according to the corresponding indicator.
AB - The study sought to improve attendance control in an educational institution through a web application based on neural networks. Under an applied research approach with a pure experimental design, direct observation was used to evaluate indicators related to the registration and generation of attendance reports, considering 30 records selected through simple random probabilistic sampling. The XP methodology was implemented, which includes the planning, design, development and testing phases. The experimental group showed notable advances compared to the control group: the average attendance registration time decreased by 53.33%, 60% in the generation of reports, and 80% exceeded the average precision of the information. Additionally, 96.67% exceeded the accuracy of the control group. It follows that the digital tool, backed by neural networks, has been essential to enhance the control of attendance in the institution. It was concluded that for the development of the results the Mann-Whitney U and Student’s T statistical tests were used according to the corresponding indicator.
KW - Artificial Intelligence
KW - Attendance Monitoring
KW - Extreme Programming
KW - Face Detection
KW - Online Platform
UR - http://www.scopus.com/inward/record.url?scp=85187109666&partnerID=8YFLogxK
M3 - Artículo
AN - SCOPUS:85187109666
SN - 1646-9895
VL - 2024
SP - 17
EP - 30
JO - RISTI - Revista Iberica de Sistemas e Tecnologias de Informacao
JF - RISTI - Revista Iberica de Sistemas e Tecnologias de Informacao
IS - 1 E65
ER -