The measurement of natural radioactivity in a given region or country is essential to provide a reference base-line map to follow up a possible variation in future. In order to perform such measurement, the natural radioactivity was measured in different locations. The locations (50 sites) were distributed over Al-Jabal Al-Gharbi Mountain, starting from the city Al-Azeeziah in the eastern part to Wazen on the Tunisian border in the west. The measurements showed obvious variation from one site to another. The levels were fluctuating from (12.8 counts/minute) in Bir-Ayad to (45.7 counts/minute) in Gherian.
In order to investigate the cause for such variation, samples were collected from (27) sites for detailed study. The levels of natur

Medicine is one of the fields where the advancement of computer science is making significant progress. Some diseases require an immediate diagnosis in order to improve patient outcomes. The usage of computers in medicine improves precision and accelerates data processing and diagnosis. In order to categorize biological images, hybrid machine learning, a combination of various deep learning approaches, was utilized, and a meta-heuristic algorithm was provided in this research. In addition, two different medical datasets were introduced, one covering the magnetic resonance imaging (MRI) of brain tumors and the other dealing with chest X-rays (CXRs) of COVID-19. These datasets were introduced to the combination network that contained deep lea

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

The research deals with a very important topic, which is social security viewed in the context of criminal protection for state security and the challenges it faces after a decisive change in the methods of war. The research also presents a different division of the generations of wars. We limit ourselves to four of them based on the change in the strategic war objectives and not just the means of committing them. This is because these means are not suitable for describing the real changes in the patterns of wars and the goals that it seeks to achieve. The research stresses the importance of putting the concept of state security in its correct framework, which is part of social security, so that the interest of the political system and the