Biometric and RFID Passive Tag based Student Identification System for Secure Attendance Management
Bibliographic record
Abstract
This paper proposes a biometric and RFID passive tag-based student identification system for secure attendance management. The system utilizes the unique biometric features of each student, such as fingerprint, along with an RFID tag embedded in the student ID card to authenticate and track student attendance. The system is designed to overcome the limitations of traditional attendance management systems, such as manual entry errors, proxy attendance, and low accuracy. The proposed system provides an automated and secure attendance management solution that ensures accurate attendance recording and eliminates the possibility of fraudulent activities. A daily brief message service (SMS) delivered by a GSM (Global System for Mobile) module, notifying the guardian as to whether the individual has arrived at the institution. There will be a web application where students and instructors will view a student's current attendance and location on campus. The system has been implemented and tested in a real-world educational setting, and the results demonstrate its efficiency and reliability in managing student attendance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".