Smarter Secure Surveillance: Leveraging AI, IoT, and Cryptographic Techniques for Enhanced Public Safety
Bibliographic record
Abstract
The Internet of Things (IoT) has significantly enhanced human security through various smart devices and wearables.Despite minor drawbacks, such as vibrations caused by these devices, their benefits far outweigh the challenges.The Internet of Things (IoT) plays a crucial role in the development of smart cities and smart homes, particularly in enhancing public safety and security.By integrating IoT with deep learning, real-time threat detection can be significantly enhanced.This research addresses a critical challenge in IoT-based security systems: ensuring secure data communication from its point of generation to analysis and final consumption.The proposed system introduces a novel dataset-driven approach that leverages deep learning for real-time detection of potential threats, such as individuals carrying sharp objects or those attempting to conceal their identity with face coverings.By combining IoT with digital image processing and cryptographic techniques, the system ensures secure data transmission while promptly alerting authorized personnel to potential security threats.This work not only strengthens public safety but also mitigates risks associated with malicious activities, offering a robust and intelligent IoT-based security framework.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".