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A machine learning based model of Bo...

Boko Haram.

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A machine learning based model of Boko Haram

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	A machine learning based model of Boko Haram/ by V. S. Subrahmanian ... [et al.].
其他作者:	Subrahmanian, V. S.
出版者:	Cham :Springer International Publishing : : 2021.,
面頁冊數:	xii, 135 p. :ill., digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Terrorism - Forecasting. -
電子資源:	https://doi.org/10.1007/978-3-030-60614-5
ISBN:	9783030606145

A machine learning based model of Boko Haram
A machine learning based model of Boko Haram[electronic resource] /by V. S. Subrahmanian ... [et al.]. - Cham :Springer International Publishing :2021. - xii, 135 p. :ill., digital ;24 cm. - Terrorism, security, and computation,2197-8778. - Terrorism, security, and computation..

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.

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.

ISBN: 9783030606145

Standard No.: 10.1007/978-3-030-60614-5doiSubjects--Corporate Names:

2156727
Boko Haram.
Subjects--Topical Terms:

3488427
Terrorism
--Forecasting.

LC Class. No.: HV6433.A35

Dewey Class. No.: 363.325096

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505 0 $a 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.
520 $a 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.
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