Blockchain and Machine Learning for Predictive Policing and Crime Pattern Analysis
Bibliographic record
Abstract
Crime pattern analysis and other forms of predictive policing are becoming indispensable tools for today's police forces. Hybrid Blockchain-Machine Learning Predictive Policing (HBL-PP) is a new method introduced by this study that aims to transform the sector by bringing together the best features of blockchain technology and machine learning algorithms. SecureCrimeChain guarantees the safe handling of crime-related data, while Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are used for advanced crime pattern analysis in HBL-PP. When compared to conventional approaches, HBL-PP performs much better in experimental evaluations. SecureCrimeChain guarantees the best degree of accuracy, precision, recall, and F1 score, outperforming other techniques. DeepCrimeNet has comparable performance and comes in a close second. FairPredict Pro, although fairness-aware, maintains a balance between equity and prediction accuracy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".