A microbial study conducted for a number of flour samples (30 samples) Uses in the bakery ovens in various areas of the city of Baghdad, by used the conventional methods used in laboratories in microbial tests and compared with the modern techniqueby usedof BacTrac Device 3400 equipped from SY-LAB Impedance analysersAustrian company.The results of two ways showed (The conventional way and BacTrac Device test)that the total counts of aerobic bacteria, coliform bacteria, StaphylococcusSpp. bacteria, Bacillus cereus bacteria and yeasts and molds,Most of them were within the permissible borders in the Iraqi standard for grain and its products With free samples from SalmonellaSpp. bacteria, and that the screening by BacTrac device are shorten

We propose a system to detect human faces in color images type BMP by using two methods RGB and YCbCr to determine which is the best one to be used, also determine the effect of applying Low pass filter, Contrast and Brightness on the image. In face detection we try to find the forehead from the binary image by scanning of the image that starts in the middle of the image then precedes by finding the continuous white pixel after continuous black pixel and the maximum width of the white pixel by scanning left and right vertically(sampled w) if the new width is half the previous one the scanning stops.

This study was conducted to estimate some heavy metals cadmium, lead, nickel and iron in 15 samples of Iraqi honey with 3 replicates for each sample which were collected from apiaries near potential contamination areas in five Iraqi governorates, including Baghdad, Karbala, Babylon, Diyala and Salah al-Din. The atomic absorption technique was used to estimate the concentrations of heavy metals, the results showed that there were significant differences at (P≤0.05) between the concentrations of these elements in the honey samples, the highest concentrations of cadmium 0.123 mg/kg were recorded in Baghdad, near the petrochemical production complex, lead 4.657 mg/kg and nickel 0.023 mg/kg in Babylon near the power plant, iron was

This study has been performed to study the inhibitory effects of crude plant extracts of Bay (laurus nobilis) leaves against some bacterial isolates represented by Staphylococcus aureus, Staphylococcus epidermids, Proteus vulgaris, Bacillus subtilis, Escherichia coli, and Pseudomonas aeroginosa in vitro. The results showed that percentages of essential chemical of laurus nobilis leaves which represented by moisture, total oil, total ash, crude protein, crude fibers, carbohydrites and caloric values in dry weight are 5.96, 4.28, 14.2, 8.75, 24.8, 76.99%, and 284.92 kcal/100g respectively, the percentages of some major and minor mineral elements of laurus nobilis leaves powder which represented by Mg, Fe, Cu, Pb, Cd and As, are: 0.211, 0.1

Generally, radiologists analyse the Magnetic Resonance Imaging (MRI) by visual inspection to detect and identify the presence of tumour or abnormal tissue in brain MR images. The huge number of such MR images makes this visual interpretation process, not only laborious and expensive but often erroneous. Furthermore, the human eye and brain sensitivity to elucidate such images gets reduced with the increase of number of cases, especially when only some slices contain information of the affected area. Therefore, an automated system for the analysis and classification of MR images is mandatory. In this paper, we propose a new method for abnormality detection from T1-Weighted MRI of human head scans using three planes, including axial plane, co

In today's world, the science of bioinformatics is developing rapidly, especially with regard to the analysis and study of biological networks. Scientists have used various nature-inspired algorithms to find protein complexes in protein-protein interaction (PPI) networks. These networks help scientists guess the molecular function of unknown proteins and show how cells work regularly. It is very common in PPI networks for a protein to participate in multiple functions and belong to many complexes, and as a result, complexes may overlap in the PPI networks. However, developing an efficient and reliable method to address the problem of detecting overlapping protein complexes remains a challenge since it is considered a complex and har

Automated detection of Dubas palm infestation by image processing techniques has practical significance as it can improve agricultural efficiency, increase crop yield and quality, protect the environment, and provide data-driven insights. It also reduces the human effort required for pest control and enhances sustainability. In this study, we aimed to automate the detection of Dubas bug infestation in palm trees using deep learning with transfer learning residual neural networks. Based on four models: InceptionResNetV2, ResNet18, ResNet50, and ResNet101, the data used in this study were obtained by drone photography, many images were taken, and then the infected area was extracted. Using two types of data, 185 infected images and 185 health