This study was conducted to detect C.sakazakii PIF and raw milk. Two hundred samples of PIF were taken from the infected hospital infants who used this type of milk and from the local markets in addition to 16 sample of raw milk were collected. The study is the first to report the isolation of C. sakazakii and Enterobacter spp. from raw milk in Iraq. The distribution of C.sakazakii and Enterobacter spp. among the presumptive isolates using Vitek-GN2 system gave 1/16(6.25%) isolates of C.sakazakii and 4/16 (25%) isolates of Enterobacter spp. Enterobacter spp. isolates include (E.cloacae ssp. cloacae and E.cloacae ssp. dissolvens, E.hormaechei, and E.ludwigii) that isolate from raw milk Differences in between percentages of each isolate perse

Aromatic hydrocarbons present in Iraqi national surface water were believed to be raised principally from combustion of various petroleum products, industrial processes and transport output and their precipitation on surface water.
Polycyclic aromatic hydrocarbons (PAHs) were included in the priority pollutant list due to their toxic and carcinogenic nature. The concern about water contamination and the consequent human exposure have encouraged the development of new methods for
PAHs detection and removal.
PAHs, the real contaminants of petroleum matter, were detected in selected sites along Tigris River within Baghdad City in summer and winter time, using Shimadzu high performance liquid chromatography (HPLC) system.
Analysi

Background: Diabetic nephropathy is a common complication of diabetes mellitus type2. The neutrophil gelatinase Associated lippocallin (NGAL) is an ubiquitous protein consist of 178 amino acid. NGAL can be identified in plasma and urine starting 2- 4 hours after a kidney injury resulting changes in glomerular filtration and tubular reabsorption and with increased secretion in tubular epithelial cells.
Objective: This study aimed to evaluate the role of serum Neutrophil Gelatinse Associated Lipocallin (NGAL) in early detection nephropathy.
Method : This study was conducted in Medical City, Baghdad Teaching Hospital during the period from December 2015to June 2016.The study included (90) subjects with age range between (30 – 56)yea

With the high usage of computers and networks in the current time, the amount of security threats is increased. The study of intrusion detection systems (IDS) has received much attention throughout the computer science field. The main objective of this study is to examine the existing literature on various approaches for Intrusion Detection. This paper presents an overview of different intrusion detection systems and a detailed analysis of multiple techniques for these systems, including their advantages and disadvantages. These techniques include artificial neural networks, bio-inspired computing, evolutionary techniques, machine learning, and pattern recognition.

Most Internet-tomography problems such as shared congestion detection depend on network measurements. Usually, such measurements are carried out in multiple locations inside the network and relied on local clocks. These clocks usually skewed with time making these measurements unsynchronized and thereby degrading the performance of most techniques. Recently, shared congestion detection has become an important issue in many computer networked applications such as multimedia streaming and
peer-to-peer file sharing. One of the most powerful techniques that employed in literature is based on Discrete Wavelet Transform (DWT) with cross-correlation operation to determine the state of the congestion. Wavelet transform is used as a de-noisin

in this paper we adopted ways for detecting edges locally classical prewitt operators and modification it are adopted to perform the edge detection and comparing then with sobel opreators the study shows that using a prewitt opreators

    In this paper, we devoted to use circular shape sliding block, in image edge determination. The circular blocks have symmetrical properties in all directions for the mask points around the central mask point. Therefore, the introduced method is efficient to be use in detecting image edges, in all directions curved edges, and lines. The results exhibit a very good performance in detecting image edges, comparing with other edge detectors results.

Skin detection is classification the pixels of the image into two types of pixels skin and non-skin. Whereas, skin color affected by many issues like various races of people, various ages of people gender type. Some previous researchers attempted to solve these issues by applying a threshold that depends on certain ranges of skin colors. Despite, it is fast and simple implementation, it does not give a high detection for distinguishing all colors of the skin of people. In this paper suggests improved ID3 (Iterative Dichotomiser) to enhance the performance of skin detection. Three color spaces have been used a dataset of RGB obtained from machine learning repository, the University of California Irvine (UCI), RGB color space, HSV color sp

Humans use deception daily since it can significantly affect their life and provide a getaway solution for any undesired situation. Deception is either related to low-stakes (e.g. innocuous) or high-stakes (e.g. with harmful situations). Deception investigation importance has increased, and it became a critical issue over the years with the increase of security levels around the globe. Technology has made remarkable achievements in many human life fields, including deception detection. Automated deception detection systems (DDSs) are widely used in different fields, especially for security purposes. The DDS is comprised of multiple stages, each of which should be built/trained to perform intelligently so that the whole system can give th