Neural Network Architecture Selection Using Particle Swarm Optimization Technique
Why this work is in the frame
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Bibliographic record
Abstract
Finding the best structure of ANN to minimize errors, the processing and the search time is one of the main objectives in the AI field. In order to achieve prediction with a high degree of accuracy in a short time, an enhanced PSO-based selection technique to determine the optimal configuration for the artificial neural network has been proposed in this paper. To design the neural network to minimize processing time, search time and maximize the accuracy of prediction, it is necessary to identify hyperparameter values with precision. PSO with 2-D search space has been employed to select the best hyperparameters in order to construct the best neural network where PSO is used as a decision-making model and ANN is used as a learning model. The suggested technique was used to select the optimal number of the hidden layer and the number of units per hidden layer. The proposed technique was evaluated using a chemical dataset. The result of testing the proposed technique displayed high prediction accuracy with MSE equal to 3.9% and the relative error between the expected output and actual target is less than 1.6%. The results of the comparison of the proposed technique with the ANN showed thatthe proposed approach could predict output with an infinitesimal error, outperforming the existing ANN model in terms of error ratio.
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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 | 0.001 |
| 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