IoT Based Women Safety Devices with Screaming Detection and Photo Capturing
Bibliographic record
Abstract
Women's safety is a pressing global issue, with increasing incidents of harassment and violence. Traditional safety tools, like personal alarms or manual panic buttons, often rely on user activation, which can be impractical in critical moments. This project seeks to overcome these challenges by creating an IoT-enabled wearable safety device that autonomously detects distress signals through screams, captures video evidence, and sends real-time alerts, including the user's location, to emergency contacts or local authorities. The device employs machine learning algorithms to differentiate between genuine distress sounds and background noise, ensuring accurate detection and reducing false alarms. Additional features include GPS tracking, automatic activation, and a user-friendly design, making the device practical and efficient in real-life situations. Data security and power management are key components of the system, with encryption safeguarding personal information and power-efficient components ensuring long-term functionality. This solution aims to improve women's safety by providing an intelligent, hands-free personal security option. By combining advanced IoT technologies with real-time communication capabilities, this project offers a robust, reliable, and proactive method to prevent harm, empower women, and enhance safety in public environments.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| 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".