Application of IoT Technology for Automatic Provisioning Systems in Freshwater Fish Farming at STMIK Kaputama
Bibliographic record
Abstract
The development of secure vault systems is essential in addressing limitations found in traditional security mechanisms, such as physical keys and numerical combinations. This study presents the design and implementation of an Internet of Things (IoT)-based vault security system that integrates dual biometric authentication, facial recognition using ESP32-CAM, and fingerprint verification using the AS608 sensor. The system enforces a sequential authentication process, ensuring the facial scan is verified before activating the fingerprint module. Successful dual authentication triggers a solenoid lock to grant access, enhances security, and prevents unauthorized entry. The system communicates with Firebase for real-time status updates and access logs, which can be monitored through an Android application. Hardware and software testing confirmed the effectiveness of component integration, biometric reliability, and real-time connectivity. The device also operates effectively on battery power, increasing portability. The results demonstrate that dual biometric verification significantly increases vault security while providing a practical and efficient user experience.
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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".