A Deep Learning-Based Predictive Policing Model for Real-Time Crime Risk Assessment in Urban India
Bibliographic record
Abstract
The proposed solution to the increase in urban crime in India is to employ artificial intelligence to predict criminal behavior. As a result, this study presents a novel artificial intelligence-based system that uses a mix of machine learning, computer vision, and natural language processing to predict criminal activities. It uses a variety of data sources, including social media, socioeconomic factors, past crime reports, CCTV video, and others, to generate real-time forecasts of where crime is most likely to occur. The suggested solution, which merges CNN and RNN, improves the accuracy of predictions and boosts the techniques employed in law enforcement. These indicate that crime rates in high-risk regions have decreased by 15%, police response times have decreased by 35%, and the accuracy of predictions has decreased by 28%. Consequently, this technology has the ability to change the way that crime is prevented in cities across India by using a data-driven and proactive approach to policing. In addition, it will improve public safety and make better use of scarce resources.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".