International Journal of Advanced Computer Science and Information Technology

10

Authors: Javad Hosseinkhani , Suriayati Chuprat , Hamed Taherdoost , Morteza Harati Cool , Sadegh Emami Korani

Criminal web data always offer novel and useful knowledge and information for Law administration. The used digital data in legal assessments are involved parts of information about the accused’ social networks. Thus, evaluation of these parts of information is challenging. Therefore, an investigator has to pull out the appropriate information from the text manually, these information are on the website. An investigator also makes the relationship between various parts of information and classify them in to a structured database so the category is ready to use in different criminal network evaluation tools for analysis. On the other hand, these manual processes are not adequate in the case that it has many errors. Moreover, as the quality of resulted evaluation depends on the investigator’s proficiency, the reliability is not stable. On another word, the more proficient operator, the better result achieved. The purpose of this paper is suggesting a framework by using concurrent crawler to show the process of exploring the criminal accused of legal data evaluation which insures the reliability gap.

18

Authors: Dr. Ahmed T. Sadiq , Sura Mahmood Abdullah

Text categorization is the task in which text documents are classified into one or more of predefined categories based on their contents. This paper shows that the proposed system consists of three main steps: text document representation, classifier construction and performance evaluation. In the first step, a set of pre-classified text documents is provided. Each text document is initially preprocessed in order to be split into features, these features are weighted based on the frequency of each feature in that text document and eliminate the non-informative features. The remaining features are next standardized by reducing a feature to its root using the stemming process. Due to the large number of features even after the non-informative features removal and the stemming process, the proposed system applies specific thresholds to extract distinct features which represent that text document. In the second step, the text categorization model (classifier) is built by learning the distinct features which represent all the preclassified text documents for each sub-category of main categories; this process can be achieved by using one of the supervised categorization techniques that is called the rough set theory. Thereafter, the model uses a pair of precise concepts from the above theory that are called the lower and upper approximations to classify any test text document into one or more of main categories and sub-categories. In the final step, the performance of the proposed system is evaluated. It has achieved good results up to 96%, when applied to a number of test text documents for each sub-category of main categories.