Cloud based virtualization for a calorie measurement e-health mobile application
Bibliographic record
Abstract
Smartphones have transformed people approach towards technology. It not only empowers them to use it for communication but also for tracking and maintaining health-fitness via mobile applications. With the increasing number of complex mobile applications, the mobile, with its limited resources (in terms of the computational power and storage capacity) cannot efficiently run these applications autonomously. Similarly our e-health mobile application (Eat Healthy Stay Healthy) requires a platform to run highly computational intensive algorithms like the deep learning (for recognizing food images) and calorie measurement would need higher processing power to perform optimally. We propose a cloud based virtualization model that provides our e-health application with the required computational power that it needs to perform efficiently and at the same time would also give it the flexibility to make use of the various cloud resources. Our model comprises of concepts like virtual swap between various mobile sessions that assist the system for faster processing and intelligent decision mechanism for distributing the task of image processing to cloud servers. By implementing intelligent decision mechanism, the final calorie computation significantly improved by 20.5% while implementing deep learning in cloud.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".