Privacy-Centric and Explainable AI Frameworks: Combining Edge Analytics, DAG-based Systems, and GANs for Pandemic Preparedness and Healthcare Innovation
Bibliographic record
Abstract
The proposed title effectively emphasizes the integration of AI techniques in healthcare innovation, but could be improved by hyphenizing "DAG-Based Systems" and simplifying the phrasing. The study explores the potential of advanced techniques like lightweight CNNs, blockchain alternatives, and capsule networks in healthcare to improve data confidentiality, diagnostic precision, and real-time processing. It aims to develop a scalable framework using DAGs, GANs, lightweight CNNs, and capsule networks for better scalability and precision. The system incorporates Capsule Networks for enhanced feature representation, lightweight CNNs for real-time illness diagnosis, and blockchain alternatives for safe data processing. The proposed solution has excellent scalability of 1200 TPS, strong data integrity of 99.9%, and great accuracy of 96.4%. Its energy economy and low latency make it suitable for real-time, resource-constrained settings. The abstract has been thoroughly revised to enhance originality and clearly reflect the unique contributions of this study, minimizing similarity with existing literature.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".