Nowadays, e-mail is widely becoming one of the fastest and most economical forms of communication .Thus, the e-mail is prone to be misused. One such misuse is the posting of unsolicited, unwanted e-mails known as spam or junk e-mails. This paper presents and discusses an implementation of an Anti-spam filtering system, which uses a Multi-Layer Perceptron (MLP) as a classifier and a Genetic Algorithm (GA) as a training algorithm. Standard genetic operators and advanced techniques of GA algorithm are used to train the MLP. The implemented filtering system has achieved an accuracy of about 94% to detect spam e-mails, and 89% to detect legitimate e-mails. 

 Abstract The Arabic language presents significant challenges to many natural language processing applications. The broken plu rals (BP) problem is one of these challenges especially for information retrieval applications. It is difficult to deal with Arabic broken plurals and reduce them to their associated singulars, because no obvious rules exist, and there are no standard stemming algorithms that can process them. This paper attempts to handle the problem of broken plural by de veloping a method to identify broken plurals in an unvowelised Arabic text and reducing them to their correct singular forms by incorporating the simple broken plural matching approach, with a machine translation system and an English stemmer as a new approach. A set of experiments has been conducted to evaluate the performance of the proposed method using a number of text samples extracted from a large Arabic corpus (AL-Hayat newspaper). The obtained re sults are analyzed and discussed.

 Abstract There are several stemming approaches that are applied to Arabic language, yet no a complete stemmer for this language is available. The existing stem-based stemmers for stemming Arabic text have a poor performance in terms of accuracy and error rates. In order to improve the accuracy rates of stemming, a hybrid method is proposed for stemming Arabic text to produce stems (not roots). The improvement of the accuracy of stemming will lead by necessity to the improvement of many applications very greatly, including: information retrieval, document classification, machine translation, text analysis and text compression. The proposed method integrates three different stemming techniques, including: morphological analysis, affix-removal and dictionaries. 

The concepts of partial size-and-shape and partial shape are defined, with motivation from a study in humanmovementanalysis. Someco-ordinates for partial shape for landmarks in three dimensions are given, and Gaussian models for the landmark co-ordinates are proposed. The main results involve the derivation of the partial size-and-shape distributions for the isotropic and general multivariate normal models for three-dimensional data. The partial shape distribution is given in the isotropic case. Maximum likelihood based inference is explored, and examples using simulated and real human movement data illustrate the methodology.

We consider the Bayesian analysis of human movement data, where the subjects perform various reaching tasks. A set of markers is placed on each subject and a system of cameras records the three-dimensional Cartesian co-ordinates of the markers during the reaching movement. It is of interest to describe the mean and variability of the curves that are traced by the markers during one reaching movement, and to identify any differences due to covariates. We propose a methodology based on a hierarchical Bayesian model for the curves. An important part of the method is to obtain identifiable features of the movement so that different curves can be compared after temporal warping. We consider four landmarks and a set of equally spaced pseudolandmarks are located in between. We demonstrate that the algorithm works well in locating the landmarks, and shape analysis techniques are used to describe the posterior distribution of the mean curve. A feature of this type of data is that some parts of the movement data may be missing-the Bayesian methodology is easily adapted to cope with this situation.

Diabetic retinopathy is one of the most common diabetic complications, and is a major cause of new blindness in the working-age population of developed countries. Progression of vascular abnormalities, including the selective loss of pericytes, formation of acellular capillaries, thickening of the basement membrane, and increased vascular permeability characterizes early nonproliferative diabetic retinopathy (NPDR). Capillary occlusion, as shown on fluorescein angiograms, is also one of the earliest clinically recognizable lesion of NPDR. In response to capillary non-perfusion, there is dilation of neighbouring capillaries, leading to early blood-retinal barrier breakdown, capillary non-perfusion, and endothelial cell injury and death. The resulting ischemia leads to increased production of growth factors, and the development of proliferative diabetic retinopathy (PDR), which is characterized by growth of new vessels and potential severe and irreversible visual loss. The exact pathogenic mechanism by which capillary non-perfusion occurs is still unclear but growing evidence now suggests that increased leukocyte-endothelial cell adhesion and entrapment (retinal leukostasis) in retinal capillaries is an early event associated with areas of vascular non-perfusion and the development of diabetic retinopathy. The leukocytes in diabetic patients are less deformable more activated, and demonstrate increased adhesion to the vascular endothelium. This review summarizes the current literature on the role of leukocytes in the pathogenesis of capillary occlusion, and discusses the potential of leukostasis as a new promising target in the treatment of diabetic retinopathy.

تمت إضافة ثلاثة أنواع نباتية للفلورا الليبية هي Ruta montana + Pimpinella cretica + plantago bellardii

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