IODnet: Indoor/Outdoor Telecommunication Signal Detection through Deep Neural Network
Bibliographic record
Abstract
In many Internet of Things (IoT) applications, understanding the device’s location can be highly significant for various reasons, including asset tracking and inventory management, geolocation services, safety and security, environmental monitoring, and proximity-based interactions. Mobile users frequently engage with mobile services/applications in both indoor and outdoor environments. Operators and other service providers can offer more suitable services to users by predicting their location accurately. Numerous attempts have been made to categorize user locations, but this paper introduces a methodology that enhances the accuracy of predicted values through Deep Neural Networks. Based on the proposed method, the accuracy of operator-side labels can be enhanced by comparing operator-side labeled datasets with real-world labeled drive-test collected datasets. The objective was to develop a precise model that rectifies uncertain labels in a dataset with more accurate labels, based on three datasets collected through crowd-sourcing and drive-testing approaches. Furthermore, the proposed method was benchmarked against state-of-the-art learning algorithms to demonstrate its superiority. Experimental results indicate that the F1-score metric can reach as high as 98% in certain datasets.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".