Mobile augmented reality is growing rapidly because of the growth of smartphones. Due to the portable

nature of smartphones, mobile augmented reality devices have become the most widely deployed consumer

augmented reality display device and show promise for becoming the first commercial success for augmented

reality technologies. The role of the user is identified and forwarded to the respective module. Cognitive

engagement and interactivity are the main two factors of influenced learning where the engagements are focused in

Learnability identification module. When a common, single optical tracking platform is available the reachability

and performance can also be increased without affecting the efficiency, which will be done in AllinoneAR Module.

An integrated framework consisting of three above stated modules is proposed in this paper

l security perspective of Cloud computing with the aim to highlight the problem from the cloud stakeholders' perspective, and the cloud service delivery models perspective. The paper aims also to introduce the mechanisms used to secure cloud computing applications as well as to compare some providers of cloud computing field with each other in general and security aspects. 

The continuous call on the increase of the quality of teaching put more pressure on academics to

draw on students experiences and propose new approaches for delivering information and increasing the

standards of teaching excellence. There are several generalized approaches to the scholarship of teaching and

learning such as continuous professional development and learning by developing, however cooperative

learning combined with project-based learning can be used in a range of disciplines in graduate studies such

as Information Technology. From our experience in teaching: e-commerce subject and web application

security course we found that such a strategy encourages lecturer to continue improve the curriculum and

delivery process leading to better learning experience for the students. This paper reports on the practical

nature of cooperative learning and how to use it to bridge Teaching and Learning as well as Theory and

Application. Our purpose is to report on cooperative learning in graduate studies using Project-based learning

strategy. It demonstrates the value of student empowerment and leadership in autonomous project groups.

The strategy is designed primarily to increase student engagement and improve the learning process. We

evaluate the success of the strategy by evaluating student attendance and active participation in classroom

discussions, learning outcomes, and student results. The success of the strategy encourages us to incorporate

the project-based approach on more than one course

neural network is considered as a nonlinear dynamic system consisting of a large number of simple processing elements interconnected in some manner with adjustable weighted strength. Neural networks provide qualitative and quantitative (analog, digital or logical) knowledge through information coding and decoding, and have powerful functions in learning and selforganization. These properties make neural networks considered to be more powerful in dealing with numerical data than other artificial intelligent systems like expert systems. However, the performance of neural networks depends deeply in number of factors including transfer function, number of hidden layers, number of nodes in hidden layers, input function, and weight function. In this article we present a comparative study of these factors and how they influence the performance of a system. 

This paper presents empirical methods for enhancing the accuracy of inductive learning systems. It addresses the problems of: learning propositional production rules in multi-class classification tasks in noisy domains, maintaining continuous learning when confronted with new situation after the initial learning phase is completed, and classifying an object when no rule is satisfied for it.

It is shown that interleaving the learning and performance-evaluation process allows accurate classifications to be made on real-world data sets. The paper presents the system ARIS which implements this approach, and it is shown that the resulting classifications are often more accurate than those made by the non-refined knowledge bases.

The core design decision that lies behind ARIS is that it employs an ordering of the rules according to their weight. A rule’s weight is learned by using Bayes’ theorem to calculate weights for the rule’s conditions and combining them. This model focuses the analyses of the knowledge base and assists the refinement process significantly.

The system is non-interactive, it relies on heuristics to focus the refinement on those experiments that appear to be most consistent with the refinement data set. The design framework of ARIS consists of tabular model for expressing rule weights, and the relationship between refinement cases and the rules satisfied for each case to focus the refinement process. The system has been used to refine knowledge bases created by ARIS itself, as well as to refine knowledge bases created by the RIPPER and C4.5 systems in ten selected domains.

Fuzzy decision forests aim to improve the predictive power of single fuzzy decision trees by allowing multiple views of the same domain to be modelled. Such forests have been successfully created for classification problems where the outcome field is discrete; however predicting a continuous output value is more challenging in combining the output from multiple fuzzy decision trees. This paper presents a new approach to creating fuzzy regression tree forests based upon the induction of multiple fuzzy regression decision trees from one training sample, where each tree will represent a different view of the data domain. The singular fuzzy regression trees are induced using a proven algorithm known as Elgasir which fuzzifies crisp CHAID decision trees using trapezoidal membership functions for fuzzification and applies Takagi-Sugeno inference to obtain the final predicted values. A modified version of Artificial Immune System Network model (opt-aiNet) is then used for the simultaneous optimization of the membership functions across all trees within the forest. A strength of the proposed method is that data does not require fuzzification before forest induction this reducing pre-processing time and the need for subjective human experts. Five problem sets from the UCI repository and KEEL repository are used to evaluate the approach. The experimental results have shown that fuzzy regression tree forests reduce the error rate compared with single fuzzy regression trees. 

In this paper we study the use of the Brill tagger [5,6,7] for tagging Modern Standard Arabic (henceforth MSA) text. The Brill tagger is a famous public domain part of speech tagger, originally designed for tagging English text by implementing machine learning approach through the method of transformation rules. IT had been adopted for other languages by many researchers [17,19,22]. Some modifications are needed on the learner and tagger that are written partly in perl and partly in C programming languages, and are run under the unix/linux operating system. The main change is done on the initial state tagger, which is used by both learner and tagger. A program is written using the lexical analyzer Lex to capture Arabic morphological structures, and then interfaced with both learner and tagger. The tagset used in this work is a revised version of that introduced by Khoja [11]. The revision included changing some of the tags for linguistic considerations and introducing some new tags to make the set more powerful, or to make up for limitations in the original tagset that hinder tagging some words. The corpus is obtained from two Jordanian magazines, and has to go through a series of editing steps. A collection of lexical rules and contextual rules are obtained and applied to Arabic text. The tagging accuracy of the resulting tagged text is measured now to be an average of up to about 84% for both known and unknown words, A rate, which is very promising for such a complex language and rich tagset. We still hope for better performance.

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