Importance of Arabic language stemming algorithm is not less than that of other languages stemming in Information Retrieval (IR) field. Lots of algorithms for finding the Arabic root are available and they are mainly categorized under two approaches which are light (stem)-based approach and root-based approach. The latter approach is somehow better than the first approach. A new root-based stemmer is proposed and its performance is compared with Khoja stemmer which is the most efficient root-based stemmers. The accuracy ratio of the proposed stemmer is (99.7) with a difference (1.9) with Khoja stemmer.

In this study, we have created a new Arabic dataset annotated according to Ekman’s basic emotions (Anger, Disgust, Fear, Happiness, Sadness and Surprise). This dataset is composed from Facebook posts written in the Iraqi dialect. We evaluated the quality of this dataset using four external judges which resulted in an average inter-annotation agreement of 0.751. Then we explored six different supervised machine learning methods to test the new dataset. We used Weka standard classifiers ZeroR, J48, Naïve Bayes, Multinomial Naïve Bayes for Text, and SMO. We also used a further compression-based classifier called PPM not included in Weka. Our study reveals that the PPM classifier significantly outperforms other classifiers such as SVM and N

    Keywords provide the reader with a summary of the contents of the document and play a significant role in information retrieval systems, especially in search engine optimization and bibliographic databases. Furthermore keywords help to classify the document into the related topic. Keywords extraction included manual extracting depends on the content of the document or article and the judgment of its author. Manual extracting of keywords is costly, consumes effort and time, and error probability. In this research an automatic Arabic keywords extraction model based on deep learning algorithms is proposed. The model consists of three main steps: preprocessing, feature extraction and classification to classify the document

Stemming is a pre-processing step in Text mining applications as well as it is very important in most of the Information Retrieval systems. The goal of stemming is to reduce different grammatical forms of a word and sometimes derivationally related forms of a word to a common base (root or stem) form like reducing noun, adjective, verb, adverb etc. to its base form. The stem needs not to be identical to the morphological root of the word; it is usually sufficient that related words map to the same stem, even if this stem is not in itself a valid root. As in other languages; there is a need for an effective stemming algorithm for the indexing and retrieval of Arabic documents while the Arabic stemming algorithms are not widely available.

Arabic calligraphy is one of the ancient arts rooted in history, And that he grew up conflicting views and writings addressed as a, communication tool for the linguistic The teaching calligraphy note an art and science because it depends on the fixed assets and precise rules in his art because centered Beauty It targets teach Arabic calligraphy speed as the education and recitation helps to write fast Which have great interest in the field of education and in life both Also accompanied Arabic calligraphy and scientific renaissance significant knowledge in the Ara

Abstract The study aimed at reviewing translation theories proposed to address problems in translation studies. To the end, translation theories and their applications were reviewed in different studies with a focus on issues such as critical discourse analysis, cultural specific items and collocation translation.

The speech recognition system has been widely used by many researchers using different
methods to fulfill a fast and accurate system. Speech signal recognition is a typical
classification problem, which generally includes two main parts: feature extraction and
classification. In this paper, a new approach to achieve speech recognition task is proposed by
using transformation techniques for feature extraction methods; namely, slantlet transform
(SLT), discrete wavelet transforms (DWT) type Daubechies Db1 and Db4. Furthermore, a
modified artificial neural network (ANN) with dynamic time warping (DTW) algorithm is
developed to train a speech recognition system to be used for classification and recognition
purposes. T

Gum Arabic is a natural gummy exudate gained from the trees of Acacia species (Acacia senegal and Acacia seyal), Family: Fabaceae. Gum Arabic considers as a dietary fiber with a high percentage of carbohydrates and low protein content. Sugars arabinose and ribose were originally discovered and isolated from gum Arabic and it is representing the original source of these sugars. A gum emanation from trees occurs under stress conditions such as heat, poor soil fertility, drought, and injury. Mainly gum is produced in belt region of Africa, mainly Sudan, Chad, and Nigeria. In the food industry, it is used in confectionery; in the pharmaceutical industry, it is used as emulsifier, film coating and others. Traditionally the g

The study aimed to identify the awareness level of hidden curriculum among field education students, College of Education, Hail University. To identify the different awareness level of hidden curriculum among field education students depending on specialization variable, and grade variable in curriculum and instruction course, a descriptive survey method was used. The study sample consisted of (182) students: 78 students in Islamic Culture major, 46 primary major and 58 in physical education. To achieve the study objectives, a hidden curriculum awareness scale was designed included (36) items. The study results showed: the awareness level of hidden curriculum among students of field education, Education College, Hail University, achieved