By Ophir Frieder (auth.), Shlomo Argamon, Newton Howard (eds.)
Modern terrorist networks pose an exceptional hazard to overseas safeguard. Their fluid and non-hierarchical constructions, their non secular and ideological motivations, and their predominantly non-territorial pursuits all considerably complicate the query of the way to neutralize them. As governments and militaries paintings to plot new rules and doctrines to strive against terror, new applied sciences are desperately had to make those efforts powerful.
This publication collects a variety of the most up-tp-date computational learn addressing serious concerns for counterterrorism in a dynamic and complicated danger environment:
- finding, summarizing, and comparing correct details from huge and dynamic info stores;
- simulation and prediction of most likely enemy activities and the results of proposed counter-efforts; and
- producing actionable intelligence by way of discovering significant styles hidden in lots of noisy facts items.
The contributions are geared up thematically into 4 sections. the 1st part issues efforts to supply powerful entry to small quantities of proper info buried in huge, immense quantities of various unstructured facts. the second one part discusses equipment for the major challenge of extracting significant info from digitized records in a variety of languages. The 3rd part offers study on interpreting graphs and networks, delivering new methods of getting to know hidden constructions in information and profiles of adversaries’ pursuits and intentions. eventually, the fourth component to the e-book describes software program structures that let analysts to version, simulate, and are expecting the results of real-world conflicts.
The versions and strategies mentioned during this publication are useful studying for governmental decision-makers designing new regulations to counter terrorist threats, for participants of the army, intelligence, and legislations enforcement groups devising counterterrorism techniques in response to new applied sciences, and for educational and commercial researchers devising more suitable equipment for wisdom discovery in complex and numerous datasets.
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