Fingerprint-based biometric smart electronic voting machine using IoT and advanced interdisciplinary approaches
Bibliographic record
Abstract
India is a Democratic country with a huge population where voting plays an important role. Every citizen has the right to choose their leaders. This is done by using electronic voting machines (EVMs) at polling booths. But even there may be some malfunctions during elections. Under these circumstances holding elections is a complex task for the Election Commission because there is rigging taking place. Electronic voting systems have come into the picture to prevent rigging up to the maximum extent. For this, we are using the R307 Fingerprint Module which scans the fingerprint and gives input to Arduino Uno. Our developed algorithm stores the particular fingerprint in the storage drive and makes sure that the fingerprint is unique from the previously stored data. Thus, when the same person comes to poll his vote during the elections, he needs to give his fingerprint before polling his vote if his fingerprint is already present in stored data. If both the data are matched. The person can be eligible to pole his vote else the buzzer will give us the alert sound. The advanced technology will improve the “Biometric Voting System” through the fingerprint enrolment process making the authentication easy and enhancing security.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".