This study has dealt with, the issue of classification of rural road network , in addition to prepare a suggested for the classification for this network in Iraq , this classification account , the specifications and characteristics of rural roads, population, and the range taking of settlements , then this classification was applied on the rural road network in the Najaf province there are four categories of classification ,the first is major arterial rural roads divided into two major arterial and minor arterial roads , while the second category collected roads which was divided into minor arterial roads and main collected roads. The third category was represented by Local Roads , it has been divided into paved roads and unpaved, the f

In the present work, pattern recognition is carried out by the contrast and relative variance of clouds. The K-mean clustering process is then applied to classify the cloud type; also, texture analysis being adopted to extract the textural features and using them in cloud classification process. The test image used in the classification process is the Meteosat-7 image for the D3 region.The K-mean method is adopted as an unsupervised classification. This method depends on the initial chosen seeds of cluster. Since, the initial seeds are chosen randomly, the user supply a set of means, or cluster centers in the n-dimensional space.The K-mean cluster has been applied on two bands (IR2 band) and (water vapour band).The textural analysis is used

Diabetic retinopathy is an eye disease in diabetic patients due to damage to the small blood vessels in the retina due to high and low blood sugar levels. Accurate detection and classification of Diabetic Retinopathy is an important task in computer-aided diagnosis, especially when planning for diabetic retinopathy surgery. Therefore, this study aims to design an automated model based on deep learning, which helps ophthalmologists detect and classify diabetic retinopathy severity through fundus images. In this work, a deep convolutional neural network (CNN) with transfer learning and fine tunes has been proposed by using pre-trained networks known as Residual Network-50 (ResNet-50). The overall framework of the proposed

In recent years, with the rapid development of the current classification system in digital content identification, automatic classification of images has become the most challenging task in the field of computer vision. As can be seen, vision is quite challenging for a system to automatically understand and analyze images, as compared to the vision of humans. Some research papers have been done to address the issue in the low-level current classification system, but the output was restricted only to basic image features. However, similarly, the approaches fail to accurately classify images. For the results expected in this field, such as computer vision, this study proposes a deep learning approach that utilizes a deep learning algorithm.

This study is to evaluate plasma levels of several cytokines in preeclamptic pregnancies compared to those of healthy pregnancies. Ninety pregnant women with preeclampsia (37 mild & 53 severe) and thirty healthy pregnant women were enrolled in the study. Blood samples were taken and plasma levels of IL-8, IL-10, and IFN-? were measured by enzyme-linked immunosorbent assay (ELISA). Preeclamptic women and their severe cases but not mild cases had significantly (P<0.05) increased levels of plasma IL-8, and IFN-? as compared with healthy pregnancies. By contrast, plasma levels of IL-10 was significantly (P<0.05) increased in healthy pregnant women as compared to all groups of preeclampsia. Preeclampsia is associated with an imbalance betwe

The mean age of AS patients was (35.0 ± 9.8) years.When the patients and control subjects were divided into different age groups (>40, 30-40, <30 years), the differences were not significantin terms of disease prevalence. The results also showed that the percentage of male patients is higher than that of females. There was no significant difference (P?0.05) between patients and controls in the distribution of males and females.Most of the patients had the disease for a period of 5 years or higher, with a disease severity of ? 2.1 and functional disability degree of I, II. The resultsshoweddifferent patterns of distribution for the three tested cytokines. A significant increase in the level of TNF-?, anon-significantincrease i

The features of strategic thought transformation in Russia based on attempt confirm the Russian influenced on Region of Eastern Europe through series of common defense treaties and mutual security between USSR and another states this treaties emphasis size on direct Russian supervision on these states in addition, Russia in trusted to formation communism regimes in its vital space, Russia inherited the status of former USSR in political and economic levels.

           Shortly we can see that Russia witnessed large Transformation in political strategic thought as a result of change in internal strategic environment and change of nature understanding which influenced on Russia. This

Calcifying epithelial odontogenic tumour (CEOT) is a benign odontogenic neoplasm of epithelial origin that secretes an amyloid‐like protein tending towards calcification. This study aims to describe a case series from Iraq of one of the rarest odontogenic tumours.