Dual-Activation Emergency Situation Notification System: A Feature Driven Development Approach
Bibliographic record
Abstract
Emergencies can unpredictably arise, posing severe threats to individuals' lives. Swift and effective communication plays a crucial role in promptly relaying vital information to relevant response agencies and loved ones, aiming to minimize or eliminate the impact of such disasters. Existing systems often require manual triggering of distress signals through button presses or image captures. However, these methods can be ineffective, particularly when victims are under the surveillance of wrongdoers, rendering them unable to discreetly use their devices. To address this issue, this study introduces the Dual-Activation Emergency Situation Notification System (DA-ESNS) model, which ensures that potential wrongdoers remain unaware of the user's attempt to seek help by employing an efficient communication and alerting system during emergencies. Through a review of previous models, the DA-ESNS model was developed to address identified gaps. The Feature Driven Development software development process model was selected due to its agile and incremental nature, enabling focused development and integration of the notification feature. ReactJS and NodeJS were utilized for frontend and backend development, respectively. The integration of Twilio API and Geolocation API facilitated SMS notifications and precise location sharing with emergency response teams and predefined contacts. The DA-ESNS model automatically relays distress signals by persistently listening for predefined keywords or manually triggered by clicking on corresponding emergency cards, notifying both the emergency response team and the victim's family members. The model was implemented across various mobile platforms, providing a centralized and interactive interface. By offering quick notifications and offline accessibility, the DA-ESNS model streamlines the emergency alert process, significantly improving the effectiveness of emergency response teams in reducing the impact of crises through rapid reaction times. As a result, the DA-ESNS model emerges as a valuable and effective tool in emergency management.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".