Artificial neural Network−Genetic algorithm modeling for moisture content prediction of savory leaves drying process in different drying conditions
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Bibliographic record
Abstract
In this study, the application of a versatile approach for modeling and prediction of the moisture content of dried savory leaves using hybrid artificial neural network-genetic algorithm has been presented. Genetic Algorithm was used in order to find the best Feed Forward Neural Network (FFNN) structure for modeling and estimation of moisture content in the drying process of savory leaves. The experiments were performed at three air temperatures of 40, 60 and 80 °C and at three levels of relative humidity 20%, 30% and 40% and air velocity of 1, 1.5 and 2.0 m/s for drying the savory leaves in the forced conductive dryer. Optimized neural network by GA had two hidden layers with 9 and 17 neurons in first and second hidden layers, respectively. Mean Square Error (MSE) value (0.000094606) and correlation coefficient (0.9992) of FFNN-GA experiments showed that moisture content can be accurately predicted from the input variables: air temperature, airflow velocity, relative humidity and drying time. Moreover, results showed that the optimized neural network topology could denote the superior ability of this intelligent model for on-line prediction of the moisture content of Savory leaves in different drying conditions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it