Artificial Intelligence-Based Classification of Trusted and Untrusted Sensor Nodes in WBAN Using Multi Layered Stacked Naïve Bayes Method for Resilient Infrastructure
Bibliographic record
Abstract
WBAN Magnetic Sensor Nodes can be classified based on artificial intelligence using the Multi-Layered Stacked Naï ve Bayes Method for Resilient Infrastructure.As wireless body area networks (WBANs) hold considerable potential for monitoring, identifying, forecasting, and diagnosing disease in humans, this study is significant for the healthcare industry.WBAN data can be inaccurate and unreliable when collected by untrusted sensor nodes, leading to inaccurate diagnoses and treatments.WBAN networks can be improved by identifying untrusted sensor nodes in this study to address this issue.Sensor nodes are categorized using the MLSNB method based on their trust aspects.When compared to other methods currently in use, MLSNB performs better.It is possible, using the proposed methodology, to introduce high-quality, affordable, and easily accessible healthcare systems to the world's growing population, in particular to the elderly and persons living with old-age diseases.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".