The typical test for diagnosis of severe acute respiratory syndrome coronavirus 2 is a reverse transcription-polymerase chain reaction (RT-PCR) technique, but the chest CT scan might play a complementary role at the first detection of Coronavirus Disease 2019 (COVID-19) pneumonia. Objectives: To determine the sensitivity of CT scan on patients with COVID-19 in Al-Najaf, Iraq, and to compare the accuracy of CT scan with that of RT-PCR technique. Material and Method: This is a prospective study. The patients suspicious of having COVID-19 infection and respiratory symptoms were registered. All patients were diagnosed by RT-PCR and chest CT. Diagnostic performance of CT was intended using RT-PCR as the reference sta

Background: Gray-scale sonography is generally
considered as a first-line diagnostic tool for patient with
suspected acute cholecystitis. It is suggested by gallstones,
Murphy's sign, thickening of the gallbladder wall and bile
sludging, but the specificity of these sonographic findings
are not as high as their sensitivity. Blood flow of the
gallbladder wall is increased in acute inflammation.
Objective: To evaluate the sensitivity and specificity of
power Doppler sonography and compared with conventional
color Doppler and gray-scale sonography in diagnosing
patients with acute cholecystitis.
Type of the study: This was a cross sectional study.
Patients and methods: The study was conducted t

Background: Metabolic syndrome (MetS) is a collection of connected cardiovascular risk factors that characterizes the complicated illness. The waist circumference cutoff point fluctuation has so far defined Mets. Objective: This study aimed to determine the cutoff point for WC in healthy Iraqi adults. Methods: This cross-sectional survey establishes the standard value for WC among 300 healthy university students in Wasit city, Iraq. They are aged between 18-25 years. The receiver operator characteristic (ROC) curve was used WC to predict the presence of two or more risk factors for MetS, as defined by IDF. Results: The cutoff level yielding maximum sensitivity and specificity for predicting the presence of multiple risk factors was

Breast cancer has got much attention in the recent years as it is a one of the complex diseases that can threaten people lives. It can be determined from the levels of secreted proteins in the blood. In this project, we developed a method of finding a threshold to classify the probability of being affected by it in a population based on the levels of the related proteins in relatively small case-control samples. We applied our method to simulated and real data. The results showed that the method we used was accurate in estimating the probability of being diseased in both simulation and real data. Moreover, we were able to calculate the sensitivity and specificity under the null hypothesis of our research question of being diseased o

<span>Dust is a common cause of health risks and also a cause of climate change, one of the most threatening problems to humans. In the recent decade, climate change in Iraq, typified by increased droughts and deserts, has generated numerous environmental issues. This study forecasts dust in five central Iraqi districts using machine learning and five regression algorithm supervised learning system framework. It was assessed using an Iraqi meteorological organization and seismology (IMOS) dataset. Simulation results show that the gradient boosting regressor (GBR) has a mean square error of 8.345 and a total accuracy ratio of 91.65%. Moreover, the results show that the decision tree (DT), where the mean square error is 8.965, c

Background: The styloid process is a cylindrical bone (protrusion). It situated above the common carotid artery between the external and internal branches immediately proximal to the internal jugular vein and facial nerves. The styloid process varies in length also it may be absent as well as elongated. Classically, an elongated styloid process and calcified of stylohyoid ligament causes Eagle’s syndrome. The aim of this study was to examine the styloid process using 3 dimensional multi-detector computed tomography (3D-MDCT) to detect the presence of Eagle’s syndrome that causes severe headache and migraine. Materials and methods: One hundred patients with severe headache and migraine were exposed to 3D- multi-detector CT with special

Computer-aided diagnosis (CAD) has proved to be an effective and accurate method for diagnostic prediction over the years. This article focuses on the development of an automated CAD system with the intent to perform diagnosis as accurately as possible. Deep learning methods have been able to produce impressive results on medical image datasets. This study employs deep learning methods in conjunction with meta-heuristic algorithms and supervised machine-learning algorithms to perform an accurate diagnosis. Pre-trained convolutional neural networks (CNNs) or auto-encoder are used for feature extraction, whereas feature selection is performed using an ant colony optimization (ACO) algorithm. Ant colony optimization helps to search for the bes

Given the growing interest in digital marketing operations and the technology imposed by the reality in which both companies and their customers are affected, the researchers attempted to shed light on the reality and challenges of digital marketing from the faculty members viewpoint at Jouf university, the problem is that technology has imposed a new reality that has resulted in a major change in behavioral patterns of customers with a number of obstacles and challenges confronted customers in digital marketing, it is expected that the outcomes of this study help companies in overcoming the obstacles that prevent the desire and ability of the customer to change his behavioral style to deal with electronic shopping operations and

Vehicular Ad Hoc Networks (VANETs) are integral to Intelligent Transportation Systems (ITS), enabling real-time communication between vehicles and infrastructure to enhance traffic flow, road safety, and passenger experience. However, the open and dynamic nature of VANETs presents significant privacy and security challenges, including data eavesdropping, message manipulation, and unauthorized access. This study addresses these concerns by leveraging advancements in Fog Computing (FC), which offers lowlatency, distributed data processing near-end devices to enhance the resilience and security of VANET communications. The paper comprehensively analyzes the security frameworks for fog-enabled VANETs, introducing a novel taxonomy that c