A Novel System for Potential Mosquito Breeding Hotspot Intimation and Monitoring Using MLOps and Improved YoloV3
Bibliographic record
Abstract
Vector-borne disease control is an important issue faced by mankind.Many existing systems perform the detection and prevention of mosquito breeding sites using UAVbased methods.However, they don't provide real-time monitoring and detection of the same on daily basis.This study proposes a cloud-based deep-learning system to control the disease spread at a high scale.The implemented system does continuous monitoring using the existing public cameras for the potential mosquito breeding hotspots, further, the corresponding location coordinates will be forwarded to the local authorities.A history of the location coordinates maintained at the remote server will help monitor hotspots.We evaluated the current approach results and discovered that by layer pruning, the accuracy is improved by 14% and further reduces execution time by 10 sec.For accuracy and execution time calculation, the pruned model was tested on the validation dataset, and then the comparison was done with the original deep learning model.This indicates that the system can accurately detect the number of potential mosquito breeding sites.These results are expected to support decision-making on rapid resource allocation for vector control actions on a regular basis by achieving the sustainability goal of UNSDG (3).
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".