Transformer Model for Bomb Blast Prediction
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Abstract
Global terrorism has increased in the last 15 years, with bomb blast incidents accounting for the majority of the attacks. Bomb explosion strikes accounted for more than 45 percent of all terrorist activity worldwide from 1970 to 2019. Several experts believe that socioeconomic conditions of a country influence the level of terrorism. This characteristic is not accounted in previous machine learning models designed to forecast terrorism. Decision Trees, Random Forest, Nave Bayes (NB), K-Nearest Neighbour (KNN), Tree Induction (C4.5), Iterative Dichotomiser (ID3), and Support Vector Machine (SVM) have all been used in the past to create terrorist predicting models. These forecasts are deterministic, so the models’ outputs are best guesses, and no model can guarantee 100% accuracy due to unknown and unpredictable variables. The transformer model has been shown to perform effectively with considerably longer sequences and is capable of learning complex relationships between each piece of incoming data. This work offers a transformer-based time series forecasting technique that learns from socioeconomic data alongside bomb explosion incidents. We also use probabilistic forecasting to narrow down the options, which can prove to be valuable in averting future terrorist attacks.
How to Cite
Daneshyari, M., & Khera, S. (2022). Transformer Model for Bomb Blast Prediction. Research Review, 3(03), 781–788. https://doi.org/10.52845/RR/2022-3-3-4
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3. Bang, J., Basuchoudhary, A., David, J. and Mitra, A., 2018. Predicting terrorism: a machine learning approach.
4. Blomberg, S.B., Hess, G.D. and Weerapana, A., 2004. Economic conditions and terrorism. European Journal of Political Economy, 20(2), pp.463-478.
5. Huamaní, E.L., Alicia, A.M. and Roman-Gonzalez, A., 2020. Machine learning techniques to visualize and predict terrorist attacks worldwide using the global terrorism database. Machine Learning, 11(4).
6. Kalaiarasi, S., Mehta, A., Bordia, D. and Sanskar, 2019. Using global terrorism database (GTD) and machine learning algorithms to predict terrorism and threat. International Journal of Engineering and Advanced Technology, 9(1), pp.5995-6000.
7. Tolan, G.M. and Soliman, O.S., 2015. An experimental study of classification algorithms for terrorism prediction. International Journal of Knowledge Engineering, 1(2), pp.107-112.
8. Sachan, A. and Roy, D., 2012. TGPM: Terrorist group prediction model for counter terrorism. International Journal of Computer Applications, 44(10), pp.49-52.
9. Soliman, G.M. and Abou-El-Enien, T.H., 2019. Terrorism Prediction Using Artificial Neural Network. Rev. d’Intelligence Artif., 33(2), pp.81-87.
10. Ferooz, F., Hassan, M.T., Awan, M.J., Nobanee, H., Kamal, M., Yasin, A. and Zain, A.M., 2021. Suicide bomb attack identification and analytics through data mining techniques. Electronics, 10(19), p.2398.
11. Cozza, V. and Rubino, M., 2014. Detection of Similar Terrorist Events. In IIR (pp. 28-33).
12. Tench, S., Fry, H. and Gill, P., 2016. Spatio-temporal patterns of IED usage by the Provisional Irish Republican Army. European Journal of Applied Mathematics, 27(3), pp.377-402.
13. Edwards, D.S., Mcmenemy, L., Stapley, S.A., Patel, H.D.L. and Clasper, J.C., 2016. 40 years of terrorist bombings–a meta-analysis of the casualty and injury profile. Injury, 47(3), pp.646-652.
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19. Ozgul, F., Erdem, Z. and Aksoy, H., 2008, June. Comparing two models for terrorist group detection: Gdm or ogdm? In International Conference on Intelligence and Security Informatics (pp. 149-160). Springer, Berlin, Heidelberg.
20. Dixon, S.J., Dixon, M.B., Elliott, J., Guest, E. and Mullier, D.J., 2011, December. A neural network for counter-terrorism. In International Conference on Innovative Techniques and Applications of Artificial Intelligence (pp. 229-234). Springer, London.
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22. Saha, Snehanshu & Aladi, Harsha & Kurian, Abu & Basu, Aparna. (2017). Future Terrorist Attack Prediction using Machine Learning Techniques. 10.13140/RG.2.2.17157.96488.
23. [Meng, X., Nie, L. and Song, J., 2019. Big data-based prediction of terrorist attacks. Computers & Electrical Engineering, 77, pp.120-127.
24. Alammar, Jay.“The Illustrated Transformer.”
Jalammar.github.io,jalammar.github.io/illustrated-transformer/.
25. Vaswani, Ashish, et al. ‘Attention Is All You Need’. ArXiv: 1706.03762 [Cs], Dec. 2017. arXiv.org, http://arxiv.org/ abs/1706.
03762.
26. ‘Machine Learning Mastery’. Machine Learning Mastery, https://machinelearning
mastery.com/.
27. Onnen, Heiko. ‘Transformer Unleashed: Deep Forecasting of Multivariate Time Series in Python’. Medium, 20 Feb. 2022, https://towardsdatascience.com/transformer-unleashed-deep-forecasting-of-multivaria
te-time-series-in- python-9ca729dac019.
28. START (National Consortium for the Study of Terrorism and Responses to Terrorism). (2021). Global Terrorism Database (GTD) [Data set]. University of Maryland. https://www.start.umd.edu/gtd
29. START (National Consortium for the Study of Terrorism and Responses to Terrorism). (2021, August). Global Terrorism Database codebook: Methodology, inclusion criteria, and variables. University of Maryland. https://www.start.umd.edu/gtd/downloads/Codebook.pdf
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31. Human Development Reports | United Nations Development Programme. https://hdr.undp.org/
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33. Want Statistics? Ask Me Anything :) | Statsama. https://www.statsama.com/
34. UNdata. https://data.un.org/
35. Macrotrends | the Long Term Perspective on Markets. https://www.macrotrends.net.
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