Decentralized Personal Cloud Data Model and its Application in Campus Health Information System

Weng, Xiaoqi; Wu, Hanlin; Pan, Yuqiu; Chen, Hong

doi:10.1109/dasc-picom-cbdcom-cyberscitech52372.2021.00146

Cited by 5 publications

(2 citation statements)

References 9 publications

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“…This method was patented, producing successful outcomes in the meter reading market in Brazil. On the other hand, [18] proposes a personal and decentralized cloud data model to manage health data in schools using realtime NoSQL databases provided by the Firebase platform. Through this service, a school health information system can have full control over sensitive data such as the student's school number, name, temperature test time, temperature data, and test machine number.…”

Section: Research Backgroundmentioning

confidence: 99%

Optimizing Attendance Management in Educational Institutions Through Mobile Technologies: A Machine Learning and Cloud Computing Approach

Caytuiro-Silva,

Maraza Quispe,

Castro-Gutierrez

et al. 2024

Int. J. Interact. Mob. Technol.

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The primary goal of the study is to optimize and streamline the attendance recording and monitoring process for learning sessions by leveraging advanced technologies such as machine learning and cloud computing. The methodology employed is based on the extreme programming (XP) project management approach. Throughout its phases, the entire implementation process of the application, from conception to launch, is described in detail. Firebase is used as the database manager to ensure the efficiency and security of student information and attendance records. Additionally, the Firebase machine learning kit is used to verify attendance registration through QR codes. The application was tested with fifth-year high school students from an educational institution. The user interface has been designed to be attractive, intuitive, and easy to use for both teachers and students. The study results demonstrate that the use of this application significantly reduces the time spent on attendance recording compared to traditional methods. There has been a high level of satisfaction and acceptance of the “ASYS” application among teachers and students. In conclusion, this study has successfully implemented a mobile application that revolutionizes attendance recording and monitoring in educational institutions. It harnesses the power of machine learning and cloud computing to enhance efficiency and the user experience.

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Section: Research Backgroundmentioning

confidence: 99%

Optimizing Attendance Management in Educational Institutions Through Mobile Technologies: A Machine Learning and Cloud Computing Approach

Caytuiro-Silva,

Maraza Quispe,

Castro-Gutierrez

et al. 2024

Int. J. Interact. Mob. Technol.

View full text Add to dashboard Cite

show abstract

“…Este método fue patentado, generando resultados viables en el mercado de lectura de medidores en Brasil. Por otro lado, en [17] proponen un modelo de datos en la nube personal y descentralizado con el fin de gestionar datos de salud en escolares usando la base de datos NoSQL en tiempo real proporcionada por la plataforma Firebase. A través de este servicio, un sistema de información de salud de una escuela puede tener control total sobre la administración de datos sensibles como número de escuela del estudiante, el nombre, la hora de la prueba de temperatura, los datos de temperatura y el número de la máquina de prueba.…”

Section: Revisión De La Literaturaunclassified

Development of a mobile application for the registration and attendance control of university students based on Machine Learning

Silva¹,

Samayani²,

Alejandro³

et al. 2022

Proceedings of the 2nd LACCEI International Multiconference on Entrepreneurship, Innovation and Regional Development (LEIRD 202

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This research aims to develop an Android mobile application that allows registration and control of attendance in educational institutions based on Machine Learning and Cloud Computing technology. For this, the project management methodology XP (EXtreme Programming) is used, in whose phases the entire process of implementation of the application and the launch are detailed; likewise, Firebase is used as a service for database management. The application tests were carried out on the students of the seventh semester of the Professional School of Systems Engineering of the University of Peru. The attendance control system was connected to a database that stores student information and attendance records. To implement the QR code attendance record functionality, the Firebase Machine Learning kit was used to Validate the student attendance record. In addition, the user interface displays the attendance records from an attractive, intuitive approach that is easy for teachers and students to manage. The results of the research show that the use of the application by teachers and students reduces and optimize the time invested in the attendance registration process compared to traditional methods, according to the satisfaction and acceptance criteria of the mobile application called "ASYS."

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Design of Campus Health Information System using Face Recognition and Body Temperature Detection

Pan

Weng

et al. 2021

2021 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf

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Decentralized Personal Cloud Data Model and its Application in Campus Health Information System

Cited by 5 publications

References 9 publications

Optimizing Attendance Management in Educational Institutions Through Mobile Technologies: A Machine Learning and Cloud Computing Approach

Optimizing Attendance Management in Educational Institutions Through Mobile Technologies: A Machine Learning and Cloud Computing Approach

Development of a mobile application for the registration and attendance control of university students based on Machine Learning

Design of Campus Health Information System using Face Recognition and Body Temperature Detection

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