A Scalable and Secure Data Analytics Framework for Decentralized Autonomous Healthcare Systems using Fuzzy Logic, Blockchain Sharding, Dynamic Network Slicing, and ECC
Bibliographic record
Abstract
Healthcare systems are increasingly challenged by the complexity of managing scalable and secure data. Addressing this, the proposed novel framework integrates elliptic curve cryptography (ECC), dynamic network slicing, blockchain sharding, and fuzzy logic to enable efficient, adaptive, and secure data analytics. The framework processes healthcare data from IoT devices, wearable sensors, and patient records, ensuring real-time analytics, scalability, and robust security. By utilizing blockchain sharding for scalability, ECC for secure encryption, and fuzzy logic for decision-making, the architecture effectively overcomes the constraints of traditional systems. The results demonstrate significant improvements, including enhanced security (98%), reduced latency (10 ms), and higher scalability (5000 TPS). These advancements establish a reliable, decentralized foundation for predictive healthcare insights, resource optimization, and adaptive governance, setting a benchmark for modern healthcare data management systems.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".