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

Background: The ultimate purpose of this prospective study is to estimate and measure swelling associated with surgical extrac¬tion of impacted mandibular third molars in different four post-operative times and to identify the risk factors associated with determination of their risk degree. Material and Methods: In this prospective cohort study 159 consecutive cases in which removal of impacted lower third molars in 107outpatients were evaluated. Five groups of variables have been studied which are regarded as a potential factor for swelling after mandibular third removal which will enable the surgeon to predict and counsel high risk patients in order to offer a preventive strategy. Results: Facial measurements were carried out on 1st, 2

BACKGROUND: Polycystic ovary syndrome(PCOS) is one of the most common endocrine disorder affecting women in reproductive age. No single etiologic factor fully accounts for the spectrum of abnormalities in the polycystic ovary syndrome. Different changes in hormonal, metabolism and the inflammatory markers as squealy of PCOS with adverse effect on the women life. OBJECTIVE: To study the relationship between polycystic ovary syndrome and levels of C-reactive protein, human interleukin and hormonal and metabolic alteration in women with PCOS PATIENTS AND METHODS: Thirty women with Polycystic Ovary syndrome (PCOS) and other thirty women without PCOS were included. Venous blood samples were taken in early follicular phase of menstrual cycle [day

Background: Odontogenic cysts include a group of osseodestructive lesions that frequently affect the jaws. Those cysts could derive from odontogenic epithelium and occur in the tooth-bearing regions of the jaws. The aims of this study were to evaluate the immunohistochemical expression of Cyclin D1 in Keratocystic Odontogenic Tumor, Dentigerous cyst and Radicular cyst in epithelium and connective tissue capsule. Materials and Methods: In this study, thirty formalin fixed paraffin embedded tissue blocks of Odontogenic cysts and Tumor, consist of 14 Keratocystic Odontogenic Tumor, 8 dentigerous cysts and 8 radicular cysts were analyzed immunohistochemically for the presence of Cyclin D1 proteins. Results: Strong to moderate expression of Cy

There is no doubt that teachers are the leaders of positive changing in community where they directed the students and build their brains. In our current generation that characterized by accelerated technological development that communication changes, economic and politics, needs from the teacher an active leadership skills that match with the soul of our generation and contribute in confrontation the current challenges and the future challenges in the form that lead to create a conscious generation where they will be a basic brick for the future community where the listeners looking forward the education where they support the continuity communication of develop process, economy, scientifically and in all life fields. In our study we take

In order to reduce the environmental pollution associated with the conventional energy sources and to achieve the increased global energy demand, alterative and renewable sustainable energy sources need to be developed. Microbial fuel cells (MFCs) represent a bio-electrochemical innovative technology for pollution control and a simultaneous sustainable energy production from biodegradable, reduced compounds. This study mainly considers the performance of continuous up flow dual-chambers MFC
fueled with actual domestic wastewater and bio-catalyzed with anaerobic aged sludge obtained from an aged septic tank. The performance of MFCs was mainly evaluated in terms of COD reductions and electrical power output. Results revealed that the C

The purpose of this paper is to model and forecast the white oil during the period (2012-2019) using volatility GARCH-class. After showing that squared returns of white oil have a significant long memory in the volatility, the return series based on fractional GARCH models are estimated and forecasted for the mean and volatility by quasi maximum likelihood QML as a traditional method. While the competition includes machine learning approaches using Support Vector Regression (SVR). Results showed that the best appropriate model among many other models to forecast the volatility, depending on the lowest value of Akaike information criterion and Schwartz information criterion, also the parameters must be significant. In addition, the residuals