Multiple Fault Detection and Smart Monitoring System Based on Machine Learning Classifiers for Infant Incubators Using Raspberry Pi 4
Bibliographic record
Abstract
The most significant and sensitive component of the biomedical field is the care of newborn newborns. Because of their gestational age or birth weight, certain newborn newborns are at a higher risk of death. Because of their unmet demand for warmth, the majority of preterm newborns born between 32 and 37 weeks of gestation die. The neonatal incubator is a device that provides a regulated and closed environment to preterm newborns. In this paper and depending on technological progress, an intelligent system has been designed to monitor the performance of the incubator sensors depending on four features (temperature, humidity, fan current, and heater current) to detect any fault in the system. The intelligent system employs a low-power computing device to detect the fault in sensors, like the Raspberry Pi 4, which delivers the data from the incubator’s sensors. For classification, tasks adopted many algorithms like Decision Tree (DT), Support Vector Machine (SVM), and Neural Network (NN), then send alerting messages (e-mail, text message) to the nurse or doctor via Wi-Fi. The promising results of the proposed method accuracy come as 98% of DT and SVM while 97.3% of NN.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".