Data Mining Approach to Counterterrorism (ouvre dans un nouvel onglet)

Auteurs

Université de Yaoundé I

Autres auteurs

Stanley Onyekachi Uche
Deborah Ebem

Advances in Multidisciplinary & Scientific Research Journal Publication

Résumé

Terrorism has long been a major threat to the world for many years and different governments have used different approaches to tackle it. This study reports on the use of available data about terrorist incidents all around the world in combating terrorism with the application of deep learning. There was a comprehensive literature review and the analysis of existing systems and ideas gathered were used to develop the system. This project is done in order to improve on the work carried out by Trisha J. (2018). To improve on the work, extra features were introduced in the dataset and a deep neural network (DNN) model for predicting the success of terrorist attacks was developed. Dataset from the Global Terrorism Database (GTD) were used to train the model. Our proposed model achieved performance accuracy of 91.371% as opposed to that of Trisha J. (2018) which achieved the performance accuracy of 91.18%. Keywords: Terrorism, deep neural network, global terrorism database, data mining

Mots-clés

Norbert Tsopzé