Enhancing Laboratory Experience: A Combination of RFID and Machine Learning Techniques to Track Attendance and Upgrade Hardware

Prasanna Kumar M; Likhith K Raj; Karthik M; Chandu Shivaputrappa Barker; Manoj R

doi:10.55524/ijircst.2024.12.3.18

Abstract

Practical subjects have grown in importance in student lives over time. Certain components may malfunction or not be suitable for the experiment being conducted. The RFID system facilitates the tracking of both student attendance and experiment details, which are saved on a MySQL server and shown inside a PHP environment. After being collected by means of an RFID reader, RFID tags, and a nodeMCU, the data has been applied to artificial understanding that identifies usage and then notifies the service. Based on mean square error (MSE) value, we have constructed three models: gradient boosting (1.00), random forest (0.5), and linear regression (0.14).

Keywords

RFID reader, RFID Tags, nodeMCU, Machine Learning

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Cites this article as

P. K. M, L. K. Raj, K. M, C. S. Barker, M. R, "Enhancing Laboratory Experience: A Combination of RFID and Machine Learning Techniques to Track Attendance and Upgrade Hardware", International Journal of Innovative Research in Computer Science and Technology (IJIRCST), Vol-12, no.3, pp.113-119, 2024. Available from: https://doi.org/10.55524/ijircst.2024.12.3.18

Corresponding Author

Prasanna Kumar M

Department of Electronics and Instrumentation Engineering, Dr. Ambedkar Institute of Technology Bengaluru, India

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