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

Mammography is at present one of the available method for early detection of masses or abnormalities which is related to breast cancer. The most common abnormalities that may indicate breast cancer are masses and calcifications. The challenge lies in early and accurate detection to overcome the development of breast cancer that affects more and more women throughout the world. Breast cancer is diagnosed at advanced stages with the help of the digital mammogram images. Masses appear in a mammogram as fine, granular clusters, which are often difficult to identify in a raw mammogram. The incidence of breast cancer in women has increased significantly in recent years.
This paper proposes a computer aided diagnostic system for the extracti

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

     The specimens of Camponotusxerxes Forel, 1904 were collected from different localities in Iraq; the purpose of morphological study of this species in details throughout the present study.

     The description was based on major workers belonging to this species, also some notes of polymorphism in workers have been mentioned; the most important of morphological features are illustrated and figured.

Artemisia is a perennial wild shrub with large branches and compound leaves. Artemisia contains about 400 types, and its medical importance is due to the presence of many active substances and compounds such as volatile oils, alkaloids and flavonoids, glycosides, saponins, tannins, and coumarins. This study was designed to study the effect of the aqueous extract of the fruit of the Artemisia plant on the organs of the body, as well as to know its ability to activate the hepatic enzyme alanine transaminase (ALT/GPT). The fruit of this shrub was extracted using the measurement technique gas chromatography-mass spectrometry (GC/MASS) and organic solvent hexane and ethyl acetate in one to one ratio. It contained 21 compounds, a high percentage

Background: Diabetic patients have been reported to be more susceptible to gingivitis and periodontitis than healthy subjects. Many intracellular enzymes like (alkaline phosphatase- (ALP), aspartate aminotransferase- (AST) and alanine aminotransferase- (ALT) that are released outside cells into the gingival crevicular fluid (GCF) and saliva after destruction of periodontal tissue during periodontitis. This study was conducted to determine the periodontal health status and the levels of salivary enzymes (ALP, AST and ALT) of the study and control groups and to correlate the levels of these enzymes with clinical periodontal parameters in each study group. Subjects, Materials and Methods: One hundred subjects were enrolled in the study, with a

Field experiment was conducted to test the effect of saline water and fertilizers rate on Pisum sativum L. plants . Treatments of the experiment included two levels of water salinity (2, 7 dSm-1) as a main plot and three levels of potash fertilization K2SO4 (44%K) namely 150 control, 300 and 450 kg/Donum as a sub plot. Results indicated that irrigation of plant with saline water 7 dSm-¹ caused a significant decrease in K and P contents specially in the upper parts of the plants , the percentage of the K increased (2.80%) under 2 dSm-¹of irrigation water and 300 kg/ donum fertilizer rate in the upper leaves, However K decreased(1.10%) in lower leaves under 7 dSm-¹ and 300 kg/donum fertilizer. while P increased in pods und

Pure and Fe-doped zinc oxide nanocrystalline films were prepared
via a sol–gel method using -
C for 2 h.
The thin films were prepared and characterized by X-ray diffraction
(XRD), atomic force microscopy (AFM), field emission scanning
electron microscopy (FE-SEM) and UV- visible spectroscopy. The
XRD results showed that ZnO has hexagonal wurtzite structure and
the Fe ions were well incorporated into the ZnO structure. As the Fe
level increased from 2 wt% to 8 wt%, the crystallite size reduced in
comparison with the pure ZnO. The transmittance spectra were then
recorded at wavelengths ranging from 300 nm to 1000 nm. The
optical band gap energy of spin-coated films also decreased as Fe
doping concentra