After the outbreak of COVID-19, immediately it converted from epidemic to pandemic. Radiologic images of CT and X-ray have been widely used to detect COVID-19 disease through observing infrahilar opacity in the lungs. Deep learning has gained popularity in diagnosing many health diseases including COVID-19 and its rapid spreading necessitates the adoption of deep learning in identifying COVID-19 cases. In this study, a deep learning model, based on some principles has been proposed for automatic detection of COVID-19 from X-ray images. The SimpNet architecture has been adopted in our study and trained with X-ray images. The model was evaluated on both binary (COVID-19 and No-findings) classification and multi-class (COVID-19, No-findings

Background: The number of coronavirus infection cases has increased rapidly since early reports in the December 2019 in China. But data on the clinical features of infected peoples is variable from one country to the other.

Objective: Studying clinical features of patients with a positive RT PCR COVID – 19, in a group of Iraqi patients.

Results: The study included 200 patients with 133 (66.5%) males and 67 (33.5%) females, and age range of 14- 89 years, with mean age 46.4 years. A history of contact with a COVID -19 positive case was found in 80 patients (40%), Ischemic Heart Disease in 11 patients (5.5%), hypertension 34 (17%), diabetes mellitus 36 patients (18%). The

Coronavirus disease (COVID-19) is an acute disease that affects the respiratory system which initially appeared in Wuhan, China. In Feb 2019 the sickness began to spread swiftly throughout the entire planet, causing significant health, social, and economic problems. Time series is an important statistical method used to study and analyze a particular phenomenon, identify its pattern and factors, and use it to predict future values. The main focus of the research is to shed light on the study of SARIMA, NARNN, and hybrid models, expecting that the series comprises both linear and non-linear compounds, and that the ARIMA model can deal with the linear component and the NARNN model can deal with the non-linear component. The models

Cervical Uterine Cancer is a disease that explains the vulnerability in which women are in terms of reproductive health with an impact on occupational health and public health, even when in Mexico the prevalence rate is lower than the other member countries of the OECD, its impact on Human Development and Local Development shows the importance that the disease have in communities more than in cities where prevention policies through check-ups and medical examinations seem to curb the trend, but show the lack of opportunities and capacities of health centers in rural areas.   To establish the reliability, validity, and correlations between the variables reported in the literature with respect to their weighting in a public hospital. A

       In the current worldwide health crisis produced by coronavirus disease (COVID-19), researchers and medical specialists began looking for new ways to tackle the epidemic. According to recent studies, Machine Learning (ML) has been effectively deployed in the health sector. Medical imaging sources (radiography and computed tomography) have aided in the development of artificial intelligence(AI) strategies to tackle the coronavirus outbreak. As a result, a classical machine learning approach for coronavirus detection from      Computerized Tomography (CT) images was developed. In this study, the convolutional neural network (CNN) model for feature extraction and support vector machine (SVM) for the classification of axial

Healthcare professionals routinely use audio signals, generated by the human body, to help diagnose disease or assess its progression. With new technologies, it is now possible to collect human-generated sounds, such as coughing. Audio-based machine learning technologies can be adopted for automatic analysis of collected data. Valuable and rich information can be obtained from the cough signal and extracting effective characteristics from a finite duration time interval that changes as a function of time. This article presents a proposed approach to the detection and diagnosis of COVID-19 through the processing of cough collected from patients suffering from the most common symptoms of this pandemic. The proposed method is based on adopt

This research aims to numerically solve a nonlinear initial value problem presented as a system of ordinary differential equations. Our focus is on epidemiological systems in particular. The accurate numerical method that is the Runge-Kutta method of order four has been used to solve this problem that is represented in the epidemic model. The COVID-19 mathematical epidemic model in Iraq from 2020 to the next years is the application under study. Finally, the results obtained for the COVID-19 model have been discussed tabular and graphically. The spread of the COVID-19 pandemic can be observed via the behavior of the different stages of the model that approximates the behavior of actual the COVID-19 epidemic in Iraq. In our study, the COV

Abstract

This research aims to assess the practice of physical activities by people with intellectual disabilities and its challenges during the Coronavirus (COVID-19) pandemic from their families' point of view. The research sample consisted of (87) individuals from families with intellectual disabilities in the Makkah region. The sample was selected by the simple random method where the researcher used the descriptive analytical approach. A questionnaire of (32) items was used as the research tool to collect data. The findings of the study showed that the assessment level of practicing physical activities by people with intellectual disabilities was low. The public facilities dimension ranked first with a moder

Abstract

The present paper attempts to detect the level of (COVID-19) pandemic panic attacks among university students, according to gender and stage variables.

To achieve this objective, the present paper adopts the scale set up by (Fathallah et al., 2021), which has been applied electronically to a previous cross-cultural sample consisting of (2285) participants from Arab countries, including Iraq. The scale includes, in its final form, (69) optional items distributed on (6) dimensions:  physical symptoms (13) items, psychological and emotional symptoms (12) items, cognitive and mental symptoms (11) items, social symptoms (8) items, general symptoms (13) items and daily living practices (12) items

Many studies dealt with the consequences of  SARS CoV-2  (which cause COVID-19 infection) on the nervous system especially sensory nerves where the virus causes loss of taste and smell as it’s known, and may affect auditory nerves and be the expected cause of some hearing problems. A case-control analytic study was performed on a connivance sample of society of university students from a medical faculty. Each participant filled out a questionnaire contains demographic data and general, auditory and respiratory health condition, in addition to vaccination status. In the other side, the audio- examinations were performed on the study sample including Pure Tone Audiometry (PTA) and tympanometry. Two statistical methods; chi-square and t