Scientometric Exploration of Fuzzy Research in Saudi Arabia
Bibliographic record
Abstract
This bibliometric research investigates the development, productivity, and academic influence of fuzzy research from 1981 to 2024 in Saudi Arabia. We retrieved the bibliometric data from the Scopus database and analyzed 5,719 publications, leading to 111,381 citations. The metric analysis shows that Mohammad A. Abido is leading the country with the highest number of publications. At the same time, King Abdulaziz University and King Saud University are the most productive institutes in fuzzy research. Journals such as the IEEE Access and MDPI are leading quite often as the publishing venue, and a trend of publication towards high-impact journals. International collaborations with Pakistan, India, China, and Canada significantly impacted the research productivity. The visual analysis was done using VOS viewer and Bibliometrix software, which includes co-citation, bibliographic coupling, co-occurrence, word cloud mapping, and emerging or declining thematic maps. These evaluations illustrate strong interdisciplinary ties of literature, while top research topics and trends involve artificial intelligence, optimization, decision making, and sustainability. The current direction is to increase the application of fuzzy logic in the energy, health, and environmental sciences. More generally, this study highlights the trends and themes of Saudi Arabia in the world of fuzzy set theory and its applications, facilitated by institutional backing, inter-institutional collaboration, and increasing demands for cross-disciplinary 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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.091 | 0.127 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".