Attention-based spatial–temporal multi-graph convolutional networks for casualty prediction of terrorist attacks
Abstract At present, terrorism has become an important factor affecting world peace and development. As the time series data of terrorist attacks usually show a high degree of spatial–temporal correlation, the spatial–temporal prediction of casualties in terrorist attacks is still a significant chal...
Main Authors: | Zhiwen Hou, Yuchen Zhou, Xiaowei Wu, Fanliang Bu |
---|---|
Format: | Article |
Language: | English |
Published: |
Springer
2023-05-01
|
Series: | Complex & Intelligent Systems |
Subjects: | |
Online Access: | https://doi.org/10.1007/s40747-023-01037-z |
Similar Items
-
Terrorist organizations and suicide attacks
by: Baljak Miroslav B., et al.
Published: (2022-01-01) -
Analysis of terrorist attacks in European Union, focused on attacks committed in France
by: Marek MAJERNÍK
Published: (2021-06-01) -
GA-CatBoost-Weight Algorithm for Predicting Casualties in Terrorist Attacks: Addressing Data Imbalance and Enhancing Performance
by: Yuxiang He, et al.
Published: (2024-03-01) -
Terrorist attacks on public transport
by: Petrović Marko Z.
Published: (2023-01-01) -
TERRORIST PHENOMENON’S IMPACT ON THE WORLD ECONOMY
by: Liviu UZLAU
Published: (2015-03-01)