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

Authors

  • Prasanna Kumar M Department of Electronics and Instrumentation Engineering, Dr. Ambedkar Institute of Technology Bengaluru, India
  • Likhith K Raj Department of Electronics and Instrumentation Engineering, Dr. Ambedkar Institute of Technology Bengaluru, India
  • Karthik M Department of Electronics and Instrumentation Engineering, Dr. Ambedkar Institute of Technology Bengaluru, India
  • Chandu Shivaputrappa Barker Department of Electronics and Instrumentation Engineering, Dr. Ambedkar Institute of Technology Bengaluru, India
  • Manoj R Department of Electronics and Instrumentation Engineering, Dr. Ambedkar Institute of Technology Bengaluru, India

Keywords:

RFID reader, RFID Tags, nodeMCU, Machine Learning

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).

 

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Published

2024-05-01

How to Cite

[1]
P. K. M, L. K. Raj, K. M, C. S. Barker, and M. R, “Enhancing Laboratory Experience: A Combination of RFID and Machine Learning Techniques to Track Attendance and Upgrade Hardware”, IJIRCST, vol. 12, no. 3, pp. 113–119, May 2024.

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