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

Background: The COVID-19 infection is a more recent pandemic disease all over the world and studying the pulmonary findings on survivors of this disease has lately commenced.

Objective: We aimed to estimate the cumulative percentage of whole radiological resolution after 3 months from recovery and to define the residual chest CT findings and exploring the relevant affecting factors.

Subjects and Methods: Patients who had been previously diagnosed with COVID-19 pneumonia confirmed by RT-PCR test and had radiological evidence of pulmonary involvement by Chest CT during the acute illness were included in the present study. The radiol

The pandemic of coronavirus disease 2019 (COVID-19), first reported in China, in December 2019 and since then the digestive tract involvement of  COVID-19 has been progressively described. In this review, I summed recent studies, which have addressed the pathophysiology of COVID-19-induced gastrointestinal symptoms, their prevalence, and bowel pathological and radiological findings of infected patients. The effects of gut microbiota on SARS-CoV-2 and the challenges of nutritional therapy of the infected patients are depicted.  Moreover, I provide a concise summary of the recommendations on the management of inflammatory bowel disease, colorectal cancer, and performing endoscopy in the COVID era. Finally, the COVID pancreatic re

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

Abstract

Epidemics that afflict humankind are descending renewed, plaguing them in the place and time they spread.

- The epidemic affects individuals and the movement of societies, and its treatment requires dealing with it according to Sharia, taking into account the current data and developments.

- Integrative jurisprudence: it is intended to know the practical legal rulings deduced from the combination of evidence of two or more sciences related to one topic related to it, and among these calamities is the Corona Covid-19 pandemic.

 - It is permissible to use sterile materials that contain a percentage of alcohol in sterilizing hands and fogging places, including mosques.

T

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

The current research was aimed at the following:
1. Measurement the Ambivalence among University students.
2. Identify the differences in Ambivalence among University students according to variable of Specialization (scientific / literary).
To achieve this aims of the research, the researcher set up the instrument is scale of Ambivalence that consistent (19) item. And the researchers applying this scale on the sample amounted to (200) among University students. Then after data processing statistically, the researchers reached the following results:
1. University students have Ambivalence.
2. There is no is differences in Ambivalence among University students according to variable of Specialization (scientific / literary).<

Objectives: This study aims to assess the knowledge, regarding Swine Flu pandemic among a sample of paramedical
specialty students of Medical Technology Institute (Baghdad).
Methodology: The study sample included (110) male and female students, randomly selected, and data was collected by
previously prepared questionnaire including different questions covering different clinic-epidemiological aspects of the
disease and followed by statistical analysis using simple binomial tests and average percentage of correct answers.
Results: The higher percentage of correct responses regarding causative virus 83%, it is respiratory disease 83%,
transmission among people through the droplets 83%, and by touching contaminated surface