Estimating the deposition efficiency of micro-particles in human upper airway using computerized tomography imaging and bio-inspired evolvable extreme learning machine interpolator
Bibliographic record
Abstract
In this investigation, an intelligent technique is used to analyze the deposition efficiency (DE) of micro-particles in a realistic human upper airway model. To do so, firstly a numerical framework including computerized tomography (CT) imaging, geometry production software and mesh generation tool is utilized to provide a bed for simulating the human airway. Thereafter, the simulated airway is exposed to computational fluid-particle dynamics (CFPD) system to study the complete upper trachea-bronchial airway from trachea (G0) to second generation of bifurcations (G2). At the numerical phase, low Reynolds number (LRN) k-ω turbulence model is considered to simulate the laminar to turbulent occurred airflow. At the final stage, the obtained knowledge is used to provide a continuous model suited for analyzing the DE value in different sections of human upper airway. Here, the authors utilize a fast yet accurate intelligent interpolator called extreme learning machine (ELM) neural network to capture the knowledge of the obtained database. Bio-inspired metaheuristics are also used to evolve the architecture of ELM neural network such that it can predict the DE values with a high degree of accuracy and robustness. The results of the conducted experiments indicate that the proposed bio-inspired ELM interpolator is capable of providing a fast and accurate model which can yield proper information in a very short period of time as compared to the existing numerical techniques such as CFD. On the other hand, by using different types of bio-inspired metaheuristics, i.e. mutable smart bee algorithm (MSBA), particle swarm optimization (PSO), firefly algorithm (FA) and scale-factor local search differential evolution (SFLSDE), it is observed that the structure of ELM neural network can be easily trained using metaheuristics. In general, the experimental results demonstrate the applicability and efficacy of soft computing techniques for predicting the DE values in human upper airway.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".