Evaluating the Performance of a Multi-Organizational E-Government Platform on Hyperledger Fabric with Fuzzy Logic-Enhanced Multi-Channel Connectivity
Bibliographic record
Abstract
In the domain of e-government, the integration of multiple organizations is imperative for the effective delivery of public services through online platforms.This study examines a hyperledger fabric blockchain platform, which interconnects six organizations via a network of four distinct communication channels.A fuzzy logic-based mechanism has been employed to determine the optimal channel for executing transactions, contingent on client volume.A series of experiments were conducted to assess the system's performance in terms of throughput, latency, and scalability.These experiments varied parameters including transaction rates, block sizes, the number of participating organizations, and client count.It was observed that a multi-channel architecture significantly outperformed a singlechannel setup in enhancing throughput and reducing latency.The findings indicate that even with the expansion to six organizations and an increase in clients to two hundred, the deployment of a multi-channel structure only resulted in marginal throughput degradation and a modest latency increment when contrasted with a single-channel framework.Moreover, the strategic distribution of block sizes across the four channels was found to substantially bolster the scalability of the system.This facilitated the inclusion of additional clients and organizations with minimal impact on the overall system performance.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".