Prediction of Future Terrorist Activities Using Deep Neural Networks
One of the most important threats to today’s civilization is terrorism. Terrorism not only disturbs the law and order situations in a society but also affects the quality of lives of humans and makes them suppressed physically and emotionally and deprives them of enjoying life. The more the civiliza...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/1373087 |
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| author | M. Irfan Uddin Nazir Zada Furqan Aziz Yousaf Saeed Asim Zeb Syed Atif Ali Shah Mahmoud Ahmad Al-Khasawneh Marwan Mahmoud |
| author_facet | M. Irfan Uddin Nazir Zada Furqan Aziz Yousaf Saeed Asim Zeb Syed Atif Ali Shah Mahmoud Ahmad Al-Khasawneh Marwan Mahmoud |
| author_sort | M. Irfan Uddin |
| collection | DOAJ |
| description | One of the most important threats to today’s civilization is terrorism. Terrorism not only disturbs the law and order situations in a society but also affects the quality of lives of humans and makes them suppressed physically and emotionally and deprives them of enjoying life. The more the civilizations have advanced, the more the people are working towards exploring different mechanisms to protect the mankind from terrorism. Different techniques have been used as counterterrorism to protect the lives of individuals in society and to improve the quality of life in general. Machine learning methods have been recently explored to develop techniques for counterterrorism based on artificial intelligence (AI). Since deep learning has recently gained more popularity in machine learning domain, in this paper, these techniques are explored to understand the behavior of terrorist activities. Five different models based on deep neural network (DNN) are created to understand the behavior of terrorist activities such as is the attack going to be successful or not? Or whether the attack is going to be suicide or not? Or what type of weapon is going to be used in the attack? Or what type of attack is going to be carried out? Or what region is going to be attacked? The models are implemented in single-layer neural network (NN), five-layer DNN, and three traditional machine learning algorithms, i.e., logistic regression, SVM, and Naïve Bayes. The performance of the DNN is compared with NN and the three machine learning algorithms, and it is demonstrated that the performance in DNN is more than 95% in terms of accuracy, precision, recall, and F1-Score, while ANN and traditional machine learning algorithms have achieved a maximum of 83% accuracy. This concludes that DNN is a suitable model to be used for predicting the behavior of terrorist activities. Our experiments also demonstrate that the dataset for terrorist activities is big data; therefore, a DNN is a suitable model to process big data and understand the underlying patterns in the dataset. |
| format | Article |
| id | doaj-art-a7a1c0dbaded4c23b892e22bd7ee522a |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-a7a1c0dbaded4c23b892e22bd7ee522a2025-02-03T01:04:40ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/13730871373087Prediction of Future Terrorist Activities Using Deep Neural NetworksM. Irfan Uddin0Nazir Zada1Furqan Aziz2Yousaf Saeed3Asim Zeb4Syed Atif Ali Shah5Mahmoud Ahmad Al-Khasawneh6Marwan Mahmoud7Institute of Computing, Kohat University of Science and Technology, Kohat, PakistanCenter for Excellence in IT, Institute of Management Sciences, Peshawar, PakistanCenter for Excellence in IT, Institute of Management Sciences, Peshawar, PakistanDepartment of Information Technology, University of Haripur, Haripur, PakistanDepartment of Information Technology, Abbotabad University of Science and Technology, Havelian, PakistanFaculty of Engineering and Information Technology, Northern University, Nowshehra, PakistanFaculty of Computer & Information Technology, Al-Madinah International University, Kuala Lumpur, MalaysiaKing Abdulaziz University, Jeddah, Saudi ArabiaOne of the most important threats to today’s civilization is terrorism. Terrorism not only disturbs the law and order situations in a society but also affects the quality of lives of humans and makes them suppressed physically and emotionally and deprives them of enjoying life. The more the civilizations have advanced, the more the people are working towards exploring different mechanisms to protect the mankind from terrorism. Different techniques have been used as counterterrorism to protect the lives of individuals in society and to improve the quality of life in general. Machine learning methods have been recently explored to develop techniques for counterterrorism based on artificial intelligence (AI). Since deep learning has recently gained more popularity in machine learning domain, in this paper, these techniques are explored to understand the behavior of terrorist activities. Five different models based on deep neural network (DNN) are created to understand the behavior of terrorist activities such as is the attack going to be successful or not? Or whether the attack is going to be suicide or not? Or what type of weapon is going to be used in the attack? Or what type of attack is going to be carried out? Or what region is going to be attacked? The models are implemented in single-layer neural network (NN), five-layer DNN, and three traditional machine learning algorithms, i.e., logistic regression, SVM, and Naïve Bayes. The performance of the DNN is compared with NN and the three machine learning algorithms, and it is demonstrated that the performance in DNN is more than 95% in terms of accuracy, precision, recall, and F1-Score, while ANN and traditional machine learning algorithms have achieved a maximum of 83% accuracy. This concludes that DNN is a suitable model to be used for predicting the behavior of terrorist activities. Our experiments also demonstrate that the dataset for terrorist activities is big data; therefore, a DNN is a suitable model to process big data and understand the underlying patterns in the dataset.http://dx.doi.org/10.1155/2020/1373087 |
| spellingShingle | M. Irfan Uddin Nazir Zada Furqan Aziz Yousaf Saeed Asim Zeb Syed Atif Ali Shah Mahmoud Ahmad Al-Khasawneh Marwan Mahmoud Prediction of Future Terrorist Activities Using Deep Neural Networks Complexity |
| title | Prediction of Future Terrorist Activities Using Deep Neural Networks |
| title_full | Prediction of Future Terrorist Activities Using Deep Neural Networks |
| title_fullStr | Prediction of Future Terrorist Activities Using Deep Neural Networks |
| title_full_unstemmed | Prediction of Future Terrorist Activities Using Deep Neural Networks |
| title_short | Prediction of Future Terrorist Activities Using Deep Neural Networks |
| title_sort | prediction of future terrorist activities using deep neural networks |
| url | http://dx.doi.org/10.1155/2020/1373087 |
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