Ranking Egyptian Universities Using Fuzzy Logic
Bibliographic record
Abstract
During the last quarter of a century, university ranking systems have been developed as an outcome of new academic fields and metrics. Although they have attracted wide attention from policy makers, students, academicians, authorities and the media, they have been subjected to criticism. Any university that does not change as with the environment around them is doomed to fail. The results of universities in international rankings are a disturbing and foreboding danger. Universities are seeking to improve its services, students, citizens and visitors, to achieve better results in the international rankings. The objective of this study is spread public awareness of the importance of ranking of universities and higher education institutions, and push these institutions towards excellence and competition in the quality of scientific research and academic performance, and spreading the culture of the orientation about the universities rankings and higher education institutions in Egypt. By using web service we have collected data on Egyptian Universities of interest from a more number of web Services for fifteen Indicator each Egyptian University, We have constructed the weight matrix for the Indicator, Where an algorithm was created to Ranking the Egyptian universities using the membership function (trapezoidal ) in fuzzy logic Has been applied the fuzzy logic algorithm on the that data, we have compared the results with some International rankings for example (QS,U.S Nwes, Webmatix).This study showed positive results about comparing the results we get with some of the global rankings, the study confirmed that the Egyptian universities have high capabilities in the field of scientific research.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".