Voice AI IoT Model for Rescue Operations Among First Responders for Disaster Management
Bibliographic record
Abstract
While few studies exist in the use of Internet of Things (IoT) in indoor disaster management scenarios, there is a significant absence of research and scalable products in voice command assistant based IoT solutions for public safety and emergency responder situations like mass transit accident, floods, active shooter, and other incidents where just-in-time response and immediate aid are essential. Public safety is the cornerstone of our research study. Social networks drive consumer adoption of IoT technology because users seek feedback from peers, family, and social media influencers to lessen IoT product or service uncertainty. Prior research has demonstrated that customer trust affects IoT adoption. With the advance in Industrial IoT (IIOT) in disaster management, we have solutioned Zenext-IoT (ZIOT) which runs on a core voice command technology for public safety personnel. The goal of this research is to evaluate the degree to which first responders are willing to use the Internet of Things-based disaster management framework that has been proposed. This research aims to solve an urgent and imminent solution of providing the target segment of public safety officials and rescue worker with hands-free voice command virtual assistant IoT model. Our target users are emergency responders and rescue workers for whom technology needs to be simple to use and just-in-time.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".