Browsing by Author "Afra, Salim"

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    Intelligent Data Analysis for Early Warning: From Multiple Sources to Multiple Perspectives
    (2019-09-12) Afra, Salim; Alhajj, Reda S.; Moussavi, Mahmood; Alhajj, Reda; Rokne, Jon G.; Moshirpour, Mohammad; Tavli, Bülent
    Misusing and benefiting from the development in technology for communication, criminal and terror groups have recently expanded and spread into global organizations and activities. Fortunately, it is possible to benefit from the technology to fight against terror and criminal groups by tracing, identifying, surrendering, and preventing them from executing their bloodily plans. Indeed, it is very affordable to capture various kinds of data which could be analyzed to predict potential criminals and terrorists. Data comes in various formats from text to images, and may become available incrementally due to dynamic sources. This leads to what has been recently classified as big data which has attracted considerable attention from the industry and the research community. Researchers and developers involved in this domain are trying to adapt and integrate existing techniques into customized solutions which could successfully and effectively handle big data with all its distinguishing characteristics. Alternatively, tremendous effort has been invested in developing new techniques to cope with big data for situations where existing techniques neither individually nor as an integrated group could address the shortcomings in this domain. Realizing the need for effective solutions capable of dealing with criminal and terror groups could be mentioned as the main motivation to undertake the study described in this thesis. The main contribution of this thesis is an early warning system that uses different sources of data to identify potential criminals and terrorists (hereafter both criminals and terrorists will be meant when any of them is mentioned in the text). The process works as follows. Criminal profiles are analyzed and their corresponding criminal networks are derived. This automates and facilitates the work of crime analysts in predicting events that may lead to disaster. We used face images as a data source and performed different studies to determine the accuracy and effectiveness of current face recognition and clustering algorithms in identifying people in uncontrolled environments, which are actually the environments encountered in real situations when dealing with criminals and terrorists. We trained our own face recognition algorithm using convolutional neural networks (CNN) by pre-processing the input images for better recognition rates. We showed how this is more effective than frontalized profile face images. We designed a queuing system for surveillance camera monitoring to raise an alarm when unknown people who pass through a monitored area turn into potential suspects. We also integrated different data sources such as social media, news, and official criminal documents to extract criminal names. We then generate a criminal profile which includes the activities that a given criminal is involved in. We also linked criminals together to build a criminal network by expanding the coverage and analyzing the collected data. We then proposed several unique criminal network analysis techniques to provide better understanding and knowledge for crime analysts. To achieve this, we added more functions related to criminal network analysis to NetDriller which is a powerful social network analysis tool developed by our research group. We also designed an algorithm for link prediction which better detects if a link between two nodes will exist in the future. All these functionalities have been well integrated into the monitoring system which has been developed and well tested to demonstrate its applicability and effectiveness.