Political Discourse Analysis is an important linguistic study approach used by politicians to gain people support. The present paper sheds light on the figures of speech  of emphasis in the televised debate between the two presidential elections candidates, Emmanuel Macron and Marine Le Pen and the distinctive effect they add to the political discourse to win general public support as well as the presidential elections.

       The present paper provides a rudimentary definition and an analysis of the terms “discourse” and “political discourse” and traces the significant role played by politically directed televised Media and internet to support political pa

The article deals with the role of metaphors in forming the plot of L. Ulitskaya’s family chronicle “Medea and Her Children”. The author of the article describes the results of the next stage of research related to the works of Lyudmila Evgenievna Ulitskaya, a representative of modern Russian prose. The analysis of tropes and figures in the works written at the turn of the XXth – XXIth centuries is of importance for the study of the modern state of Russian language as an independent system. “Medea and Her Children” is one of the works by L. Ulitskaya (written in 1996), which, like her other works, is characterized by a unique style of narration, rich in vocabulary, lexical, semantic and stylistic diversity of the author’s word

Lung cancer is one of the most serious and prevalent diseases, causing many deaths each year. Though CT scan images are mostly used in the diagnosis of cancer, the assessment of scans is an error-prone and time-consuming task. Machine learning and AI-based models can identify and classify types of lung cancer quite accurately, which helps in the early-stage detection of lung cancer that can increase the survival rate. In this paper, Convolutional Neural Network is used to classify Adenocarcinoma, squamous cell carcinoma and normal case CT scan images from the Chest CT Scan Images Dataset using different combinations of hidden layers and parameters in CNN models. The proposed model was trained on 1000 CT Scan Images of cancerous and non-c

The technique of integrate complimentary details from two or more input images is known as image fusion.  The fusion image is more informational and will be complete more than any of the original input images. This paper Illustrates implementation and evaluation of fusion techniques used on the Satellite images a high-resolution Panchromatic (Pan) and Multispectral (MS). A new algorithm is proposed to fuse a  Pan  and MS  of the lowresolution images based on combining IHS and Haar wavelet transform.Firstly, this paper clarifies the classical fusion by using IHS transform and Haar wavelet transform individually. Secondly proposition new strategy of combining the two methods. Performance of the proposed method is evalua

One of the most important of satellite image is studying the surface water
according of its distribution and depth. In this work, three images have been taken
for Baghdad and surrounding for year (1991, 1999 and 2014) and by using of envi
program has been used. Different classes have been evaluated for Al-Habania and
Al-Razaza River according to its depth and water reflectance. In the present work
four types of water depth (very shallow, shallow, moderate, and deep area) have
been detected.

Microscope images are characterized by a number of specific parameters, the influence of such parameters (intensity, magnification, numerical aperture, diaphragms aperture, segmentation, and edge detecting technique) on measurement in optical microscope images have been determined with using a powerful image processing methods. As one of the most widespread techniques in biological investigation and dynamic process, light compound microscopy has used to analyze the optical properties of biological images. The results indicate that a wide aperture allows maximum resolution and depth of field, but decreases the contrast. While a small aperture improve visibility and contrast but decreases the resolution. The results also show the best perf

One of the most difficult issues in the history of communication technology is the transmission of secure images. On the internet, photos are used and shared by millions of individuals for both private and business reasons. Utilizing encryption methods to change the original image into an unintelligible or scrambled version is one way to achieve safe image transfer over the network. Cryptographic approaches based on chaotic logistic theory provide several new and promising options for developing secure Image encryption methods. The main aim of this paper is to build a secure system for encrypting gray and color images. The proposed system consists of two stages, the first stage is the encryption process, in which the keys are genera

     Marking content with descriptive terms that depict the image content is called “tagging,” which is a well-known method to organize content for future navigation, filtering, or searching. Manually tagging video or image content is a time-consuming and expensive process. Accordingly, the tags supplied by humans are often noisy, incomplete, subjective, and inadequate. Automatic Image Tagging can spontaneously assign semantic keywords according to the visual information of images, thereby allowing images to be retrieved, organized, and managed by tag. This paper presents a survey and analysis of the state-of-the-art approaches for the automatic tagging of video and image data. The analysis in this paper covered the publications

The computer vision branch of the artificial intelligence field is concerned with developing algorithms for analyzing video image content. Extracting edge information, which is the essential process in most pictorial pattern recognition problems. A new method of edge detection technique has been introduces in this research, for detecting boundaries.

           Selection of typical lossy techniques for encoding edge video images are also discussed in this research. The concentration is devoted to discuss the Block-Truncation coding technique and Discrete Cosine Transform (DCT) coding technique. In order to reduce the volume of pictorial data which one may need to store or transmit,

Foreground object detection is one of the major important tasks in the field of computer vision which attempt to discover important objects in still image or image sequences or locate related targets from the scene. Foreground objects detection is very important for several approaches like object recognition, surveillance, image annotation, and image retrieval, etc. In this work, a proposed method has been presented for detection and separation foreground object from image or video in both of moving and stable targets. Comparisons with general foreground detectors such as background subtraction techniques our approach are able to detect important target for case the target is moving or not and can separate foreground object with high det