Mobile Application of Drug Follow-up Information System with Data Matrix Reader
Bibliographic record
Abstract
The number of products that simplify people's lives are increasing with the enormous development of the technology.Mobile devices have a great importance for the provision of communication which is one of the most significant need of human beings.Mobile devices have gone beyond to be used originally as a mobile phone purposes and they have begun to be used as a smartphone by taking in charge of computers.They are not only used for communication but also they are used like camera, photo camera, notebook, television and reminder.Google's Android platform is a widely anticipated open source operating system for mobile phones.Google's Android Operating System (AOS) in mobile phones are still relatively new, however, AOS has been progressing quite rapidly.The increasing number of smartphone users has prepared the ground for the emergence of new ideas to make life easier.Recently, especially some applications in health sector have reflected one of the most important samples.Some of the mobile applications in this field used by humans are about hearing test, vision test, diabetes, pregnancy, and doctor appointment.This paper focuses on following of drugs, taken by patients, through mobile phones.The application running on the AOS provides the use of drugs on time with the alarm system.In addition to this, the application gives information (time, dosage, and name) about drugs by reading data matrix located on the medicine box.Thanks to visual and understandable interface and easy usage, many difficulties experienced in drug intake can be eliminated with this application.Finally, the percentage of drug intake on time can be increased in the future.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.101 | 0.059 |
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".