A machine learning based model of Boko Haram / V.S. Subrahmanian, Chiara Pulice, James F. Brown, Jacob Bonen-Clark ; foreword by Geert Kuiper.
2021
HV6433.A35
Linked e-resources
Linked Resource
Concurrent users
Unlimited
Authorized users
Authorized users
Document Delivery Supplied
Can lend chapters, not whole ebooks
Details
Title
A machine learning based model of Boko Haram / V.S. Subrahmanian, Chiara Pulice, James F. Brown, Jacob Bonen-Clark ; foreword by Geert Kuiper.
Author
ISBN
9783030606145 (electronic bk.)
3030606147 (electronic bk.)
9783030606152 (print)
3030606155
9783030606169 (print)
3030606163
9783030606138
3030606139
3030606147 (electronic bk.)
9783030606152 (print)
3030606155
9783030606169 (print)
3030606163
9783030606138
3030606139
Published
Cham : Springer, [2021]
Language
English
Description
1 online resource (xii, 135 pages) : illustrations (chiefly color)
Item Number
10.1007/978-3-030-60614-5 doi
Call Number
HV6433.A35
Dewey Decimal Classification
363.325096
Summary
This is the first study of Boko Haram that brings advanced data-driven, machine learning models to both learn models capable of predicting a wide range of attacks carried out by Boko Haram, as well as develop data-driven policies to shape Boko Haram's behavior and reduce attacks by them. This book also identifies conditions that predict sexual violence, suicide bombings and attempted bombings, abduction, arson, looting, and targeting of government officials and security installations. After reducing Boko Haram's history to a spreadsheet containing monthly information about different types of attacks and different circumstances prevailing over a 9 year period, this book introduces Temporal Probabilistic (TP) rules that can be automatically learned from data and are easy to explain to policy makers and security experts. This book additionally reports on over 1 year of forecasts made using the model in order to validate predictive accuracy. It also introduces a policy computation method to rein in Boko Haram's attacks. Applied machine learning researchers, machine learning experts and predictive modeling experts agree that this book is a valuable learning asset. Counter-terrorism experts, national and international security experts, public policy experts and Africa experts will also agree this book is a valuable learning tool.
Bibliography, etc. Note
Includes bibliographical references.
Access Note
Access limited to authorized users.
Digital File Characteristics
text file
PDF
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed February 24, 2021).
Series
Terrorism, security, and computation. 2197-8778
Available in Other Form
Print version: 3030606139
Linked Resources
Record Appears in
Table of Contents
Chapter 1: Introduction
Chapter 2: History of Boko Haram
Chapter 3: Temporal Probabilistic Rules and Policy Computation Algorithms
Chapter 4: Sexual Violence
Chapter 5: Suicide Bombings
Chapter 6: Abductions
Chapter 7: Arson
Chapter 8: Other Types of Attacks
Appendix A: All TP-Rules
Appendix B: Data Collection
Appendix C: Most Used Variables
Appendix D: Sample Boko Haram Report.
Chapter 2: History of Boko Haram
Chapter 3: Temporal Probabilistic Rules and Policy Computation Algorithms
Chapter 4: Sexual Violence
Chapter 5: Suicide Bombings
Chapter 6: Abductions
Chapter 7: Arson
Chapter 8: Other Types of Attacks
Appendix A: All TP-Rules
Appendix B: Data Collection
Appendix C: Most Used Variables
Appendix D: Sample Boko Haram Report.