Development of Aczel-Alsina Aggregation Operators in Neutrosophic Cubic Sets for Multi-Expert and Multi-Criteria Weighting: Optimizing Alternative Fuel Technology Selection
Bibliographic record
Abstract
Managing vague and uncertain data has long been a challenge in decision-making (DM), particularly in scenarios where criteria and expert assessments play a critical role. This paper introduces operational laws based on Aczel-Alsina (AA) norms within Neutrosophic Cubic Sets (NCS) to more effectively handle uncertainty. Leveraging these operational laws, we propose two aggregation operators: the Neutrosophic Cubic Aczel-Alsina Weighted Averaging (NCAAWA) and the Neutrosophic Cubic Aczel-Alsina Weighted Geometric (NCAAWG) operators. These provide a comprehensive approach to data aggregation, preserving both additive and multiplicative influences on outcomes in complex systems. In DM, the importance of weights is paramount, and we introduce a novel method for determining weights based on a model value that represents the entire dataset. This model value serves as a pivotal measure for deriving weights, reflecting the relevance and influence of each input from expert and criteria matrices. The use of model value enhances the intuitiveness and accuracy of the aggregated results. To demonstrate the utility of this approach, we propose a DM method that integrates NCAAWA and NCAAWG with the Weighted Aggregated Sum and Product Assessment Selection (WASPAS) model, allowing for the simultaneous consideration of both additive and multiplicative criteria. This dual approach provides a more comprehensive evaluation of alternatives by accounting for the cumulative and interactive contributions of criteria. The proposed DM method is applied to the evaluation of alternative fuel technologies (AFT), a complex area involving multi-expert criteria that span economic, social, environmental, and technical aspects. The framework offers a balanced and thorough assessment, accommodating the interdependencies and uncertainties inherent in the criteria involved.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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".