A hybrid model based on fuzzy VIKOR and the classical optimal search to detect illegal chemical warehouses
Bibliographic record
Abstract
The use of chemical weapons has turned into an increasing risk for the world. In this study, a hybrid approach on the basis of fuzzy VIKOR and the optimal search model to cope with an important case study called the detection of illegal chemical warehouses is introduced. It is obvious that such illegal activities are accomplished under high secret considerations. Therefore, we have several types of ambiguity and uncertainty in this problem. First, fuzzy VIKOR is used to prioritize the suspicious warehouses based on time and cost of a search under fuzzy environment. Also the probability of existence of chemical agents in each warehouse (Pi) and the probability of detection (αi) in case materials exist in warehouse, are estimated. Next, the output of VIKOR i.e., Q is assumed as an input of optimal search model and optimal strategy for searching is achieved by solving a stochastic dynamic programming. According to this hybrid approach, we start from the location that has the maximum value of αiPiQi. Although we get benefits of fuzzy logic, VIKOR, and classical optimal search, the suggested method is easy to understand and straightforward to utilize in real-world problems. Also this model can enhance the robust nature of hybrid approach and reduces its sensitivity to the change of weights.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".