In the present survey 18 species of endo and ecto-parasites were recorded during the examination of 50 Mus musculus (Linnaeus, 1758) among 10 localities in Erbil city, of which 7 species were protozoan and as follows : Chilomastix bettencourti (da Fonseca 1915)82%; Giardia muris (Filice, 1952) 68%; Tritrichomonas muris (Grassi,1879)36%; Entamoeba histolytica (Schaudinn,1903) 24%; Entamoeba coli (Grassi,1879)32%; Eimeria sp. 28% and Trypanosoma musculi (Kendall,1906)2%; and 8 species were helminthes as follows: 4 Cestodes: Rodentolepis nana (von Siebold, 1852) 8%; Hymenolepis diminuta (Rudolphi, 1819)2%; larval stage of Echinococcus granulosus (Batsch, 1786)8%, Cysticercus fasciolaris (Rudolphi, 1808)6%, 4 Nematodes: Aspiculuris tetrapter

In this research, the region in the south-west of Iraq is classified using a fuzzy inference system to estimate its desertification degree. Three land cover indices are used which are the Normalized Difference Vegetation Index, Normalized Multi-Band Drought Index and the top of atmosphere surface temperature to build a fuzzy decision about the desertification degree using eight decision roles. The study covers a temporal period of 38 years, where about every 10 years a sample is elected to verify the desertification status of the region, starting from 1990 to 2018. The results show that the desertification status varied every 10 years, wherein 2000 encountered the highest desertification in the south-west of Iraq.

A QR code is a type of barcode that can hold more information than the familiar kind scanned at checkouts around the world. The “QR” stands for “Quick Response”, a reference to the speed at which the large amounts of information they contain can be decoded by scanners. They are being widely used for advertising campaigns, linking to company websites, contest sign-up pages and online menus. In this paper, we propose an efficient module to extract QR code from background and solve problem of rotation in case of inaccurate image taken from mobile camera.

    Literature reviews of reports concerning the parasitic fauna of fishes of Al-Diwaniyah province, Iraq till the end of December 2018 showed that a total of 43 parasite species are so far known from 13 valid fish species investigated for parasitic infections. The parasitic fauna included one euglenozoan, two myzozoans, six ciliophorans, three myxozoans, three trematodes, nine monogeneans, four cestodes, six nematodes, three acanthocephalans and six crustaceans. The infection with the trematodes, one monogenean, two cestodes and one nematode occurred with larval stages, while the remaining infections were either with trophozoites or adult parasites. Among the inspected fishes, Carasobarbus luteus was infected wit

This work deals with the description of histopathological effects of the nematode Hartertia
gallinarurn Theiler. 1919 on the digestive system of the seesee partridge collected from Qa’ra
area in the western desert district of Iraq. along with some notes on intensity fluctuation of the
parasite according to the seasons. It is found that the major effects of the nematode are
necrosis and fibrosis of gizzard: granulomatous reaction. necrosis and mononuclear
infiltration of proventriculus: damage of mucosal lining of intestine and lymphocytic
infiltration of liver.

The precise classification of DNA sequences is pivotal in genomics, holding significant implications for personalized medicine. The stakes are particularly high when classifying key genetic markers such as BRAC, related to breast cancer susceptibility; BRAF, associated with various malignancies; and KRAS, a recognized oncogene. Conventional machine learning techniques often necessitate intricate feature engineering and may not capture the full spectrum of sequence dependencies. To ameliorate these limitations, this study employs an adapted UNet architecture, originally designed for biomedical image segmentation, to classify DNA sequences.The attention mechanism was also tested LONG WITH u-Net architecture to precisely classify DNA sequences

Traffic classification is referred to as the task of categorizing traffic flows into application-aware classes such as chats, streaming, VoIP, etc. Most systems of network traffic identification are based on features. These features may be static signatures, port numbers, statistical characteristics, and so on. Current methods of data flow classification are effective, they still lack new inventive approaches to meet the needs of vital points such as real-time traffic classification, low power consumption, ), Central Processing Unit (CPU) utilization, etc. Our novel Fast Deep Packet Header Inspection (FDPHI) traffic classification proposal employs 1 Dimension Convolution Neural Network (1D-CNN) to automatically learn more representational c

         The economy is exceptionally reliant on agricultural productivity. Therefore, in domain of agriculture, plant infection discovery is a vital job because it gives promising advance towards the development of agricultural production. In this work, a framework for potato diseases classification based on feed foreword neural network is proposed. The objective of this work  is presenting a system that can detect and classify four kinds of potato tubers diseases; black dot, common scab, potato virus Y and early blight based on their images. The presented PDCNN framework comprises three levels: the pre-processing is first level, which is based on K-means clustering algorithm to detect the infected area from potato image. The s