Language Teaching & Leaning Problems at the Iraqi university level: Image & Reality

The present study examines critically the discursive representation of Arab immigrants in selected American news channels. To achieve the aim of this study, twenty news subtitles have been exacted from ABC and NBC channels. The selected news subtitles have been analyzed within van Dijk’s (2000) critical discourse analysis framework. Ten discourse categories have been examined to uncover the image of Arab immigrants in the American news channels. The image of Arab immigrants has been examined in terms of five ideological assumptions including "us vs. them", "ingroup vs. outgroup", "victims vs. agents", "positive self-presentation vs. negative other-presentation", and "threat vs. non-threat". Analysis of data reveals that Arab immig

Most companies use social media data for business. Sentiment analysis automatically gathers analyses and summarizes this type of data. Managing unstructured social media data is difficult. Noisy data is a challenge to sentiment analysis. Since over 50% of the sentiment analysis process is data pre-processing, processing big social media data is challenging too. If pre-processing is carried out correctly, data accuracy may improve. Also, sentiment analysis workflow is highly dependent. Because no pre-processing technique works well in all situations or with all data sources, choosing the most important ones is crucial. Prioritization is an excellent technique for choosing the most important ones. As one of many Multi-Criteria Decision Mak

Objectives: The purpose of this in vitro study was to compare the effect of adding a poloxamer surfactant to the irrigant solutions on its cleaning efficiency. Design: In this study the roots of extracted permanent premolar teeth were used and evaluated by using Scanning Electronic Microscopy (SEM). Materials and Method: 72 human single tooth of permanent premolar (8 for each group) were used in this in vitro study. Roots after sectioning at cervical area to get 15 mm were embedded in a plastic container filled with impression silicon, then instrumented with ProTaper rotary instruments till size F4. Each group (8 root) were irrigated with one of the nine solutions used in study: three concentrations of NaOH [5% (A1), 2.5%(A2), 0.5%(A3)], th

In this work, thermodynamic efficiency of individual cell and stack of cells (two cells) has been computed by studying the variation of voltage produced during an operation time of 30 min as a result of the affected parameters:- stoichiometric feed ratio, flow field design on single cell and feed distribution on stack of cells. The experiments were carried out by using two cells, one with serpentine flow field and the other with spiral flow field. These cells were fed with hydrogen and oxygen at low volumetric flow rates from 1 to 2 ml/sec and stoichiometric ratios of fuel (H₂) to oxidant (O₂) as 1:2, 1:1 and 2:1 respectively. The results showed that

The present work initiated to eaIuate the efficiency of Al-Rustamivah sewage treatment plant
as reflected by the quality of final effluent that is thrown to Di ala river. Weekly samples of
wastewater and final effluent were collected between November 1994 and end of January
1995 and analyzed for different chemical and biological features. Results ha e inidicated that
Al-Rustamiyah sewage treatment plant could not be able efficiently to purify the raw sewage.
The mean values of suspended solids. BOD. COD Dichromate and Oil & grease effluents
were felt to pass standard limits (98.4. 92.8. 125.2 and 39.1 ppm. respectiel). The atherse
possible effects of pollution on Diuala equatic life hae been also discussed in res

The field of Optical Character Recognition (OCR) is the process of converting an image of text into a machine-readable text format. The classification of Arabic manuscripts in general is part of this field. In recent years, the processing of Arabian image databases by deep learning architectures has experienced a remarkable development. However, this remains insufficient to satisfy the enormous wealth of Arabic manuscripts. In this research, a deep learning architecture is used to address the issue of classifying Arabic letters written by hand. The method based on a convolutional neural network (CNN) architecture as a self-extractor and classifier. Considering the nature of the dataset images (binary images), the contours of the alphabet