#5329. Winning the war on terror: Using

August 2026publication date
Proposal available till 13-05-2025
4 total number of authors per manuscript0 $

The title of the journal is available only for the authors who have already paid for
Journal’s subject area:
Library and Information Sciences;
Strategy and Management;
Information Systems;
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Abstract:
From the perspective of counterterrorism strategies, terrorist risk assessment has become an important approach for counterterrorism early warning research. Combining with the characteristics of known terrorists, a quantitative analysis method of active risk assessment method with terrorists as the research object is proposed. This assessment method introduces deep learning algorithms into social computing problems on the basis of information coding technology. The authors design a special Top-k algorithm to screen the terrorism related features and optimize the evaluation model through convolution neural network so as to determine the risk level of terrorist suspects. This study provides important research ideas for counterterrorism assessment and verifies the feasibility and accuracy of the proposed scheme through a number of experiments, which greatly improves the efficiency of counterterrorism early warning.
Keywords:
Top-K algorithm; Characteristic of terrorist; Convolutional neural network; Non-privacy virtual data; Risk assessment

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