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

A novel method for Network Intrusion Detection System (NIDS) has been proposed, based on the concept of how DNA sequence detects disease as both domains have similar conceptual method of detection. Three important steps have been proposed to apply DNA sequence for NIDS: convert the network traffic data into a form of DNA sequence using Cryptography encoding method; discover patterns of Short Tandem Repeats (STR) sequence for each network traffic attack using Teiresias algorithm; and conduct classification process depends upon STR sequence based on Horspool algorithm. 10% KDD Cup 1999 data set is used for training phase. Correct KDD Cup 1999 data set is used for testing phase to evaluate the proposed method. The current experiment results sh

Roller Compacted Concrete (RCC) is a technology characterized mainly by the use of rollers for compaction; this technology achieves significant time and cost savings in the construction of dams and roads. The primary scope of this research is to study the durability and performance of roller compacted concrete that was constructed in the laboratory using roller compactor manufactured in local market. A total of (60) slab specimen of (38×38×10) cm was constructed using the roller device, cured for 28 days, then 180 sawed cubes and 180 beams are obtained from RCC slab. Then, the specimens are subjected to 60 cycles of freezing and thawing, sulfate attack test and wetting and drying. The degree of effect of the type of coarse aggregate (c

Background: Hypertension and osteoporosis are worldwide diseases with high prevalence; the study was done to estimate the correlation between hypertension and bone turnover in a sample of Iraqi postmenopausal women with hypertension and obesity. Materials and Methods: A cross-sectional study was done on 99 hypertensive obese postmenopausal Iraqi women (65 hypertensive obese and 34 normotensive non obese). The serum calcium (Ca), phosphorus (Pi), and alka line phosphatase (ALP) levels were measured as a represen tative of bone markers. Results: The results demonstrated that serum alkaline phosphatase is significantly elevated in the case group compared to the controls. No significant changes were observed in the levels of Ca and

Background: Myasthenia gravis is an autoimmune disease of the neuromuscular junction that results in fluctuating muscle weakness as well as significant fatigue. Disease exacerbation is a critical condition, and the predisposing factors for it need to be identified to improve preventive measures.

Objectives:  Our study aims to determine the predisposing factors for myasthenia gravis exacerbations in a group of Iraqi patients.

Subjects and Methods: A total number of 30 myasthenia gravis patients were admitted to the hospital with an exacerbation of their symptoms, determined as the development of functional disability, dysphagia, or respiratory fai

In this paper, a fast lossless image compression method is introduced for compressing medical images, it is based on splitting the image blocks according to its nature along with using the polynomial approximation to decompose image signal followed by applying run length coding on the residue part of the image, which represents the error caused by applying polynomial approximation. Then, Huffman coding is applied as a last stage to encode the polynomial coefficients and run length coding. The test results indicate that the suggested method can lead to promising performance.

In this work we present a technique to extract the heart contours from noisy echocardiograph images. Our technique is based on improving the image before applying contours detection to reduce heavy noise and get better image quality. To perform that, we combine many pre-processing techniques (filtering, morphological operations, and contrast adjustment) to avoid unclear edges and enhance low contrast of echocardiograph images, after implementing these techniques we can get legible detection for heart boundaries and valves movement by traditional edge detection methods.