國立高雄大學圖資館 |

Language: English

Back

A machine learning based model of Bo...

Boko Haram.

A machine learning based model of Boko Haram

Record Type:	Electronic resources : Monograph/item
Title/Author:	A machine learning based model of Boko Haramby V. S. Subrahmanian ... [et al.].
other author:	Subrahmanian, V. S.
Published:	Cham :Springer International Publishing :2021.
Description:	xii, 135 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	TerrorismForecasting.
Online resource:	https://doi.org/10.1007/978-3-030-60614-5
ISBN:	9783030606145$q(electronic bk.)

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$q(electronic bk.)

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

726763
Boko Haram.
Subjects--Topical Terms:

889040
Terrorism
--Forecasting.

LC Class. No.: HV6433.A35

Dewey Class. No.: 363.325096

A machine learning based model of Boko Haram
LDR:02804nmm a2200337 a 4500 001 596296
003 DE-He213
005 20201211223322.0
006 m d
007 cr nn 008maaau
008 211013s2021 sz s 0 eng d
020 $a 9783030606145$q(electronic bk.)
020 $a 9783030606138$q(paper)
024 7 $a 10.1007/978-3-030-60614-5 $2 doi
035 $a 978-3-030-60614-5
040 $a GP $c GP
041 0 $a eng
050 4 $a HV6433.A35
072 7 $a UYQM $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQM $2 thema
082 0 4 $a 363.325096 $2 23
090 $a HV6433.A35 $b M149 2021
245 0 2 $a A machine learning based model of Boko Haram $h [electronic resource] / $c by V. S. Subrahmanian ... [et al.].
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a xii, 135 p. : $b ill., digital ; $c 24 cm.
490 1 $a Terrorism, security, and computation, $x 2197-8778
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.
610 2 0 $a Boko Haram. $3 726763
650 0 $a Terrorism $x Forecasting. $3 889040
650 0 $a Terrorism $x Prevention. $3 266172
650 1 4 $a Machine Learning. $3 833608
650 2 4 $a Data Mining and Knowledge Discovery. $3 275288
650 2 4 $a Terrorism and Political Violence. $3 740222
700 1 $a Subrahmanian, V. S. $3 889039
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a Terrorism, security, and computation. $3 715543
856 4 0 $u https://doi.org/10.1007/978-3-030-60614-5
950 $a Computer Science (SpringerNature-11645)