This study aims to observe and analysis the propaganda discourse image for Daesh, and know how it marketing the fear due to symbols structure, and discover the straight meanings and hidden inspiration, with the ideology that the image presented.
The study is descriptive and qualitative, and the method is analytic survey used semiotic approach.
The most important results of the study refer to:
- Daesh functioning the image in fear manufacture in all it components: the symbol of savageness, body language, color, clothes uniform and professionally shot.
- The indicative meaning of fear promoted by Daesh based of the manufacturing «Holy», and that mean places non-touchable and non-insulted.
- Daesh used in its propagand

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

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

Augmented reality technology is a modern technique used in all fields, including: medicine, engineering and education, and has received attention from officials in the educational process at present; The focus of this research is on the degree of use of augmented reality among field experience students in the project's optimal investment program for teaching staff and their difficulties, applied to a sample of 75 students, through a questionnaire prepared by the researcher as a tool to determine the degree of use, as well as difficulties. The researcher addressed the subject through two main axes to determine the degree of use, as well as the difficulties preventing teachers and learners from using this technique. The results of the rese

The aim of this study was to identify the rate of return of the stock through the financial information disclosed by the financial statements of companies both services and insurance included in Iraqi market for securities . The study used a descriptive statistical methods and the correlation matrix for the independent factors , in addition to a regression model for data  analysis and hypothesis . Model included a number of independent variables , which was measured in the size of company (sales or revenue) , and the leverage , in addition to the structure of assets and the book value of owners'  equity in the company , as well as the general price index .Based on the data of (11)companies and for three years, showed the result

Background: Temporomandibular joint (TMJ) is a compound articulation formed from the articular surfaces of the temporal bone and the mandibular condyle.CBCT imaging of TMJ is that it allows accurate measurements of the volume and surface of the condyle. The aim of the study is to assess the sagittal position of mandibular condyle in patients with temporomandibulardysfunction using Cone Beam Computed Tomography in centric occlusion. Materials and Methods: CBCT images for all patients were obtained in an upright position using New Tom Giano CBCT with different field of view (11 x 8), (11 x 5), and (8 x 8) and exposure factors was changed accordingly using NNT version 5.1 software for sagittal reconstruction, anterior, superior and posterior