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

 ABSTRACT

Two compounds were isolated from the fruit part of Rhus coriaria that grow wildly or cultivated in the north of Iraq. The compounds were separated by preparative high-Performance Liquid Chromatography and their structures were established based on detailed spectroscopic techniques like FTIR and LC-MS/MS.

Keywords: Rhus coriaria, Preparative HPLC, LC-MSMS, FTIR

The main aim of this study is to evaluate the remaining oil in previously produced zones, locate the water productive zone and look for any bypassed oil behind casing in not previously perforated intervals. Initial water saturation was calculated from digitized open hole logs using a cut-off value of 10% for irreducible water saturation. The integrated analysis of the thermal capture cross section, Sigma and Carbon/oxygen ratio was conducted and summarized under well shut-in and flowing conditions. The logging pass zone run through sandstone Zubair formation at north Rumaila oil field. The zones where both the Sigma and the C/O analysis show high remaining oil saturation simila

The main aim of this study is to evaluate the remaining oil in previously produced zones, locate the water productive zone and look for any bypassed oil behind casing in not previously perforated intervals. Initial water saturation was calculated from digitized open hole logs using a cut-off value of 10% for irreducible water saturation. The integrated analysis of the thermal capture cross section, Sigma and Carbon/oxygen ratio was conducted and summarized under well shut-in and flowing conditions. The logging pass zone run through sandstone Zubair formation at north Rumaila oil field. The zones where both the Sigma and the C/O analysis show high remaining oil saturation similar to the open hole oil saturation, could be good oil zones that

A simple, rapid and environmentally friendly dispersive liquid–liquid microextraction method-based spectrophotometric method for the trace determination of folic acid has been developed. The proposed method is based on the formation of a deep yellow product via reaction of folic acid and 1,2-naphthoquine-4-sulfonate at pH = 9. The formed complex was extracted using a mixture of chloroform and ethanol. Then, the tiny organic droplets were measured at λ = 520 nm. At the optimum conditions, linearity was ranged from 0.05 to 1.5 μg/mL for the standard and samples, with a linear correlation coefficient of 0.9996. The detection limits were 0.02, 0.027, 0.03, 0.02 and 0.04 μg/mL for standard, tablet (5 mg), tablet (1 mg), syrup and fl

In the present work, a closed loop circulation system consist of three testing sections was designed and constructed. The testing sections made from (3m) of commercial carbon steel pipe of diameters(5.08, 2.54 and 1.91 cm) . Anionic surfactant  (SDBS )with  concentrations  of (50,  100,  150, 200 and 250 ppm)  was tested as a drag reducing  agent.  The additive(SDBS)studied using crude oil from south of Iraq. The flow rates of crude oil were used in 5.08 and 2.54 cm I.D. pipes are (1 - 12) m³/hr while (1-6) m3/hr were used in 1.91 cm J .D. pipe . Percentage drag reduction (%Dr) was found to increase by increasing solution velocity, pipe diameter and additives concentration (i.e. increasi

The continuous advancement in the use of the IoT has greatly transformed industries, though at the same time it has made the IoT network vulnerable to highly advanced cybercrimes. There are several limitations with traditional security measures for IoT; the protection of distributed and adaptive IoT systems requires new approaches. This research presents novel threat intelligence for IoT networks based on deep learning, which maintains compliance with IEEE standards. Interweaving artificial intelligence with standardization frameworks is the goal of the study and, thus, improves the identification, protection, and reduction of cyber threats impacting IoT environments. The study is systematic and begins by examining IoT-specific thre