|
Showing 1 - 2 of
2 matches in All Departments
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.
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.
|
|