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Biometric and RFID Passive Tag based Student Identification System for Secure Attendance Management

2023· article· en· W4383109805 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIoT-based Smart Home Systems
Canadian institutionsHorizon College and Seminary
Fundersnot available
KeywordsAttendanceBiometricsComputer scienceFingerprint recognitionIdentification (biology)Fingerprint (computing)Computer securityGSMShort Message ServiceReliability (semiconductor)Service (business)MultimediaComputer networkBusiness

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.492
Threshold uncertainty score0.599

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.232
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations5
Published2023
Admission routes1
Has abstractyes

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