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Record W4415360272 · doi:10.59934/jaiea.v5i1.1661

Design and Build a Vending Machine Prototype Using RFID Based on IoT

2025· article· W4415360272 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

VenueJournal of Artificial Intelligence and Engineering Applications (JAIEA) · 2025
Typearticle
Language
FieldEngineering
TopicWireless Sensor Networks and IoT
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsRadio-frequency identificationMicrocontrollerInternet of ThingsPaymentDatabase transactionStock (firearms)The Internet

Abstract

fetched live from OpenAlex

This research discusses the design and build of an Internet of Things (IoT)-based vending machine prototype with a payment method using Radio Frequency Identification (RFID). The system is designed using the ESP32 microcontroller connected to an RFID reader, DC motor, photointerrupter sensor, and RTC as the main controller. The Blynk application is used as a medium for monitoring drink stock and real-time notifications. Test results show that the prototype can perform cashless transactions with RFID automatically, dispense products according to user selection, and display stock data through the Blynk application. The application of RFID and IoT technology in vending machines has proven to improve operational efficiency, reduce cash transaction usage, and provide a more practical transaction experience. This research is expected to be a modern solution for digitally based automatic sales systems in the future.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.923
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.032
GPT teacher head0.275
Teacher spread0.244 · 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