IoT-Powered IPMS and AIPRA Revolutionize Healthcare With AI-Driven Pandemic Detection, Resource Optimization, Remote Monitoring, and Global Health
Bibliographic record
Abstract
Background Information: Pandemics severely challenge global health care systems. IPMS and AIPRA through IoT, AI, and blockchain are capable of real-time detection, resource optimization, and secured data exchange. Objectives: This paper aims at enhancing preparedness in pandemics and optimization in the usage of resources, allowing proactive surveillance, and application of IoT and AI-driven sustainable solutions to address concerns of scalability, interoperability, and privacy issues. Methods: Resource management, data-driven pandemic prediction, remote monitoring, and global health operations using AI, machine learning, adaptive algorithms, and IoT by IPMS and AIPRA. Results: The hybrid system achieves 85 ms latency, 92.3% resource usage, and 96.5% detection accuracy. Improved features enhance privacy protection (0.95) and scalability (0.88). Conclusion: The system will ensure safe, scalable, and cooperative control over pandemics; enhanced patient care; maximum use of resources; and effective handling of future international health emergencies.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.019 |
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".