| Autor: |
Kumara, Banage, Kuhaneswaran, Banujan, Sandagiri, Chamith |
| Zdroj: |
International Journal of Systems & Service-Oriented Engineering (IJSSOE); January 2021, Vol. 11 Issue: 1 p15-30, 16p |
| Abstrakt: |
Crimes have affected the quality of life and economic growth of the country badly. The authors can identify the crime patterns and predict the crimes by detecting and analyzing the historical data. However, some crimes are unregistered and unsolved due to a lack of evidence. Thus, detecting crimes is a still challenging task. Individuals can use social media like Twitter to detect crime-related activities. Because Twitter users sometimes convey messages related to their surrounding environment, this paper proposed a machine learning approach to predict crimes. The proposed framework consists of three modules: data (tweet) collecting, detecting crimes, and predicting crime. Long short-term memory (LSTM) neural network model was used as a proposed approach for crime prediction. Experimental results found that by achieving the highest precision of 82.5%, precision of 86.4%, and recall of 80.4%, the proposed LSTM-based approach worked better than the other approaches. |
| Databáze: |
Supplemental Index |
| Externí odkaz: |
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