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RESEARCH PAPER · PLoS ONE · 2017

Understanding the dynamics of terrorism events with multiple-discipline datasets and machine learning approach

Fangyu Ding, Quansheng Ge, Dong Jiang, Jingying Fu, Mengmeng Hao

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Understanding the dynamics of terrorism events with multiple-discipline datasets and machine learning approachOriginalSpatiotemporal Distribution of U5MR and Their Relationship with Geographic and Socioeconomic Factors in China4A review of the landscape: Challenges and gaps in trauma response to civilian high threat mass casualty incidents15Spatio-temporal simulation of the geopolitical environment system10An Intelligent Resource Manager Over Terrorism Knowledge Base12Simulating Spatio-Temporal Patterns of Terrorism Incidents on the Indochina Peninsula with GIS and the Random Forest Method46Risk management standards and the active management of malicious intent in artificial superintelligence22Beyond validation accuracy: incorporating out-of-distribution checks, explainability, and adversarial attacks into classifier design1Comparative Study on Artificial Intelligence Techniques in Crime Forecasting5Understanding the composition and evolution of terrorist group networks: A rough set approach12A Proposed Gradient Tree Boosting with Different Loss Function in Crime Forecasting and Analysis4Long-range Event-level Prediction and Response Simulation for Urban Crime and Global Terrorism with Granger Networks2Reconsidering the Risk Society: Its Parameters and Repercussions Evaluated by a Statistical Model with Aspects of Different Social Sciences2Global Characteristics of Chemical, Biological, and Radiological Poison Use in Terrorist Attacks5Prediction of Future Terrorist Activities Using Deep Neural Networks88MACHINE LEARNING PER CARATTERIZZARE GLI ATTACCHI TERRORISTICI0Ethics Implications of the Use of Artificial Intelligence in Violence Risk Assessment.23On Recommending Safe Travel Periods to High Attack Risk Destinations0A combination classification method based on Ripper and Adaboost2A combination classification method based on Ripper and Adaboost4Identifying Reliable Recommenders in Users’ Collaborating Filtering and Social Neighbourhoods0Safe Travelling Period Recommendation to High Attack Risk European Destinations Based on Past Attack Information0Design and Implementation Recommender System for Iraqi Terrorist Database Based on Deep Learning1Terrorism and Political Attitudes: Evidence from European Social Surveys8Learning future terrorist targets through temporal meta-graphs38Occupant behavior modeling methods for resilient building design, operation and policy at urban scale: A review87Predicting non-state terrorism worldwide38Spatial Distribution Assessment of Terrorist Attack Types Based on I-MLKNN Model5An analysis of the crucial indicators impacting the risk of terrorist attacks: A predictive perspective20Quantitative Analysis and Prediction of Global Terrorist Attacks Based on Machine Learning14A New Model for Predicting and Dismantling a Complex Terrorist Network8Forecasting the risk of terrorist attacks based on machine learning algorithms1Trend prediction of irrigation area using improved random forest regression5Predicting Terrorism in Europe with Remote Sensing, Spatial Statistics, and Machine Learning19Applying unsupervised machine learning to counterterrorism6A hybrid deep learning-based framework for future terrorist activities modeling and prediction14Risk Matrix for Violent Radicalization: A Machine Learning Approach11Quantifying the influence of climate variability on armed conflict in Africa, 2000–201524Physicians' Response and Preparedness of Terrorism-Related Disaster Events in Quetta City, Pakistan: A Qualitative Inquiry4Predicting terrorist attacks in the United States using localized news data13Open source intelligence extraction for terrorism‐related information: A review15Varying climatic-social-geographical patterns shape the conflict risk at regional and global scales27Predicting the Success of Suicide Terrorist Attacks using different Machine Learning Algorithms11Terrorism and political attitudes: Evidence from European social surveys12Countering Domestic Racially and Ethnically Motivated Violent Terrorism on Social Media: Introducing the Racist and Violent Extremist Flock Tool0Desafíos para la inclusión estratégica de equipos caninos en seguridad y defensa en Colombia4Leveraging Deep Learning for Forecasting Terrorist Activity in Jammu and Kashmir0Identifying factors associated with terrorist attack locations by data mining and machine learning2Attention-based spatial–temporal multi-graph convolutional networks for casualty prediction of terrorist attacks8Exploring the worldwide impact of COVID-19 on conflict risk under climate change2أثر هشاشة الدولة والفقر على الإرهاب في أفريقيا باستخدام نموذجي المربعات الصغرى (OLS) والانحدار الجغرافي الموزون (GWR)0An integrated deep-learning and multi-level framework for understanding the behavior of terrorist groups13Praemonitus praemunitus: can we forecast and prepare for future viral disease outbreaks?19Erőszakos radikalizmus kockázatbecslése – elméleti háttér és kutatási eredmények0Spatiotemporal multi‐graph convolutional network‐based provincial‐day‐level terrorism risk prediction6Terrorism in Egypt: a comprehensive spatial, spatio-temporal, and statistical analysis12Predicting Violent Extremism with Machine Learning: A Scoping Review0Systematic Literature Review and Assessment for Cyber Terrorism Communication and Recruitment Activities4Predictive analysis of terrorist activities in Thailand's Southern provinces: a deep learning approach2Developing a goal-driven data integration framework for effective data analytics11BAST-MGCN: Integrating Background Attributes for Enhanced Spatio-temporal Prediction of Terrorist Attack Casualties1Physics Informed Machine Learning (PIML) for Design, Management and Resilience-Development of Urban Infrastructures: A Review32A novel spatial-aware deep learning approach for exploring the environmental context of terrorist attacks and armed conflicts5Anders Breivik’s manifesto: Measuring Violence Indicator in Anti-Islamic Extremist Discourse0A Comprehensive Survey on AI in Counter-Terrorism and Cybersecurity: Challenges and Ethical Dimensions9Analyzing and Predicting of Global Terrorism Attacks Based on Machine Learning0Social Significance of Soft Targets at the Local Government Level0Literature Review Analysis for Cyberattacks at Management Applications and Industrial Control Systems21Assessing the Transformation of Armed Conflict Types: A Dynamic Approach2Land use as a target for terrorism: A geospatial and temporal framework of terrorist preferences analysis in Egypt4Deciphering terrorist attributions: a global terrorism data-driven comparison of machine learning models2Evolution of Terrorist Attack Risk Assessment Methodologies: A Literature Review of the ”Likelihood-Vulnerability-Consequence” Tripartite Framework0Unsupervised Geospatial Analysis of Terror in the UK0Spatial intensity modelling of ISIS-related attacks using random forest regression: A GeoAI-based analysis of GTD data (2012–2019)0

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73 direct citing papers saved. Showing the first 50 here; use Browse Matching Papers in the map for all loaded papers.

  1. Spatiotemporal Distribution of U5MR and Their Relationship with Geographic and Socioeconomic Factors in China

    International Journal of Environmental Research and Public Health · 2017-11-21

    Zeng Li, Jingying Fu, Dong Jiang, Gang Lin, Donglin Dong, Xiaoxi Yan

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  2. A review of the landscape: Challenges and gaps in trauma response to civilian high threat mass casualty incidents

    The Journal of Trauma: Injury, Infection, and Critical Care · 2018-01-25

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  3. Spatio-temporal simulation of the geopolitical environment system

    Journal of Geographical Sciences · 2018-05-28

    Quansheng Ge, Dong Jiang, Feng Lu, Jingying Fu, Shaoqiang Wang, Xiangzheng Deng

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  4. An Intelligent Resource Manager Over Terrorism Knowledge Base

    Recent Advances in Computer Science and Communications · 2019-01-14

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  5. Simulating Spatio-Temporal Patterns of Terrorism Incidents on the Indochina Peninsula with GIS and the Random Forest Method

    ISPRS International Journal of Geo-Information · 2019-03-07

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  6. Risk management standards and the active management of malicious intent in artificial superintelligence

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  7. Beyond validation accuracy: incorporating out-of-distribution checks, explainability, and adversarial attacks into classifier design

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  8. Comparative Study on Artificial Intelligence Techniques in Crime Forecasting

    Applied Mechanics and Materials · 2019-06-01

    Alif Ridzuan Khairuddin, Razana Alwee, Habibollah Harun

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  9. Understanding the composition and evolution of terrorist group networks: A rough set approach

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  10. A Proposed Gradient Tree Boosting with Different Loss Function in Crime Forecasting and Analysis

    Advances in intelligent systems and computing · 2019-11-01

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  11. Long-range Event-level Prediction and Response Simulation for Urban Crime and Global Terrorism with Granger Networks

    arXiv (Cornell University) · 2019-11-04

    Timmy Li, Yi Huang, James A. Evans, Ishanu Chattopadhyay

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  12. Reconsidering the Risk Society: Its Parameters and Repercussions Evaluated by a Statistical Model with Aspects of Different Social Sciences

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  13. Global Characteristics of Chemical, Biological, and Radiological Poison Use in Terrorist Attacks

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  14. Prediction of Future Terrorist Activities Using Deep Neural Networks

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    Muhammad Irfan Uddin, Nazir Zada, Furqan Aziz, Yousaf Saeed, Asim Zeb, Syed Atif Ali Shah, Mahmoud Ahmad Al‐Khasawneh, Marwan Mahmoud

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  15. MACHINE LEARNING PER CARATTERIZZARE GLI ATTACCHI TERRORISTICI

    Rendiconti. Classe di lettere e scienze morali e storiche · 2020-07-15

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  16. Ethics Implications of the Use of Artificial Intelligence in Violence Risk Assessment.

    PubMed · 2020-09-01

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  17. On Recommending Safe Travel Periods to High Attack Risk Destinations

    IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · 2020-12-07

    Dimitris Spiliotopoulos, Costas Vassilakis, Dionisis Margaris

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  18. A combination classification method based on Ripper and Adaboost

    International Journal of Embedded Systems · 2021-01-01

    Shenggang Yang, Zuozhi Chen, Sangzhi Zhu, Zhiqiang Zhang, Min Wang

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  19. A combination classification method based on Ripper and Adaboost

    International Journal of Embedded Systems · 2021-01-01

    Min Wang, Zuozhi Chen, Zhiqiang Zhang, Sangzhi Zhu, Shenggang Yang

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  20. Identifying Reliable Recommenders in Users’ Collaborating Filtering and Social Neighbourhoods

    Lecture notes in social networks · 2021-01-01

    Dionisis Margaris, Dimitris Spiliotopoulos, Costas Vassilakis

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  21. Safe Travelling Period Recommendation to High Attack Risk European Destinations Based on Past Attack Information

    Lecture notes in social networks · 2021-01-01

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  22. Design and Implementation Recommender System for Iraqi Terrorist Database Based on Deep Learning

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    Ismael Abdul Sattar, Rafah Shihab Alhamdani, Mohamed N. Abdulla

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  23. Terrorism and Political Attitudes: Evidence from European Social Surveys

    National Bureau of Economic Research · 2021-04-01

    Giovanni Peri, Daniel I. Rees, Brock Smith

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  24. Learning future terrorist targets through temporal meta-graphs

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  25. Occupant behavior modeling methods for resilient building design, operation and policy at urban scale: A review

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  26. Predicting non-state terrorism worldwide

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  27. Spatial Distribution Assessment of Terrorist Attack Types Based on I-MLKNN Model

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  28. An analysis of the crucial indicators impacting the risk of terrorist attacks: A predictive perspective

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  29. Quantitative Analysis and Prediction of Global Terrorist Attacks Based on Machine Learning

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  30. A New Model for Predicting and Dismantling a Complex Terrorist Network

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  31. Forecasting the risk of terrorist attacks based on machine learning algorithms

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  32. Trend prediction of irrigation area using improved random forest regression

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  33. Predicting Terrorism in Europe with Remote Sensing, Spatial Statistics, and Machine Learning

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  34. Applying unsupervised machine learning to counterterrorism

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  35. A hybrid deep learning-based framework for future terrorist activities modeling and prediction

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  36. Risk Matrix for Violent Radicalization: A Machine Learning Approach

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  37. Quantifying the influence of climate variability on armed conflict in Africa, 2000–2015

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  38. Physicians' Response and Preparedness of Terrorism-Related Disaster Events in Quetta City, Pakistan: A Qualitative Inquiry

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  39. Predicting terrorist attacks in the United States using localized news data

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  40. Open source intelligence extraction for terrorism‐related information: A review

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  41. Varying climatic-social-geographical patterns shape the conflict risk at regional and global scales

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  42. Predicting the Success of Suicide Terrorist Attacks using different Machine Learning Algorithms

    International Conference on Computer and Information Technology (ICCIT) · 2022-12-17

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  43. Terrorism and political attitudes: Evidence from European social surveys

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  44. Countering Domestic Racially and Ethnically Motivated Violent Terrorism on Social Media: Introducing the Racist and Violent Extremist Flock Tool

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  46. Leveraging Deep Learning for Forecasting Terrorist Activity in Jammu and Kashmir

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  47. Identifying factors associated with terrorist attack locations by data mining and machine learning

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  48. Attention-based spatial–temporal multi-graph convolutional networks for casualty prediction of terrorist attacks

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  49. Exploring the worldwide impact of COVID-19 on conflict risk under climate change

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  50. أثر هشاشة الدولة والفقر على الإرهاب في أفريقيا باستخدام نموذجي المربعات الصغرى (OLS) والانحدار الجغرافي الموزون (GWR)

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