LEADER 00754nam0-22002891i-450- 001 990006701030403321 005 20001010 035 $a000670103 035 $aFED01000670103 035 $a(Aleph)000670103FED01 035 $a000670103 100 $a20001010d--------km-y0itay50------ba 101 0 $aita 105 $ay-------001yy 200 1 $a<>existentialisme$fPaul Foulquie' 210 $aParis$cPUF$d1974 215 $a126 p.$d22 cm 225 1 $aQue sais-je?$v253 700 1$aFoulquie,$bPaul$f<1893-1983>$0162472 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990006701030403321 952 $aCOLLEZ. 241 (253)$b7344$fFSPBC 959 $aFSPBC 996 $aExistentialisme$9137090 997 $aUNINA DB $aGEN01 LEADER 03661nam 22006015 450 001 9910484486803321 005 20251113181014.0 010 $a3-030-60614-7 024 7 $a10.1007/978-3-030-60614-5 035 $a(CKB)4100000011645120 035 $a(DE-He213)978-3-030-60614-5 035 $a(MiAaPQ)EBC6424424 035 $a(PPN)252516257 035 $a(EXLCZ)994100000011645120 100 $a20201211d2021 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 12$aA Machine Learning Based Model of Boko Haram /$fby V. S. Subrahmanian, Chiara Pulice, James F. Brown, Jacob Bonen-Clark 205 $a1st ed. 2021. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2021. 215 $a1 online resource (XII, 135 p. 38 illus., 29 illus. in color.) 225 1 $aTerrorism, Security, and Computation,$x2197-8786 311 08$a3-030-60613-9 327 $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. 330 $aThis is the first study of Boko Haram that brings advanced data-driven, machine learning models to both learn models capable of predicting a wide range of attacks carried out by Boko Haram, as well as develop data-driven policies to shape Boko Haram?s behavior and reduce attacks by them. This book also identifies conditions that predict sexual violence, suicide bombings and attempted bombings, abduction, arson, looting, and targeting of government officials and security installations. After reducing Boko Haram?s history to a spreadsheet containing monthly information about different types of attacks and different circumstances prevailing over a 9 year period, this book introduces Temporal Probabilistic (TP) rules that can be automatically learned from data and are easy to explain to policy makers and security experts. This book additionally reports on over 1 year of forecasts made using the model in order to validate predictive accuracy. It also introduces a policy computation method to rein in Boko Haram?s attacks. Applied machine learning researchers, machine learning experts and predictive modeling experts agree that this book is a valuable learning asset. Counter-terrorism experts, national and international security experts, public policy experts and Africa experts will also agree this book is a valuable learning tool. 410 0$aTerrorism, Security, and Computation,$x2197-8786 606 $aMachine learning 606 $aData mining 606 $aTerrorism 606 $aPolitical violence 606 $aMachine Learning 606 $aData Mining and Knowledge Discovery 606 $aTerrorism and Political Violence 615 0$aMachine learning. 615 0$aData mining. 615 0$aTerrorism. 615 0$aPolitical violence. 615 14$aMachine Learning. 615 24$aData Mining and Knowledge Discovery. 615 24$aTerrorism and Political Violence. 676 $a363.32 700 $aSubrahmanian$b V. S.$0542368 702 $aKuiper$b Geert 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910484486803321 996 $aA machine learning based model of Boko Haram$92814382 997 $aUNINA