An integrated deep-learning and multi-level framework for understanding the behavior of terrorist groups.

ABSTRACT: Human security is threatened by terrorism in the 21st century. A rapidly growing field of study aims to understand terrorist attack patterns for counter-terrorism policies. Existing research aimed at predicting terrorism from a single perspective, typically employing only background contextual information or past attacks of terrorist groups, has reached its limits. Here, we propose an integrated deep-learning framework that incorporates the background context of past attacked locations, social networks, and past actions of individual terrorist groups to discover the behavior patterns of terrorist groups. The results show that our framework outperforms the conventional base model at different spatio-temporal resolutions. Further, our model can project future targets of active terrorist grou

SUBMITTER: Jiang D 

PROVIDER: S-EPMC10457427 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature