Neural networks approach to biocomposites processing
Bibliographic record
Abstract
Biological neural networks mathematical counterpart artificial neural networks (or neural networks: NN) have contributed to the evolution of a distinct parallel information processing methodology for computational sciences. Problems such as biocomposites modeling or prediction are complicated to model with traditional statistical and mathematical tools due to the inherent noise in data. NN's efficient parallel processing capability for pattern recognition, forecasting, system analysis, controls and modeling can aid fast prediction, characterization and modeling of novel biocomposites, provided a good knowledge base is available. For the large knowledge base creation, samples with varying flax fiber (0%-35% with 5% interval) load are created with 2 different operating pressures 1 psi and 1.6 psi (variable operating parameters) to produce compression molded biocomposite boards. These boards go through destructive sampling process to contribute to tensile, impact, hardness, flexural and density data. Using this data a number of neural networks using Matlab®were evaluated to find the optimal neural network architecture. The multilayer feed forward with backpropagation learning (FFBPNN, L1: 10, L2:10, L3: 2) provided best results. It was then further trained with 5 separate training algorithms. Finally the FFBPNN trained with TRAINLM was selected to generate prediction results that were optimal. The trained NN is capable of providing required composition and pressure based on desired mechanical property.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".