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| Autori principali: | , , , |
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| Natura: | Recurso digital |
| Lingua: | |
| Pubblicazione: |
Zenodo
2024
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| Soggetti: | |
| Accesso online: | https://doi.org/10.5281/zenodo.18145737 |
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Sommario:
- Every nation in the planet is extremely concerned about the rise in violent crime. Crime forecasting is one of the many crime analysis techniques that have been used to lower the frequency of violent crimes. Since it helps law enforcement agencies plan successful crime prevention measures, crime forecasting is a useful tool. It has been noted recently that researchers are favouring the use of artificial intelligence (AI) approaches in crime predicting and analysis. This development serves as the impetus for this study, which compares the effectiveness of three artificial intelligence (AI) techniques—neural network (ANN), support vector regression (SVR), and gradient tree boosting (GTB)—in forecasting the rates of four different categories of crimes in the US. Quantitative error measurement was used to compare each AI technique's forecasting ability. Based on the acquired data, GTB outperformed ANN and SVR in terms of forecast accuracy, with the fewest observed error readings.