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

In most manufacturing processes, and in spite of statistical control, several process capability indices refer to non conformance of the true mean (µc ) from the target mean ( µT ), and the variation is also high. In this paper, data have been analyzed and studied for a blow molded plastic product (Zahi Bottle) (ZB). WinQSB software was used to facilitate the statistical process control, and process capability analysis and some of capability indices. The relationship between different process capability indices and the true mean of the process were represented, and then with the standard deviation (σ ), of achievement of process capability value that can reduce the standard deviation value and improve production out of theoretical con

A comparison between the resistance capacity of a single pile excited by two opposite rotary machines embedded in dry and saturated sandy soil was considered experimentally. A small-scale physical model was manufactured to accomplish the experimental work in the laboratory. The physical model consists of: two small motors supplied with eccentric mass 0·012 kg and eccentric distance 20 mm representing the two opposite rotary machines, an aluminum shaft with 20 mm in diameter as the pile, and a steel plate with dimensions of (160 × 160 × 20 mm) as a pile cap. The experimental work was achieved taking the following parameters into consideration, pile embedment depth ratio (L/d; length to diameter) and operating freq

Deep learning has recently received a lot of attention as a feasible solution to a variety of artificial intelligence difficulties. Convolutional neural networks (CNNs) outperform other deep learning architectures in the application of object identification and recognition when compared to other machine learning methods. Speech recognition, pattern analysis, and image identification, all benefit from deep neural networks. When performing image operations on noisy images, such as fog removal or low light enhancement, image processing methods such as filtering or image enhancement are required. The study shows the effect of using Multi-scale deep learning Context Aggregation Network CAN on Bilateral Filtering Approximation (BFA) for d

The current study aimed to isolate and diagnose Candida spp yeasts that cause candidiasis with a PCR device from patients reviewed for some hospitals in Baghdad city and by 190 samples, the study recorded 123 isolates and the total percentage of infection was 64.7% .Samples were taken from different clinical cases of the vagina, blood and mouth and the Candida spp were (70.37%, 41.26%, 86.95%) respectively. Five types of yeasts were isolated and diagnosed, namely C. albicans, C. tropicalis, C. parapsilosis, C. krusei and C.glabarta. They were confirmed by PCR device and the most notable were yeast C. albicans, where 91 isolates were found, 73.98%, while the lowest infection was recorded. C.glabartawith 3 isolates, at 2.43%, significant diff

Bidentate Schiff base ligand 3-(3,4-Dihydroxy-phenyl)-2-[(4-dimethylamino-benzylidene)-amino]-2-methyl-propionic acid was prepared and characterized by spectroscopic techniques studies and elemental analysis. The Cd(II), Ni(II), Cu(II), Co(II), Cr(III),and Fe(III) of mixed-ligand complexes were structural explicate through Moler conductance , [FT-IR, UV-Vis & AAS], chloride contents, , and magnetic susceptibility measurements. Octahedral geometries have been suggested for all complexes. The Schiff base and its complexes were tested against various bacterial species, two of {gram(G+) and gram(G-)} were shown weak to good activity against all bacteria.