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

A system was used to detect injuries in plant leaves by combining machine learning and the principles of image processing. A small agricultural robot was implemented for fine spraying by identifying infected leaves using image processing technology with four different forward speeds (35, 46, 63 and 80 cm/s). The results revealed that increasing the speed of the agricultural robot led to a decrease in the mount of supplements spraying and a detection percentage of infected plants. They also revealed a decrease in the percentage of supplements spraying by 46.89, 52.94, 63.07 and 76% with different forward speeds compared to the traditional method.

    This study identified the genus Coelastrella Chodat, 1922 which was isolated from a sediment sample taken from the Tigris river in Baghdad Governorate, Iraq. The alga was isolated and cultured in modified Chu 10 media and the morphological features of the isolated algae were observed in light microscopy (LM); it showed some characteristic features of this genus, such as its ellipsoidal or lemon- shaped cells, a visible pyrenoid and the chloroplast parietal. To ensure correct identification of the isolated alga, a molecular analysis using 18S rRNA gene and DNA sequencing revealed a match with C. terrestris (Reisigl) Hedewald & N. Hanagata 2002. This species is a new record in Iraq

   Adsorption of lead ions from wastewater by native agricultural waste, precisely tea waste. After the activation and carbonization of tea waste, there was a substantial improvement in surface area and other physical characteristics which include density, bulk density, and porosity. FTIR analysis indicates that the functional groups in tea waste adsorbent are aromatic and carboxylic. It can be concluded that the tea waste could be a good sorbent for the removal of Lead ions from wastewater. Different dosages of the adsorbents were used in the batch studies. A random series of experiments indicated a removal degree efficiency of lead reaching (95 %) at 5 ppm optimum concentration, with adsorbents R² =97.75% for tea. Three mo

Piperine, a crystalline alkaloid compound isolated from Piper nigrum, piper longum, and other types of piper, has had many fabulous pharmacological advantages for preventing and treating some specific diseases, such as analgesic, anti-inflammatory, hepatoprotective, antimetastatic, antithyroid, immunomodulatory, antitumor, rheumatoid arthritis, osteoarthritis, Alzheimer's, and improving the bioavailability of other drugs. However, its potential for clinical use through oral usage is hindered by water solubility and poor bioavailability. The low level of oral bioavailability is caused by low solubility in water and is photosensitive, susceptible to isomerization by UV light, which causes piperine concentration to decrease. Many different

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

Background: Polycystic ovarian syndrome is one of the common gynaecological diseases encountered nowadays in the gynaecological clinic. Many criteria and diagnostic test had been evolved to be used with different classifications methods.Objectives: The present study aimed to measure the anti-mullerian hormone levels in serum of the women with Polycystic Ovary Syndrome and to test the possibility that if it can be used as a marker for diagnosis of polycystic ovary syndrome patients.Methods: A cross sectional study that had been conductedat Kamal AL-Samaraee Hospital, AL-Suwayrah Hospital andAl-Elwiya Maternity Teaching Hospital during the periodfrom July, 1st, 2013 – Jan. 1st, 2014. Where forty women withPolycystic ovarian syndrome (wit