We present an investigation analysis approach for mining anonymous email content. The core idea behind our approach is concentrated on collecting various effective features from previous emails for all the possible suspects. The extracted features are then used with several machine learning algorithms to extract a unique writing style for each suspect. A sophisticated comparison between the investigated anonymous email and the suspects writing styles is employed to extract evidence of the possible email sender. Extensive experimental results on a real data sets show the improved performance of the proposed method with very limited number of features.

Several routing algorithms have been proposed for efficient routing in mobile ad hoc networks, most of them consider mobile nodes embedded in two-dimensional environments. However, in reality, these networks are embedded in three-dimensional environments. Usually, two-dimensional routing algorithms have several assumptions that are not valid for three-dimensional spaces. In this article, we propose four different randomized geographic-based routing algorithms that have the following properties: (1) nearly guaranteed delivery rate, by using randomize route to overcome local minimum problems; (2) low overhead, by extracting a virtual backbone of the network and then conducting the routing algorithms over the extracted backbone to decrease the search space; (3) low path dilation, by hybridizing the new algorithms with progress-based routing which have very low path dilation; and (4) works in three-dimensional environment. The first algorithm 3DRanDom chooses the next neighbor randomly from a dominating set of the network (extracted locally). The second algorithm 3DRanDomProb extracts a dominating set and sends to one of the resulted neighbors randomly with more probability for the nodes closer to the destination. The third algorithm G_3DRanDomProb tries to progress as much as possible to the destination, if the progress is not possible, the algorithm switches to 3DRanDomProb. The fourth algorithm G_3DRanDomProb_G uses progress-based routing as much as possible, then it switches to 3DRanDomProb until it overcomes the local minimum problem and then goes back to progress-based routing. We show experimentally that these hybrid randomized routing algorithms on three-dimensional mobile ad hoc networks can achieve nearly guaranteed delivery while discovering routes significantly closer in length to the shortest path and with low overhead.

The main aim of this paper is to identify critical success factors (CSFs) and investigate whether they are the same or not across different project organization structures. The organization structures under the study are: functional, project, and matrix. The study is based on a survey that was conducted on a large number of software projects in Jordan. To rank success factors (SFs) and identify critical ones, we use the importance index of SFs, which is calculated based on the likelihood and impact across different structures. For deeper analysis, we carry out statistical experiments with an ANOVA test and Spearman’s rank correlation test. Analysis results of an ANOVA test partially indicates that the values of the SF importance index are slightly different across the three organization structures. Moreover, the Spearman’s rank correlation test results show a high degree of correlation of the SF importance index between the function and project organization structures and a low degree of correlation between the function and matrix organization structures.

Predicting the reading difficulty level of English texts is a critical process for second language education and assessment. Reading difficulty level is concerned with the problem of matching a reader’s proficiency and the appropriate text. The reading difficulty level or readability assessment is the process for predicting the reading grade level required from an input text or document, which corresponds to the reader and to the materials. Students in Jordan at their academic levels find obstacles in finding relevant readable data for any subject at their levels. This paper is intended to introduce a model that foretells the reading difficulty level of a given text in terms of a student's ability to read and understand English as a non-native English speaker in Jordanian schools. In this paper, Jordanian students were classified into four categories according to their knowledge of English. The prediction of the reading difficulty level is achieved by using a modern statistical model that is situated on the Bayes model. The model compares the given text with some standard predefined text that strongly reflects the ability to read and understand English text. The accuracy of the proposed model was tested using the hold-out method. The overall prediction accuracy was 75.9%.