Enhanced Autism Spectrum Disorder Facial Expression Recognition Using Hybrid Weighed Quantum Particle Swarm Optimization with Fast Mask Recurrent Convolutional Neural Network
Bibliographic record
Abstract
Autism Spectrum Disorder (ASD) affects brain development, impacting socialization, communication, and creativity in children.Signs typically appear within the first three years, with many children struggling with language acquisition, affecting their learning abilities.Various treatments help manage behaviors, benefiting both children and their parents.The mechanisms by which visual information about facial expressions translates into emotional categories are not well understood.This study proposes a system-level explanation through predictive processing theory.An innovative method combining Fast Mask Recurrent Convolutional Neural Network (FMRCNN) and hybrid Weighed Quantum Particle Swarm Optimization (WQPSO) aims to improve recognition of abnormal facial movements in individuals with ASD.FMRCNN captures temporal relationships and extracts features from input data, while the fast mask mechanism enhances network speed and efficiency by focusing on relevant input regions.The proposed method leverages predictive processing to improve accuracy and efficiency in facial expression recognition.It can identify six emotions: anger, fear, joy, sadness, surprise, and disgust.Results show significant potential in supporting ASD-related challenges, achieving 99.8% accuracy, 99.8% precision, 100% recall, and 94% specificity, surpassing existing systems in ASD facial expression recognition.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".