Abstract With the explosive growth of the World Wide Web, Information Retrieval Systems (IRS) have recently become a focus of research. Query expansion is defined as the process of supplementing additional terms or phrases to the original query to improve the information retrieval performance. Arabic is highly inflectional and derivational language which makes the query expansion process a hard task. In this paper, the well known approach, Pseudo Relevance Feedback (PRF) is adopted to be applied on Arabic. Prior to applying PRF, first; datasets (three collections of Arabic documents) are pre-processed to create documents inverted index vocabularies, then, the normal indexing process is carried out. The PRF is applied to create a modified (expanded) query of the original one and the target collection is indexed once more. To judge the enhancement of retrieval process, the results of normal indexing and those of applying PRF are evaluated against each other using precision and recall measures. The results have shown that the PRF method has significantly enhanced the performance of the Arabic Information Retrieval (AIR) System. As the number of expansion terms increases up to a certain extent (35 terms), the performance has been improved. On the other hand, the performance will not be affected, or grow insignificantly as the number of expansion terms exceeds this limit. 

Abstract: Nowadays, enormous amounts of accessible textual information available on the Internet are phenomenal. Automatic text classification is considered an important application in natural language processing. It is the process of assigning a document to predefined categories based on its content. In this paper, the well-known Centroid-based technique developed for text classification is considered to be applied on Arabic text. Arabic language is highly inflectional and derivational which makes text processing a complex and challenging task. In the proposed work, the Centroid-based Algorithm is adopted and adapted to be applied to classify Arabic documents. The implemented algorithm is evaluated using a corpus containing a set of Arabic documents. The experimental results against a dataset of 1400 Arabic text documents covering seven distinct categories reveal that the adapted Centroid-based algorithm is applicable to classify Arabic documents. The performance criteria of the implemented Arabic classifier achieved roughly figures of 90.7%, 87.1%, 88.9%, 94.8%, and 5.2% of Micro-averaging recall, precision, F measure, accuracy, and error rates respectively.

Abstract— Automatic text categorization is considered an important application in natural language processing. It is the process of assigning a document to predefined categories based on its content. In this research, some well-known techniques developed for classifying English text are considered to be applied on Arabic. This work focuses on applying the well-known Rocchio (Centroid-based) technique on Arabic documents. This technique uses centroids to define good class boundaries. The centroid of a class c is computed as center of mass of its members. Arabic language is highly inflectional and derivational which makes text processing a complex task. In the proposed work, first Arabic text is preprocessed using tokenization and stemming techniques. Then, the Rocchio Algorithm is adopted and adapted to be applied to classify Arabic documents. The implemented algorithm is evaluated using a corpus containing a set of actual documents. The results show that the adapted Rocchio algorithm is applicable to categorize Arabic text. Ratios of 92.2%, 92.7%, and 92.1% of Micro-averaging recall, precision, and F-measure respectively are achieved, against a data set of 500 Arabic text documents covering five distinct categories.

Network layer indications are not readily available upon a link change; therefore, general dependes on the network layer may introduce unnecessary delays due to network layer signaling for a simple link layer handover. If information could be gathered at link layer to determine the need for network layer signaling, then both the delay and signaling load could be really improved over the current standards of Mobile IP.

This paper presents a Cross-layer decision on two layer network and link layers to improve the performance of Enhanced Mobile IP (E-Mobile IP) handover in which reducing packet loss and latency during handover process.

 In this paper, English language will be used as an instance of natural languages as we will be concerned with the set of all natural language texts. this research tries to employ a set of all synonyms as a way to hide secret message inside a natural language text. 

 Abstract: The paper presents an adaptive intelligent control method to overcome effects of some indeterminate and undealt factors that a DC .drive is suffered. In the speed loop, we use a three-layer neural networks through a backpropagation (BP) algorithm out of line learning to realize the fuzzy-control tactics. We use unit neuron through Hebb algorithm on-line dynamic learning to realize adaptive mechanism. The simulation is based on a MATLAB6.0 neural networks toolbox with simulink. The results of the simulation show that adaptive intelligent control method enables the system to have good dynamic and stability performance. The proposed method develops the use of simulink in the field of electrical drive of adaptive intelligent control.

Handover latency is the primary cause of packet loss resulting in performance degradation of standard Mobile IPv6. Mobile IPv6 with fast Handover enables a Mobile Node (MN) to quickly detect at the IP layer that it has moved to a new subnet by receiving link-related information from the link-layer; furthermore it gathers anticipative information about the new Access Point (AP) and the associated subnet prefix when the MN is still connected to the previous Corresponding Node (CN).

This paper proposes an enhancement to Fast Mobile IPv6 handover (FMIPv6), based on link layer information, we also present performance evaluations in terms of the packet loss and handover latency using evaluation models.