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.

    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

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

The Arabic Grammar between Originality and Sufficiency

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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

Objective: To identify feeding problems of children with congenital heart disease.
Methodology: Non probability (purposive) sample of (65) were selected of 225 children who visit Al Nasiriya
heart center during the period of conducting the pilot study, previously diagnosed with congenital heart
disease.
Results: The study results indicated that children with congenital heart disease have feeding difficulties, low
birth weight , repeated diarrhea , more than half of the sample taking medication for heart disease which cause
repeated vomiting, difficulty taking liquids and refusal of feeding or eating.(64.6%) of study sample suffered
from wasting. (78.5%) suffered from stunting. Almost half of the study sample suffered

Due to its importance in physics and applied mathematics, the non-linear Sturm-Liouville problems
witnessed massive attention since 1960. A powerful Mathematical technique called the Newton-Kantorovich
method is applied in this work to one of the non-linear Sturm-Liouville problems. To the best of the authors’
knowledge, this technique of Newton-Kantorovich has never been applied before to solve the non-linear
Sturm-Liouville problems under consideration. Accordingly, the purpose of this work is to show that this
important specific kind of non-linear Sturm-Liouville differential equations problems can be solved by
applying the well-known Newton-Kantorovich method. Also, to show the efficiency of appl

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