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Machine Learning Techniques to Predict Terrorist Attacks : Exemplified by Jama'at Nasr al-Islam wal Muslimin - Laura Mostert

Machine Learning Techniques to Predict Terrorist Attacks

Exemplified by Jama'at Nasr al-Islam wal Muslimin

By: Laura Mostert, Roy Lindelauf, Chiara Pulice, Marnix Provoost, V. S. Subrahmanian

eText | 1 July 2025

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One of the most influential actors in spreading Islamist violence across the Sahel is Jama'at Nasr Al Islam Wal Muslimin (JNIM).This book provides the first systematic quantitative analysis of JNIM's behavior by analyzing a 12-year database of JNIM's attacks and the environment surrounding JNIM. This book leverages AI/ML predictive models to accurately predict almost 40 types of attacks using over 80 independent variables.

This book describes a set of temporal probabilistic rules that state that when the environment in which the group operates satisfies some conditions, then an attack of a certain type will likely occur in the next N months. This provides a deep, easy to comprehend understanding of the conditions under which JNIM carries various kinds of attacks up to 6 months into the future.

This book will serve as an invaluable guide to scholars (computer scientists, political scientists, policy makers). Military officers, intelligence personnel, and government employees, who seek to understand, predict, and eventually mitigate attacks by JNIM and bring peace to the nations of Mali, Burkina Faso, and Niger will want to purchase this book as well.

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Non-FictionComputing & I.T.Computer ScienceArtificial IntelligenceMachine LearningWarfare & DefencePolitics & GovernmentPolitical ActivismTerrorismMathematicsProbability & StatisticsCentral Government
Central Government PoliciesEngineering & TechnologyOther Technologies & Applied SciencesMilitary Engineering

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