Spatiotemporal multi‐graph convolutional network‐based provincial‐day‐level terrorism risk prediction
Published 2023 View Full Article
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Title
Spatiotemporal multi‐graph convolutional network‐based provincial‐day‐level terrorism risk prediction
Authors
Keywords
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Journal
RISK ANALYSIS
Volume -, Issue -, Pages -
Publisher
Wiley
Online
2023-10-24
DOI
10.1111/risa.14241
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- (2021) Ruifang Zhao et al. ISPRS International Journal of Geo-Information
- An analysis of the crucial indicators impacting the risk of terrorist attacks: A predictive perspective
- (2021) Lanjun Luo et al. SAFETY SCIENCE
- DDP-GCN: Multi-graph convolutional network for spatiotemporal traffic forecasting
- (2021) Kyungeun Lee et al. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
- Bidirectional LSTM with self-attention mechanism and multi-channel features for sentiment classification
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- (2020) M. Irfan Uddin et al. COMPLEXITY
- Simulating the Linkages Between Economy and Armed Conflict in India With a Long Short‐Term Memory Algorithm
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- (2020) Jiaqi Shi et al. SENSORS
- A Comprehensive Survey on Graph Neural Networks
- (2020) Zonghan Wu et al. IEEE Transactions on Neural Networks and Learning Systems
- Simulating Spatio-Temporal Patterns of Terrorism Incidents on the Indochina Peninsula with GIS and the Random Forest Method
- (2019) Mengmeng Hao et al. ISPRS International Journal of Geo-Information
- T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction
- (2019) Ling Zhao et al. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
- Crime prediction through urban metrics and statistical learning
- (2018) Luiz G.A. Alves et al. PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
- Earthquake prediction model using support vector regressor and hybrid neural networks
- (2018) Khawaja M. Asim et al. PLoS One
- Understanding the dynamics of terrorism events with multiple-discipline datasets and machine learning approach
- (2017) Fangyu Ding et al. PLoS One
- Prediction of crime occurrence from multi-modal data using deep learning
- (2017) Hyeon-Woo Kang et al. PLoS One
- MLSMOTE: Approaching imbalanced multilabel learning through synthetic instance generation
- (2015) Francisco Charte et al. KNOWLEDGE-BASED SYSTEMS
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- (2015) Seung Geun Kim et al. Nuclear Engineering and Technology
- The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains
- (2013) D. I. Shuman et al. IEEE SIGNAL PROCESSING MAGAZINE
- Pattern in Escalations in Insurgent and Terrorist Activity
- (2011) N. Johnson et al. SCIENCE
- Wavelets on graphs via spectral graph theory
- (2010) David K. Hammond et al. APPLIED AND COMPUTATIONAL HARMONIC ANALYSIS
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