The study focuses on assessment of the quality of some image enhancement methods which were implemented on renal X-ray images. The enhancement methods included Imadjust, Histogram Equalization (HE) and Contrast Limited Adaptive Histogram Equalization (CLAHE). The images qualities were calculated to compare input images with output images from these three enhancement techniques. An eight renal x-ray images are collected to perform these methods. Generally, the x-ray images are lack of contrast and low in radiation dosage. This lack of image quality can be amended by enhancement process. Three quality image factors were done to assess the resulted images involved (Naturalness Image Quality Evaluator (NIQE), Perception based Image Qual

The abdominal nerve cord of some species of Iraq Carabids has been studied to evaluate
the variation in the number of the abdominal ganglia among the species and to find out
relation of these variations with the classical taxonomy of the family Carabidae into tribes.

Sixty samples of commercially available contact lens solutions were collected from students at the Pharmacy College/Baghdad University. The types of lenses used varied from medical to cosmetic. They were cultured to diagnose any microbial contamination within the solutions. Both used and unused solutions were subject for culturing. Thirty six (60%) used samples showed bacterial growth, fungal growth was absent. Pseudomonas aeruginosa accounts for the highest number of isolates (25%) followed by E. coli (21%), Staphylococcus epidermidis (6.6%), Pseudomonas fluorescence (5%) and Proteus mirabilis (1.6%) respectively. Only one (1) unused (sealed) sample showed growth of P. fluorescence.

    This paper reviews the distribution range of wild goat Capra aegagrus (Erxleben, 1777) in Iraq with new sighting of very small herd of wild goat occur in Alqosh mountain, north of Nineveh province, where wild goat have a little informations on the distribution areas in Iraq according to the Red List of the International Union for Conservation of Nature (IUCN).

        In current study, the dye from flowers petals of Strelitzia reginae used for the first time to prepare natural photosensitizer for DSSC fabrication. Among five different solvents used to extract the natural dye from S. reginae flowers, the ethanol extract of anthocyanin dye revealed higher absorption spectrum of 0.757a.u. at wavelength of 454nm.  A major effect of temperature was studied to increase the extraction yield. The results show that the optimal temperature was 70 °C and there was a sharp decrease of dye concentration from 0.827 at temperature of 70 °C to 0.521 at temperature of 90°C. The extract solution of flowers of S. reginae showed higher concentration in acidic media, especially at pH 4 (0.902). The

During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve

Various theories have been proposed since in last century to predict the first sighting of a new crescent moon. None of them uses the concept of machine and deep learning to process, interpret and simulate patterns hidden in databases. Many of these theories use interpolation and extrapolation techniques to identify sighting regions through such data. In this study, a pattern recognizer artificial neural network was trained to distinguish between visibility regions. Essential parameters of crescent moon sighting were collected from moon sight datasets and used to build an intelligent system of pattern recognition to predict the crescent sight conditions. The proposed ANN learned the datasets with an accuracy of more than 72% in comp

During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve