LEADER 03261nam 2200493 450 001 996464387803316 005 20210310092954.0 010 $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 $a20210310d2021 uy 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 /$fV. S. Subrahmanian [and three others] ; foreword by Geert Kuiper 205 $a1st ed. 2021. 210 1$aCham, Switzerland :$cSpringer,$d[2021] 210 4$dİ2021 215 $a1 online resource (XII, 135 p. 38 illus., 29 illus. in color.) 225 1 $aTerrorism, Security, and Computation 311 $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. 606 $aTerrorism$xForecasting 606 $aTerrorism$xPrevention 615 0$aTerrorism$xForecasting. 615 0$aTerrorism$xPrevention. 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 $a996464387803316 996 $aA machine learning based model of Boko Haram$92814382 997 $aUNISA