Machine Learning Techniques to Visualize and Predict Terrorist Attacks Worldwide using the Global Terrorism Database

Terrorist attacks affect the confidence and security of citizens; it is a violent form of a political struggle that ends in the destruction of order. In the current decade, along with the growth of social networks, terrorist attacks around the world are still ongoing and have had potential growth in recent years. Consequently, it is necessary to identify where the attacks were committed and where is the possible area for an attack. The objective is to provide assertive solutions to these events. As a solution, this research focuses on one of the branches of artificial intelligence (AI), which is the Automatic Learning, also called Machine Learning. The idea is to use AI techniques to visualize and predict possible terrorist attacks using classification models, the decision trees, and the Random Forest. The input would be a database that has a systematic record of worldwide terrorist attacks from 1970 to the last recorded year, which is 2018. As a final result, it is necessary to know the number of terrorist attacks in the world, the most frequent types of attacks and the number of seizures caused by region; furthermore, to be able to predict what kind of terrorist attack will occur and in which areas of the world. Finally, this research aims to help the scientific community use artificial intelligence to provide various types of solutions related to global events.

@article{Huamaní2020,
  title     = {Machine Learning Techniques to Visualize and Predict Terrorist Attacks Worldwide using the Global Terrorism Database},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {11},
  number    = {4},
  year      = {2020},
  publisher = {The Science and Information Organization},
  author    = {Enrique Lee Huamaní and Alva Mantari Alicia and Avid Roman-Gonzalez},
  doi       = {10.14569/IJACSA.2020.0110474},
  url       = {https://doi.org/10.14569/IJACSA.2020.0110474}
}