The need for participants’ performance assessments in academia and industry has been a growing concern. It has attendance, among other metrics, is a key factor in engendering a holistic approach to decision-making. For institutions or organizations where managing people is an important yet challenging task, attendance tracking and management could be employed to improve this seemingly time-consuming process while keeping an accurate attendance record. The manual/quasi-analog approach of taking attendance in some institutions could be unreliable and inefficient, leading to inaccurate computation of attendance rates and data loss. This work, therefore, proposes a system that employs embedded technology and a biometric/ w

This study aimed for isolation and identification of Candida glabrata from specimens of blood collected from national center of hematology /Al - mustansiriya University of immunocompromised patients infection of candidemia after diagnosis by doctor. Results showed the morphological features on many media SDA and Corn meal and differential CHROMagar. Colonies appear small and pink pale to dark. Microscopic exam of show that C. glabrata not form pseudohyphae and not produce germ tubes, the cell size 2 - 3 microns, on the other hand, Biochemical test of C. glabrata have high ability for fermentation of glucose within three hours and trehalose given false positive within two hrs., and the Urease test given negative result, on the other hand

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

Gravity and magnetic data are used to study the tectonic situation of Al-Kut- Al-
Hai and surrounding areas in central Iraq. The study included application of many
processing and interpretation programs. The window method with different spacing
was used to separate the residual from regional anomalies for gravity and magnetic
data. The Total Horizontal Derivative (THDR) techniques used to identify the fault
trends in the basement and sedimentary cover rocks depending upon gravity and
magnetic data. The identified faults in the study area show (NW-SE), (NE-SW) (NS)
and (E-W) trends. It is believed that these faults extending from the basement to
the upper most layer of the sedimentary cover rocks.

This paper presents a nonlinear finite element modeling and analysis of steel fiber reinforced concrete (SFRC) deep beams with and without openings in web subjected to two- point loading. In this study, the beams were modeled using ANSYS nonlinear finite element
software. The percentage of steel fiber was varied from 0 to 1.0%.The influence of fiber content in the concrete deep beams has been studied by measuring the deflection of the deep beams at mid- span and marking the cracking patterns, compute the failure loads for each deep beam, and also study the shearing and first principal stresses for the deep beams with and without openings and with different steel fiber ratios. The above study indicates that the location of openings an

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

— To identify the effect of deep learning strategy on mathematics achievement and practical intelligence among secondary school students during the 2022/2023 academic year. In the research, the experimental research method with two groups (experimental and control) with a post-test were adopted. The research community is represented by the female students of the fifth scientific grade from the first Karkh Education Directorate. (61) female students were intentionally chosen, and they were divided into two groups: an experimental group (30) students who were taught according to the proposed strategy, and a control group (31) students who were taught according to the usual method. For the purpose of collecting data for the experimen

    The Internet of Things (IoT) technology and smart systems are playing a major role in the advanced developments in the world that take place nowadays, especially in multiple privilege systems. There are many smart systems used in daily human life to serve them and facilitate their tasks, such as alarm systems that work to prevent unwanted events or face detection and recognition systems. The main idea of this work is to capture live video using a connected Pi camera, save it, and unlock the electric strike door in several ways; either automatically by displaying a live video connected via USB webcam using a deep learning algorithm of facial recognition and OpenCV or by RFID technology, as well as by detecting abnormal entrance wit