TRENDS AND PATTERNS IN ARTIFICIAL INTELLIGENCE RESEARCH AT ANNA UNIVERSITY: A SCOPUS-BASED SCIENTOMETRIC ANALYSIS
Bibliographic record
Abstract
Artificial Intelligence (AI) has emerged as a rapidly expanding and transformative research domain across multiple disciplines.This study presents a bibliometric analysis of AI research publications produced by Anna University from 1984 to March 2025.The study examines year-wise growth, source-wise distribution, prolific authors, subject areas, leading journals, institutional and country collaborations, and funding patterns.The findings reveal a significant exponential increase in AI research output, particularly after 2010, with the highest growth observed during the period 2020-2024.Journal articles constitute the dominant form of publication, reflecting strong engagement with peer-reviewed scholarly communication.Computer Science and Engineering remain the primary subject areas, while notable expansion into interdisciplinary domains such as medicine, social sciences, environmental studies, and business is evident.The analysis also highlights strong national and international collaborations and substantial support from global funding agencies.Overall, the study demonstrates the progressive development, multidisciplinary expansion, and growing global integration of AI research at Anna University, positioning the institution as a significant contributor to AI scholarship at both national and international levels.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.072 | 0.126 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".