Dynamic interval-valued intuitionistic normal fuzzy aggregation operators and their applications to multi-attribute decision-making
Bibliographic record
Abstract
For multi-attribute decision-making (MADM) problems with temporal characteristics and attribute values of interval-valued intuitionistic normal fuzzy numbers, dynamic interval-valued intuitionistic normal fuzzy weighted averaging (DIINFWA) operators are presented, and their properties are proved. Since attribute weights and time weights have both been unknown in MADM problems, we propose a dynamic interval-valued intuitionistic normal fuzzy MADM method. In this method, a combination weighting method of gray correlation analysis and the maximum deviation method are used to solve for attribute weights, comprehensively considering the subjective experience of decision-makers and objectives of decision data; time weights are decomposed into time-constant and time-variable weight vectors. We determine time weights using the time function, combining information entropy and a logistic function. According to the algorithm of interval-valued intuitionistic normal fuzzy numbers, decision-making information in different time sequences are aggregated using the proposed DIINFWA operators. We construct a dynamic interval-valued intuitionistic normal fuzzy comprehensive decision matrix and use the VIKOR (Vlsekriterijumska Optimizacija I Kompromisno Resenje) method to obtain the optimal solution. Finally, the feasibility and significance of the presented method compared to existing methods are verified through analysis of numerical examples.
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 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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".