Data Analysis and Risk Modelling for Terrorism Prevention: - Röttinger, Raphael

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Résumé :
1 Introduction 1.1 Problem definition and research question 1.2 Objectives and approach 2 Literature research - data analysis in the context of terrorism prevention 2.1 Data science and artificial intelligence 2.2 Data analysis models 2.3 Modern terrorism 2.4 Factors in the prevention of terrorism 2.5 Challenges in data analysis 2.6 Hypotheses and concept of the work 3 Methodology and data basis 3.1 Data records 3.2 Methodological approach 3.3 Research ethics 4 Analysis model and interpretation 4.1 Data processing and preparation 4.2 Cluster and classification analysis 4.3 Deep learning 4.4 Visualisation of the results 5 Discussion 5.1 Feasibility and technical realisation 5.2 Interpretation of the results and recommendations for action 6 Conclusion IV Bibliography...

Biographie:
1 Introduction 1.1 Problem definition and research question 1.2 Objectives and approach 2 Literature research - data analysis in the context of terrorism prevention 2.1 Data science and artificial intelligence 2.2 Data analysis models 2.3 Modern terrorism 2.4 Factors in the prevention of terrorism 2.5 Challenges in data analysis 2.6 Hypotheses and concept of the work 3 Methodology and data basis 3.1 Data records 3.2 Methodological approach 3.3 Research ethics 4 Analysis model and interpretation 4.1 Data processing and preparation 4.2 Cluster and classification analysis 4.3 Deep learning 4.4 Visualisation of the results 5 Discussion 5.1 Feasibility and technical realisation 5.2 Interpretation of the results and recommendations for action 6 Conclusion IV Bibliography...

Sommaire:
1 Introduction 1.1 Problem definition and research question 1.2 Objectives and approach 2 Literature research - data analysis in the context of terrorism prevention 2.1 Data science and artificial intelligence 2.2 Data analysis models 2.3 Modern terrorism 2.4 Factors in the prevention of terrorism 2.5 Challenges in data analysis 2.6 Hypotheses and concept of the work 3 Methodology and data basis 3.1 Data records 3.2 Methodological approach 3.3 Research ethics 4 Analysis model and interpretation 4.1 Data processing and preparation 4.2 Cluster and classification analysis 4.3 Deep learning 4.4 Visualisation of the results 5 Discussion 5.1 Feasibility and technical realisation 5.2 Interpretation of the results and recommendations for action 6 Conclusion IV Bibliography...