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

The issue of image captioning, which comprises automatic text generation to understand an image’s visual information, has become feasible with the developments in object recognition and image classification. Deep learning has received much interest from the scientific community and can be very useful in real-world applications. The proposed image captioning approach involves the use of Convolution Neural Network (CNN) pre-trained models combined with Long Short Term Memory (LSTM) to generate image captions. The process includes two stages. The first stage entails training the CNN-LSTM models using baseline hyper-parameters and the second stage encompasses training CNN-LSTM models by optimizing and adjusting the hyper-parameters of

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

          The subject of marketing culture and mental image is one of the important topics in the field of management. There is no study that combines these two variables. The research is important because of the increasing importance of the subject. The future direction of the company in question will support the company's economic and marketing responsibilities. And reflect the company's mental image, as a culture that contributes to changing the reality of the organization investigated by polling the views of a sample of managers in the General Company for Vegetable Oil Industry, which (30) out of the (65) individual, and There are two hypotheses of research: There is a significant

For more than seven decades, the Palestinian-Israeli conflict has not ended, and every one claims his right to the land, waters, airspace and wealth of Palestine. This conflict was reflected in the political, military, social and cultural aspects, and the cinema had a prominent role in conveying messages to the world about this conflict. On the geopolitical dimensions and their impact on the cinematic image in the films of the Palestinian cause), the researchers divided the research into four frameworks that came as follows: (methodological framework) and included the research problem, its importance, research objectives, research limits and sealing the chapter by defining the terms. And (theoretical framework): it was divided into two s

Two unsupervised classifiers for optimum multithreshold are presented; fast Otsu and k-means. The unparametric methods produce an efficient procedure to separate the regions (classes) by select optimum levels, either on the gray levels of image histogram (as Otsu classifier), or on the gray levels of image intensities(as k-mean classifier), which are represent threshold values of the classes. In order to compare between the experimental results of these classifiers, the computation time is recorded and the needed iterations for k-means classifier to converge with optimum classes centers. The variation in the recorded computation time for k-means classifier is discussed.

CuO nanoparticles were synthesized in two different ways, firstly by precipitation method using copper acetate monohydrate Cu(CO₂CH₁₃)₂·H2O, glacial acetic acid (CH3COOH) and sodium hydroxide(NaOH), and secondly by sol-gel method using copper chloride(CuCl₂), sodium  hydroxide (NaOH) and ethanol (C₂H₆O). Results of scanning electron microscopy (SEM) showed that different CuO nanostructures (spherical and Reef) can be formed using precipitation and sol- gel process, respectively, at which the particle size was found to be less than 2 µm. X-ray diffraction (XRD)manifested that the pure synthesized powder has no inclusions that may exist during preparations. XRD result

Segmentation is the process of partition digital images into different parts depending on texture, color, or intensity, and can be used in different fields in order to segment and isolate the area to be partitioned. In this work images of the Moon obtained through observations in Astronomy and space dep. College of science university of Baghdad by ( Toward space telescopes and widespread used of a CCD camera) . Different segmentation methods were used to segment lunar craters. Different celestial objects cause craters when they crash into the surface of the Moon like asteroids and meteorites. Thousands of craters appears on the Moon's surface with ranges in size from meter to many kilometers, it provide insights into the age and ge