Interpretable machine learning-based terrorist attack success rate prediction-Reference-Cited by-同舟云学术

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Interpretable machine learning-based terrorist attack success rate prediction

Published:2022-12-18 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)
language:
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Author:

Luo Lanjun¹,Li Boxiao²,Wang Xueyan³,Cui Lei⁴,Liu Gang²

Affiliation:

1. School of Management North Sichuan Medical College,Nanehong,China

2. School of Management Huazhong University of Science and Technology,Wuhan,China

3. Information Centre Affiliated Hospital of North Sichuan Medical College,Nanchong,China

4. School of Artificial Intelligence and Automation Huazhong University of Science and Technology,Wuhan,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10089231/10089220/10089352.pdf?arnumber=10089352

Reference28 articles.

1. Predicting the Next Location: A Recurrent Model with Spatial and Temporal Contexts

2. Simulating the Linkages Between Economy and Armed Conflict in India With a Long Short‐Term Memory Algorithm

3. Big data-based prediction of terrorist attacks

4. Use of Predictive Modeling for Prediction of Future Terrorist Attacks in Pakistan

5. Wavelet neural networks for nonlinear time series analysis;minu;Applied Mathematical Sciences,2010

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Fostering Global Stability: Machine Learning for Anticipating Terrorism;2023 International Conference on Integrated Intelligence and Communication Systems (ICIICS);2023-11-24

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