Background: Background : Patients with non-rheumatic atrial fibrillation have high risk of thromboembolism especially ischemic stroke usually arising from left atrial appendage .Transoesophageal echocardiography provides useful information for risk stratification in these patients as it detects thrombus in the left atrial or left atrial appendage. Objective : This study was conducted at Al-Kadhimiya Teaching Hospital to assess the prevalence of left atrial chamber thrombi in patients with chronic non-rheumatic atrial fibrillation using transoesophageal echocardiography and its clinical significance as well as to verify the superiority of transoesophageal over transthoracic echocardiography in the detection of these abnormalities. Type of

The aim of this study was to Identifying The Effect of using Linear programming and Branching programming by computer in Learning and Retention of movement concatenation(Linkwork) in parallel bars in Artistic Gymnastics. The searchers have used the experimental method. The search subject of this article has been taken (30) male - students in the second class from the College of Physical Education/University of Baghdad divided into three groups; the first group applied linear programming by computer, and the second group has been applicated branching programming by computer, while precision group used traditional method in the college. The researchers concluded the results by using the statistical bag for social sciences (spss) such as both

Dust is a frequent contributor to health risks and changes in the climate, one of the most dangerous issues facing people today. Desertification, drought, agricultural practices, and sand and dust storms from neighboring regions bring on this issue. Deep learning (DL) long short-term memory (LSTM) based regression was a proposed solution to increase the forecasting accuracy of dust and monitoring. The proposed system has two parts to detect and monitor the dust; at the first step, the LSTM and dense layers are used to build a system using to detect the dust, while at the second step, the proposed Wireless Sensor Networks (WSN) and Internet of Things (IoT) model is used as a forecasting and monitoring model. The experiment DL system

Distributed Denial of Service (DDoS) attacks on Web-based services have grown in both number and sophistication with the rise of advanced wireless technology and modern computing paradigms. Detecting these attacks in the sea of communication packets is very important. There were a lot of DDoS attacks that were directed at the network and transport layers at first. During the past few years, attackers have changed their strategies to try to get into the application layer. The application layer attacks could be more harmful and stealthier because the attack traffic and the normal traffic flows cannot be told apart. Distributed attacks are hard to fight because they can affect real computing resources as well as network bandwidth. DDoS attacks

Image compression plays an important role in reducing the size and storage of data while increasing the speed of its transmission through the Internet significantly. Image compression is an important research topic for several decades and recently, with the great successes achieved by deep learning in many areas of image processing, especially image compression, and its use is increasing Gradually in the field of image compression. The deep learning neural network has also achieved great success in the field of processing and compressing various images of different sizes. In this paper, we present a structure for image compression based on the use of a Convolutional AutoEncoder (CAE) for deep learning, inspired by the diversity of human eye

An experimental and numerical study was carried out to investigate the heat transfer by natural convection in a three dimensional annulus enclosure filled with porous media (silica sand) between two inclined concentric cylinders with (and without) annular fins attached to the inner cylinder under steady state condition. The experiments were carried out for a range of modified Rayleigh number (0.2 ≤Ra*≤ 11) and extended to Ra*=500 for numerical study and for annulus inclination angle of (δ = 0˚, 30˚, 60˚ and 90˚). The numerical study was to give the governing equation under assumptions that used Darcy law and Boussinesq’s approximation and then it was solved numerically using finite difference approximation. It was found that t

The meniscus has a crucial function in human anatomy, and Magnetic Resonance Imaging (M.R.I.) plays an essential role in meniscus assessment. It is difficult to identify cartilage lesions using typical image processing approaches because the M.R.I. data is so diverse. An M.R.I. data sequence comprises numerous images, and the attributes area we are searching for may differ from each image in the series. Therefore, feature extraction gets more complicated, hence specifically, traditional image processing becomes very complex. In traditional image processing, a human tells a computer what should be there, but a deep learning (D.L.) algorithm extracts the features of what is already there automatically. The surface changes become valuable when