Training and teaching applications for Autistic Children based on C# standalone application
Bibliographic record
Abstract
Autism is a disease, which affects the child's ability to communicate with those around them and develop mutual relations with them and hence it needs to have a quick and efficient treatment technique. The earliest age of ASD diagnosis is between 4.5 and 5.5 years. Approximately 6 per 1000 children under eight years suffer from ASD. Statistics show an annual increase of the disease about 3,500 cases of children with autism in the Sultanate of Oman. The aim of this work is to design an Interactive Learning System based Windows Application for teaching the children with Autism. The Windows Application is developed using C# which will be useful to teach the children different things such as Alphabets, Numbers, Fruits, Vegetables and many more. The application is a bi-lingual application (English and Arabic). The application can be used also to test the progress of the children. Also, develop interactive materials that help children with special needs for enhancing their communicating and thinking. In addition develop interactive materials that help children with special needs for enhancing their communication and rational skills which could help them to integrate into the society.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.010 |
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".