000			03852nam a22004815i 4500
999			_c334674 _d334674 _x1
001			334674
003			ES-MaUEC
005			20230102114734.0
006			a||||fo|||| 00| 0
007			cr nn nnnaamaa
008			201211s2021 gw a s |||| 0|eng d
020			_a9783030606145
024	7		_a10.1007/978-3-030-60614-5 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ325.5 _b2021 EB
100	1		_aSubrahmanian, V.S. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _999279
245	1	2	_aA Machine Learning Based Model of Boko Haram _cby V. S. Subrahmanian, Chiara Pulice, James F. Brown, Jacob Bonen-Clark
250			_aFirst edition 2021
264		1	_aCham, Switzerland _bSpringer International Publising _c2021
300			_a1 recurso en línea (XII, 135 páginas) _b38 ilustraciones, 29 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF _2rda
490	0		_aTerrorism Security and Computation _x2197-8778
505	0		_aChapter 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	3		_aThis 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.
988			_aSpringer_Computer_2021
650		7	_2embne _aAprendizaje automático _9166090
650		7	_2embne _9419613 _aTerrorismo _xPolítica gubernamental
700	1		_aPulice, Chiara _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9679211
700	1		_aBrown, James F. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9679213
700	1		_aBonen-Clark, Jacob _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9679214
710	2		_aSpringerLink
776	0	8	_iPrinted edition: _z9783030606138
776	0	8	_iPrinted edition: _z9783030606152
776	0	8	_iPrinted edition: _z9783030606169
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-60614-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b07/2021 _dz _ea _zSI