Summary of Deep Learning Based Crime Prediction Models: Experiments and Analysis, by Rittik Basak Utsha et al.
Deep Learning Based Crime Prediction Models: Experiments and Analysis
by Rittik Basak Utsha, Muhtasim Noor Alif, Yeasir Rayhan, Tanzima Hashem, Mohammad Eunus Ali
First submitted to arxiv on: 27 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper investigates various deep learning-based methods for predicting crimes and compares their performance in real-life scenarios. The authors review existing research in this area, highlighting the strengths and weaknesses of different approaches. Their comprehensive experimental evaluation reveals key insights into the pros and cons of each model, allowing them to recommend suitable models for specific applications. By considering design practices for future crime prediction models, the study aims to improve the effectiveness of these systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to predict crimes using computer programs. Right now, some of these programs use special techniques called deep learning to make predictions. These programs can be really good at predicting when and where crimes might happen. But nobody has looked at all of these different programs together to see which ones work best in real-life situations. So, the researchers did just that – they tested many different crime-predicting programs and compared their results. They found out what makes each program good or bad, and then suggested which programs would be best for certain situations. By learning from this study, we can make even better programs to help keep people safe. |
Keywords
» Artificial intelligence » Deep learning
