東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

A machine learning based model of Bo...

Boko Haram.

Linked to FindBook

Google Book

Amazon

博客來

A machine learning based model of Boko Haram

Record Type:	Electronic resources : Monograph/item
Title/Author:	A machine learning based model of Boko Haram/ by V. S. Subrahmanian ... [et al.].
other author:	Subrahmanian, V. S.
Published:	Cham :Springer International Publishing : : 2021.,
Description:	xii, 135 p. :ill., digital ;24 cm.
[NT 15003449]:	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
Subject:	Terrorism - Forecasting. -
Online resource:	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

A machine learning based model of Boko Haram
LDR:02804nmm a2200337 a 4500 001 2236756
003 DE-He213
005 20201211223322.0
006 m d
007 cr nn 008maaau
008 211111s2021 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 2156727
650 0 $a Terrorism $x Forecasting. $3 3488427
650 0 $a Terrorism $x Prevention. $3 588483
650 1 4 $a Machine Learning. $3 3382522
650 2 4 $a Data Mining and Knowledge Discovery. $3 898250
650 2 4 $a Terrorism and Political Violence. $3 2182328
700 1 $a Subrahmanian, V. S. $3 706285
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Terrorism, security, and computation. $3 2139163
856 4 0 $u https://doi.org/10.1007/978-3-030-60614-5
950 $a Computer Science (SpringerNature-11645)