Aplicações da extensão de Zadeh na dinâmica do HIV com retardo fuzzy sob tratamento antirretroviral
Bibliographic record
Abstract
Mathematical models as applied to biological phenomena are usually involved with uncertainties. One way of determining solutions of such mathematical models, including their uncertainties, is by applying the Zadeh's extension principle, which is part of the fuzzy set theory. In this context, the general objective of this research is to present applications of the one-dimensional and two-dimensional Zadeh's extension principle on two mathematical models that simulate the dynamics of HIV antiretroviral treatment, with delay, in seropositive individuals. These applications are presented through fuzzy solutions of the proposed models of HIV dynamics, in which the delay and death rate of the virus are considered as fuzzy numbers. In the first model, the delay and the mortality rate of the virus are considered as fuzzy numbers correlated through an injective and monotonous function which facilitates the attainment of a fuzzy solution of the presented model, since it turns the two-dimensional Zadeh's extension principle into an one-dimensional type. The function that correlates the fuzzy parameters is estimated by two different methods, the first is Mamdani inference method and the second is Takagi-Sugeno inference method, which motivated a comparative study of the solutions in terms of the absolute error. In the other model, a numerical fuzzy solution is obtained through an algorithm developed in this work, considering in the model as triangular fuzzy numbers, the delay and the mortality rate of the virus that do not use a function that correlates them.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".