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Information users depend heavily on emails' system as one of the major sources of communication. Its importance and usage are continuously growing despite the evolution of mobile applications, social networks, etc. Emails are used on both the personal and professional levels. They can be considered as official documents in communication among users. Emails' data mining and analysis can be conducted for several purposes such as: Spam detection and classification, subject classification, etc. In this paper, a large set of personal emails is used for the purpose of folder and subject classifications. Algorithms are developed to perform clustering and classification for this large text collection. Classification based on NGram is shown to be the best for such large text collection especially as text is Bi-language (i.e. with English and Arabic content). Ó 2014 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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The continuous inventions and evolutions in all information technology fields open new channels and opportunities to enhance teaching and educational methods. On one side, these may improve the abilities of educators to present information in interactive and media-enhanced formats relative to traditional methods. This may help students or learners through offering them the information in channels and methods that can be easier to understand, deal with and retrieve. On the other hand, offering those alternative methods of teaching can be helpful particularly for children, people with special needs, or students in rural areas where they can have virtual or remote instructors, especially for majors who have shortages. The purpose of this study is to investigate the impact of utilising multimedia technologies on enhancing, or not, the effectiveness of teaching students at early stages in Jordanian primary schools. To achieve this objective, a program has been developed to test the students' ability to understand basic mathematical knowledge and skills. Two groups were selected from a local school based on their own class distribution, where one group was taught the subject in basic math using a program developed for this purpose and the second class was taught the same subject using traditional methods of teaching (i.e. direct student-to-child instruction, board, etc.). Results showed that in such math skills at this age, using programs or multimedia-enhanced methods of teaching can be effective in getting students' attention, especially when cartoon characters are used. Results also showed that there is no significant difference in learning and knowledge skills and information absorption based on gender distribution, as a comparison of the results between little boys and girls showed no significant difference in their learning skills.

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Abstract-Some Web sites developers act as spammers and try to mislead the search engines by using illegal Search Engine Optimizations (SEO) tips to increase the rank of their Web documents, to be more visible at the top 10 SERP. This is since gaining more visitors for marketing and commercial goals. This study is a continuation of a series of Arabic Web spam studies conducted by the authors, where this study is dedicated to build the first Arabic content/link Web spam detection system. This Novel system is capable to extract the set of content and link features of Web pages, in order to build the largest Arabic Web spam dataset. The constructed dataset contains three groups with the following three percentages of spam contents: 2%, 30%, and 40%. These three groups with varying percentages of spam contents were collected through the embedded crawler in the proposed system. The automated classification of spam Web pages used based on the features in the benchmark dataset. The proposed system used the rules of Decision Tree; which is considered as the best classifier to detect Arabic content/link Web spam. The proposed system helps to clean the SERP from all URLs referring to Arabic spam Web pages. It produces accuracy of 90.1099% for Arabic contentbased, 93.1034% for Arabic link-based, and 89.011% in detecting both Arabic content and link Web spam, based on the collected dataset and conducted analysis.

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