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

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

This paper presents a novel inverse kinematics solution for robotic arm based on artificial neural network (ANN) architecture. The motion of robotic arm is controlled by the kinematics of ANN. A new artificial neural network approach for inverse kinematics is proposed. The novelty of the proposed ANN is the inclusion of the feedback of current joint angles configuration of robotic arm as well as the desired position and orientation in the input pattern of neural network, while the traditional ANN has only the desired position and orientation of the end effector in the input pattern of neural network. In this paper, a six DOF Denso robotic arm with a gripper is controlled by ANN. The comprehensive experimental results proved the appl

Objective(s): To evaluate students’ communication skills and their academic performance; to compare between the students relative to communication skills and their academic performance in the University of Baghdad and to identify the relationship between students’ communication skills, academic performance and their socio-demographic characteristics of age, gender, grade and socioeconomic status. Methodology: A descriptive design, using the evaluation approach, is carried through the present study to evaluate colleges’ students’ communication skills and their academic performance in the University of Baghdad for the period of January 7th 2019 to August 28th 2019. A non-probability, purposive sample, of (80) university students, i

Oilwell cementing operations are crucial for drilling and completion, preserving the well's productive life. However, weak and permeable formations pose a high risk of cement slurry loss, leading to failure. Lightweight cement, like foamed cement, is used to avoid these difficulties. This study is focused on creating a range of foamed slurry densities and examining the effect of gas concentration on their rheological properties. The foaming agent and foam stabilizer are tested, and the optimal concentration is determined to be 2% and 0.12%, respectively, by the weight of the cement.

Furthermore, the construction of samples of foam cement with different densities (0.8, 1.0, 1.2, 1.4, and 1.6) g/cc is performed to f

Dairy wastewater generally contains fats, lactose, whey proteins, and nutrients. Casein precipitation causes the effluent to decompose into a dark, strong-smelling sludge. Fluid waste contains soluble organic matter, suspended solids, and gaseous organic matter, which cause undesirable taste and smell, grant tone and turbidity, and advance eutrophication, which plays an essential role in increasing biological oxygen demand (BOD) in water. It also contains detergents and disinfecting agents from the rinses and washing processes, which increase the need for chemical oxygen (COD). One of the characteristics of dairy effluents is their relatively high temperature, high organic contents, and wide pH range, so the discharge of wastewater into

: Clobetasol propionate (CP) is a potent corticosteroid used for skin conditions but often causes side effects due its systemic absorption. To improve its solubility and reduce it side effects (like skin irritation, skin atrophy, hypopigmentation and steroidal acne), Microsponge (Msg) has been employed as a unique three-dimensional particle that can encapsulate hydrophilic and lipophilic drugs. This study aims to develop and evaluate CP Msg-loaded hydrogels. Two Clobetasol-loaded ethylcellulose-based Msg formulas were prepared using the quasi-emulsion solvent diffusion method, then they were incorporated into Carbopol hydrogel. Two ratios of Carbopol 940 (1% and 1.5% w/w) were used. The prepared hydrogel were assessed for appearance, pH, dr

Objective: To evaluate the knowledge and practices of nursing staff at the orthopedic wards relative to
nursing care presented to patients with femur fractures.
MethodologyThe sample consisted of (50) staff nurses was selected out of orthopedic wards of five
teaching hospital in Baghdad city for duration 15th Nivember 2001-15th of January 2002.
For the purpose of data collection, two instruments were constructed. First, observational
checklist for the practices measurements and second, knowledge test for the evaluation of the nurse
knowledge. Such construction was employed through literature review and validity expert’s responses.
Data were analyzed through the application of descriptive data analysis (frequency, p