Medicine is one of the fields where the advancement of computer science is making significant progress. Some diseases require an immediate diagnosis in order to improve patient outcomes. The usage of computers in medicine improves precision and accelerates data processing and diagnosis. In order to categorize biological images, hybrid machine learning, a combination of various deep learning approaches, was utilized, and a meta-heuristic algorithm was provided in this research. In addition, two different medical datasets were introduced, one covering the magnetic resonance imaging (MRI) of brain tumors and the other dealing with chest X-rays (CXRs) of COVID-19. These datasets were introduced to the combination network that contained deep lea

Rheumatoid arthritis (RA), is an autoimmune, and inflammatory disease that is closely related to the destruction of cartilage and bone. DC-SIGN are important types of C-type lectin receptors (CLRs), expressed on dendritic cells and macrophages, and have a central role in regulating innate and adaptive immunity, function as pattern recognition receptors, and as cell adhesion molecules. Recent evidence has demonstrated that DC-SIGN is involved in the pathophysiological of chronic inflammation, so DC-SIGN has been linked to several autoimmune and may play an essential indicator in the pathogenesis and progression of RA. Therefore, the purpose of this study is to determine the serum level of DC-SIGN in RA patients, as well as the level of DC

Background: Pain is one of the most reported side effects of orthodontic treatment despite the advanced technology in orthodontics. Many analgesics have been introduced to control orthodontic pain including acetaminophen and selective and nonselective nonsteroidal anti-inflammatory drugs. The great concern about these drugs is their adverse effect on rate of teeth movement. Aims: The purpose of this study was to evaluate and compare the effect of acetaminophen, ibuprofen and etoricoxib on pain perception and their influence on the rate of teeth movement during leveling and alignment stage. Methods: Forty patients were evenly and randomly distributed in a blinded way to one of four groups: placebo (starch capsules), acetaminophen 500mg th

Atmospheric transmission is disturbed by scintillation, where scintillation caused more beam divergence. In this work target image spot radius was calculated in presence of atmospheric scintillation. The calculation depend on few relevant equation based on atmospheric parameter (for Middle East), tracking range, expansion ratio of applied beam expander's, receiving unit lens F-number, and the laser wavelength besides photodetector parameter. At maximum target range Rmax =20 km, target image radius is at its maximum Rs=0.4 mm. As the range decreases spot radius decreases too, until the range reaches limit (4 km) at which target image spot radius at its minimum value (0.22 mm). Then as the range decreases, spot radius increases due to geom

With the proliferation of both Internet access and data traffic, recent breaches have brought into sharp focus the need for Network Intrusion Detection Systems (NIDS) to protect networks from more complex cyberattacks. To differentiate between normal network processes and possible attacks, Intrusion Detection Systems (IDS) often employ pattern recognition and data mining techniques. Network and host system intrusions, assaults, and policy violations can be automatically detected and classified by an Intrusion Detection System (IDS). Using Python Scikit-Learn the results of this study show that Machine Learning (ML) techniques like Decision Tree (DT), Naïve Bayes (NB), and K-Nearest Neighbor (KNN) can enhance the effectiveness of an Intrusi