Titlebook: Counterterrorism and Open Source Intelligence; Uffe Kock Wiil Book 2011 Springer-Verlag/Wien 2011 Counterterrorism.Data Mining.Open Source

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Deduktion und Umkehrung des Idealismusse learners. Then we have applied boosting algorithm with suitable weak learners and parameter settings such as the number of boosting iterations. We propose a Naive Bayes classifier as a suitable weak learner for the boosting algorithm. It achieves maximum performance with very few boosting iterations.

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Die historische Dimension der Dialektikoutperforms the existing classification models for the task of suspicious email detection and topic categorization on the Reuters-21578 and 20 Newsgroups datasets. Our model also outperforms A Decision Cluster Classification (ADCC) and the Decision Cluster Forest Classification (DCFC) models on the Reuters-21578 dataset.

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Understanding Terrorist Network Topologies and Their Resilience Against Disruptiony the resilience properties of secrecy versus information balanced networks. This provides an explanation of the survival of global terrorist networks and food for thought on counterterrorism strategy policy.

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Co-offending Network Miningny such data set and link the data model to the analysis techniques. We contend that central aspects considered in the work presented here carry over to a wide range of large data sets studied in intelligence and security informatics to better serve law enforcement and intelligence agencies.

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The Use of Open Source Intelligence in the Construction of Covert Social Networks into the structure of covert social networks from the limited and fragmentary data gathered from intelligence operations or open sources. A protocol for predicting the existence of hidden “key-players” covert in social networks is given.

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Retracted: A Novel Method to Analyze the Importance of Links in Terrorist Networksa novel method to analyze the importance of links in terrorist networks inspired by research on transportation networks. The12.6pc]The first author has been considered as corresponding author. Please check. link importance measure is implemented in CrimeFighter Assistant and evaluated on known terrorist networks harvested from open sources.

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Cluster Based Text Classification Modeloutperforms the existing classification models for the task of suspicious email detection and topic categorization on the Reuters-21578 and 20 Newsgroups datasets. Our model also outperforms A Decision Cluster Classification (ADCC) and the Decision Cluster Forest Classification (DCFC) models on the Reuters-21578 dataset.

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