Criminals use online social networks for various activities by including communication, planning, and execution of criminal acts. They often employ ciphered posts using slang expressions, which are restricted to specific groups. Although literature shows advances in analysis of posts in natural language messages, such as hate discourses, threats, and more notably in the sentiment analysis; research enabling intention analysis of posts using slang expressions is still underexplored. We propose a framework and construct software prototypes for the selection of social network posts with criminal slang expressions and automatic classification of these posts according to illocutionary classes. The developed framework explores computational ontologies and machine learning (ML) techniques. Our defined Ontology of Criminal Expressions represents crime concepts in a formal and flexible model, and associates them with criminal slang expressions. This ontology is used for selecting suspicious posts and decipher them. In our solution, the criminal intention in written posts is automatically classified relying on learned models from existing posts. This work carries out a case study to evaluate the framework with 8,835,290 tweets. The obtained results show its viability by demonstrating the benefits in deciphering posts and the effectiveness of detecting user’s intention in written criminal posts based on ML.
Este artigo apresenta um método robusto para localização rápida da pupila, íris e das pálpebras. O método tem como objetivo incrementar o desempenho de sistemas de reconhecimento biométrico de íris através da redução do tempo de processamento na etapa de segmentação de imagem de íris.
This paper describes in detail different hand vein recognition methods based on Wavelet-SVM, Wavelet-ANN and Image Registration. A new image segmentation and processing algorithm is proposed to efficiently locate vein regions and suitable for feature extraction (wavelet coefficients and normalized vein imagens) and classification (SVM, ANN and Image Registration). For real time recognition and high recognition rate, we proposed an integrated system which combines three above mentioned classification methods. The simulation results reveal that the proposed integrated system achieves 1% false rejection rate (FRR) and 0.02% false acceptance rate (FAR). Resumo Este artigo descreve em detalhes diferentes métodos para o reconhecimento biométrico com base nas veias das mãos. Os métodos têm como base Wavelets com SVM, Wavelets com Rede Neural Artificial e Registro de Imagens. Um novo método de segmentação de imagensé proposto para localizar de modo adequado e eficiente a região das veias, permitindo a extração de características (com Wavelets ou a partir de uma imagem normalizada) e a classificação (com SVM, Rede Neural e Registro de Imagens). Para uma alta taxa de reconhecimento realizada em tempo real nós propomos um sistema híbrido que combina os três métodos de classificação mencionados. Os resultados de testes revelam que o sistema híbrido fornece uma taxa de falsa rejeição (FRR) de 1% para uma taxa de falsa aceitação (FAR) de 0.02%.
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