PEMBUATAN APLIKASI SISTEM PAKAR UNTUK DIAGNOSA PENYAKIT MATA PADA MANUSIA BERBASIS WEB
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The eye is the organ which gives us the sense of vision, with eye human can recognize people, object, differentiating color and miscellaneous its. Eye is including to part of that sensitive body organ, hence from health of eye have to be taken care of and paid attention with as good as possible in order not to lessen eye performance. By exploiting growth of information technology in computer area hence made an "Making an Application of Expert System For Diagnose Eye Disease At Human Base on Web". This Application aim to provide information concerning types disease of eye at human completely like definition, symptom, medication and cause for the disease of eye. Besides, this application also provide a facility to diagnosed disease of eye. Method which is used in making of this application is method of forward chaining. While for the method of modelling system use flow map (schema emit a stream of document) and UML (Unified Modelling Language). Software which is used in making of this application is programming of PHP5, and MySQL as databases server. With existence of this expert system application expected can water down society in getting information concerning types disease of eye and also can assist society in doing inspection to disease of suffered eye.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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