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

The research shed light on the historic evolution of Baghdad through its long, expansive history. The starting point focuses on the geographic characteristics, and the nature of its habitation, prior to laying the circular plan of Baghdad. Then the research proceeds to cover the stage of building the round city of Baghdad. The research continue to cover the expansion and sequential growth across the banks of Tigris river.
A concentrated attention is devoted to analyses the morphological, geographical and above all the makeup of present day city of Baghdad, pinpointing the apathetic plans, decisions, and actions which completely disfigured the image, and tradition of the old city of Baghdad, behind the delusive slogans of “comprehens

The study sought to identify the attitudes of PhD students towards establishing the field of educational administration. The study followed the descriptive survey method. The questionnaire was used to collect information from the study community consisting of (95) male and female students in the department of educational administration and Planning. Among the most important results about students ’attitudes towards establishing the educational administration field are the following: 1) identifying the necessity of establishing the educational administration field. 2) Encouraging students to attend seminars and scientific conferences in Islamic rooting. 3) there are no statistically significant differences in the attitudes of doctoral s

                  In light of the corona pandemic, educational institutions have moved to learning and teaching via the Internet and e-learning ,and this is considered a turning  point in course of higher education in Iraq in particular and education in general, which generated a great challenge for educational institutions to achieve the highest possible levels in practices and processes to reach the highest quality of their outputs from graduate students to the labor market that auditing performance by adopting e-learning standards is one of the effective tools that help the management of educational institutions by providing information on the ex

Machine Learning (ML) algorithms are increasingly being utilized in the medical field to manage and diagnose diseases, leading to improved patient treatment and disease management. Several recent studies have found that Covid-19 patients have a higher incidence of blood clots, and understanding the pathological pathways that lead to blood clot formation (thrombogenesis) is critical. Current methods of reporting thrombogenesis-related fluid dynamic metrics for patient-specific anatomies are based on computational fluid dynamics (CFD) analysis, which can take weeks to months for a single patient. In this paper, we propose a ML-based method for rapid thrombogenesis prediction in the carotid artery of Covid-19 patients. Our proposed system aims

The COVID-19 pandemic has necessitated new methods for controlling the spread of the virus, and machine learning (ML) holds promise in this regard. Our study aims to explore the latest ML algorithms utilized for COVID-19 prediction, with a focus on their potential to optimize decision-making and resource allocation during peak periods of the pandemic. Our review stands out from others as it concentrates primarily on ML methods for disease prediction.To conduct this scoping review, we performed a Google Scholar literature search using "COVID-19," "prediction," and "machine learning" as keywords, with a custom range from 2020 to 2022. Of the 99 articles that were screened for eligibility, we selected 20 for the final review.Our system

This paper proposes a better solution for EEG-based brain language signals classification, it is using machine learning and optimization algorithms. This project aims to replace the brain signal classification for language processing tasks by achieving the higher accuracy and speed process. Features extraction is performed using a modified Discrete Wavelet Transform (DWT) in this study which increases the capability of capturing signal characteristics appropriately by decomposing EEG signals into significant frequency components. A Gray Wolf Optimization (GWO) algorithm method is applied to improve the results and select the optimal features which achieves more accurate results by selecting impactful features with maximum relevance

Nosocomial infections (NIs) are hospital-acquired associated infections, and also contracted due to the infections or toxins that exist in some location, like hospital. Therefore in our study, 4 Lactic acid bacteria (LAB) isolates were obtained from dairy product (Lactobacillus brevis, L. acidophilus, Lactococcus raffinolactis and Lactococcus lactis) and were tested for Bacteriocin production to select Lactococcus lactis among them. Cell free supernatant (CFS), Lipid and partial purification of protein La. Lactis had high inhibitory effect against test pathogens (E. coli, Bacillus cereus, Staphylococcus aureus and Streptococcus). 30 isolates that diagnosed by Vitec, were isol