Development and Implementation of an RFID-Enabled Automatic Rice Vending System Using Arduino Mega 2560
Bibliographic record
Abstract
A technology known as radio frequency identification (RFID) makes it possible to automatically and connectionless identify items using radio frequency.The development of the RFID system, the production of the prototype, and the integration of the system with the rice sales prototype are the many steps of this research.The purpose of this research is to propose the creation and development of an automated rice sales prototype with RFID capabilities, which offers a customization feature through a keypad to select the desired amount of rice.To enhance customer convenience in understanding the amount of rice and total cost, a Liquid Crystal Display (LCD) is integrated into the hardware interface of the rice selling prototype.RFID reader modules and RFID cards are used as a secure payment method.The utilization of this RFID system allows for a faster and more secure transaction process.The results of this research include the development of an automated rice sales tool prototype that successfully uses RFID technology to identify buyers.Performance evaluation of the prototype showed efficiency in the sales process and enhanced transaction security.The successful integration of RFID enables quick identification of buyers, increases sales productivity, and provides a more convenient purchasing experience.These findings confirm the potential of RFID in improving automation and security in rice selling systems.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".