Developing a new adaptive satellite images classification technique, based on a new way of merging between regression line of best fit and new empirical conditions methods. They are supervised methods to recognize different land cover types on Al habbinya region. These methods should be stand on physical ground that represents the reflection of land surface features.      The first method has separated the arid lands and plants. Empirical thresholds of different TM combination bands; TM3, TM4, and TM5 were studied in the second method, to detect and separate water regions (shallow, bottomless, and very bottomless). The Optimum Index Factor (OIF) is computed for these combination bands, which realized

Abstract

 Metal cutting processes still represent the largest class of manufacturing operations. Turning is the most commonly employed material removal process. This research focuses on analysis of the thermal field of the oblique machining process. Finite element method (FEM) software DEFORM 3D V10.2 was used together with experimental work carried out using infrared image equipment, which include both hardware and software simulations. The thermal experiments are conducted with AA6063-T6, using different tool obliquity, cutting speeds and feed rates. The results show that the temperature relatively decreased when tool obliquity increases at different cutting speeds and feed rates, also it

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

In the image processing’s field and computer vision it’s important to represent the image by its information. Image information comes from the image’s features that extracted from it using feature detection/extraction techniques and features description. Features in computer vision define informative data. For human eye its perfect to extract information from raw image, but computer cannot recognize image information. This is why various feature extraction techniques have been presented and progressed rapidly. This paper presents a general overview of the feature extraction categories for image.

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 present research aims at revealing the advertising image semiotics in the American printed poster by following the image's significance and its transformations through the poster design trends and indicating its nature whether it is an explicit or implicit image. The limits of the research were the American printed poster during 2016-2018 period. The theoretical side was determined by two sections, the first: (the advertising image semiotics) and the second (design trends in the printed poster). The research procedures were represented by the research method adopted in the analysis of the sample models identified in four models taken from the research community which contains (24) models. The selection was made according to the trend

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 growth of developments in machine learning, the image processing methods along with availability of the medical imaging data are taking a big increase in the utilization of machine learning strategies in the medical area. The utilization of neural networks, mainly, in recent days, the convolutional neural networks (CNN), have powerful descriptors for computer added diagnosis systems. Even so, there are several issues when work with medical images in which many of medical images possess a low-quality noise-to-signal (NSR) ratio compared to scenes obtained with a digital camera, that generally qualified a confusingly low spatial resolution and tends to make the contrast between different tissues of body are very low and it difficult to co