Leveraging transfer learning with deep learning for crime prediction
Crime remains a crucial concern regarding ensuring a safe and secure environment for the public. Numerous efforts have been made to predict crime, emphasizing the importance of employing deep learning approaches for precise predictions. However, sufficient crime data and resources for training state...
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| Format: | Article |
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Public Library of Science
2024
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| Online Access: | http://psasir.upm.edu.my/id/eprint/110550/ |
| _version_ | 1848865546298195968 |
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| author | Butt, Umair Muneer Letchmunan, Sukumar Hassan, Fadratul Hafinaz Koh, Tieng Wei |
| author_facet | Butt, Umair Muneer Letchmunan, Sukumar Hassan, Fadratul Hafinaz Koh, Tieng Wei |
| author_sort | Butt, Umair Muneer |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Crime remains a crucial concern regarding ensuring a safe and secure environment for the public. Numerous efforts have been made to predict crime, emphasizing the importance of employing deep learning approaches for precise predictions. However, sufficient crime data and resources for training state-of-the-art deep learning-based crime prediction systems pose a challenge. To address this issue, this study adopts the transfer learning paradigm. Moreover, this study fine-tunes state-of-the-art statistical and deep learning methods, including Simple Moving Averages (SMA), Weighted Moving Averages (WMA), Exponential Moving Averages (EMA), Long Short Term Memory (LSTM), Bi-directional Long Short Term Memory (BiLSTMs), and Convolutional Neural Networks and Long Short Term Memory (CNN-LSTM) for crime prediction. Primarily, this study proposed a BiLSTM based transfer learning architecture due to its high accuracy in predicting weekly and monthly crime trends. The transfer learning paradigm leverages the fine-tuned BiLSTM model to transfer crime knowledge from one neighbourhood to another. The proposed method is evaluated on Chicago, New York, and Lahore crime datasets. Experimental results demonstrate the superiority of transfer learning with BiLSTM, achieving low error values and reduced execution time. These prediction results can significantly enhance the efficiency of law enforcement agencies in controlling and preventing crime. |
| first_indexed | 2025-11-15T14:06:25Z |
| format | Article |
| id | upm-110550 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T14:06:25Z |
| publishDate | 2024 |
| publisher | Public Library of Science |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1105502024-06-17T08:36:52Z http://psasir.upm.edu.my/id/eprint/110550/ Leveraging transfer learning with deep learning for crime prediction Butt, Umair Muneer Letchmunan, Sukumar Hassan, Fadratul Hafinaz Koh, Tieng Wei Crime remains a crucial concern regarding ensuring a safe and secure environment for the public. Numerous efforts have been made to predict crime, emphasizing the importance of employing deep learning approaches for precise predictions. However, sufficient crime data and resources for training state-of-the-art deep learning-based crime prediction systems pose a challenge. To address this issue, this study adopts the transfer learning paradigm. Moreover, this study fine-tunes state-of-the-art statistical and deep learning methods, including Simple Moving Averages (SMA), Weighted Moving Averages (WMA), Exponential Moving Averages (EMA), Long Short Term Memory (LSTM), Bi-directional Long Short Term Memory (BiLSTMs), and Convolutional Neural Networks and Long Short Term Memory (CNN-LSTM) for crime prediction. Primarily, this study proposed a BiLSTM based transfer learning architecture due to its high accuracy in predicting weekly and monthly crime trends. The transfer learning paradigm leverages the fine-tuned BiLSTM model to transfer crime knowledge from one neighbourhood to another. The proposed method is evaluated on Chicago, New York, and Lahore crime datasets. Experimental results demonstrate the superiority of transfer learning with BiLSTM, achieving low error values and reduced execution time. These prediction results can significantly enhance the efficiency of law enforcement agencies in controlling and preventing crime. Public Library of Science 2024 Article PeerReviewed Butt, Umair Muneer and Letchmunan, Sukumar and Hassan, Fadratul Hafinaz and Koh, Tieng Wei (2024) Leveraging transfer learning with deep learning for crime prediction. PLoS One, 19 (4). art. no. e0296486. pp. 1-20. ISSN 1932-6203 https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0296486 10.1371/journal.pone.0296486 |
| spellingShingle | Butt, Umair Muneer Letchmunan, Sukumar Hassan, Fadratul Hafinaz Koh, Tieng Wei Leveraging transfer learning with deep learning for crime prediction |
| title | Leveraging transfer learning with deep learning for crime prediction |
| title_full | Leveraging transfer learning with deep learning for crime prediction |
| title_fullStr | Leveraging transfer learning with deep learning for crime prediction |
| title_full_unstemmed | Leveraging transfer learning with deep learning for crime prediction |
| title_short | Leveraging transfer learning with deep learning for crime prediction |
| title_sort | leveraging transfer learning with deep learning for crime prediction |
| url | http://psasir.upm.edu.my/id/eprint/110550/ http://psasir.upm.edu.my/id/eprint/110550/ http://psasir.upm.edu.my/id/eprint/110550/ |