In this article, the research presents a general overview of deep learning-based AVSS (audio-visual source separation) systems. AVSS has achieved exceptional results in a number of areas, including decreasing noise levels, boosting speech recognition, and improving audio quality. The advantages and disadvantages of each deep learning model are discussed throughout the research as it reviews various current experiments on AVSS. The TCD TIMIT dataset (which contains top-notch audio and video recordings created especially for speech recognition tasks) and the Voxceleb dataset (a sizable collection of brief audio-visual clips with human speech) are just a couple of the useful datasets summarized in the paper that can be used to test A

Deep Learning Techniques For Skull Stripping of Brain MR Images

Medium Access Control (MAC) spoofing attacks relate to an attacker altering the manufacturer assigned MAC address to any other value. MAC spoofing attacks in Wireless Fidelity (WiFi) network are simple because of the ease of access to the tools of the MAC fraud on the Internet like MAC Makeup, and in addition to that the MAC address can be changed manually without software. MAC spoofing attacks are considered one of the most intensive attacks in the WiFi network; as result for that, many MAC spoofing detection systems were built, each of which comes with its strength and weak points. This paper logically identifies and recognizes the weak points
and masquerading paths that penetrate the up-to-date existing detection systems. Then the

Magnetic  Resonance  Imaging  (MRI)  is  one  of  the  most important diagnostic tool. There are many methods to segment the

tumor of human brain. One of these, the conventional method that uses pure image processing techniques that are not preferred because they need human interaction for accurate segmentation. But unsupervised methods do not require any human interference and can segment   the   brain   with   high   precision.   In   this   project,   the unsupervised  classification methods have been used in order to detect the tumor  disease from MRI images.    These metho

     Many authors investigated the problem of the early visibility of the new crescent moon after the conjunction and proposed many criteria addressing this issue in the literature. This article presented a proposed criterion for early crescent moon sighting based on a deep-learned pattern recognizer artificial neural network (ANN) performance. Moon sight datasets were collected from various sources and used to learn the ANN. The new criterion relied on the crescent width and the arc of vision from the edge of the crescent bright limb. The result of that criterion was a control value indicating the moon's visibility condition, which separated the datasets into four regions: invisible, telescope only, probably visible, and certai

Background: Scientific education aims to be inclusive and to improve students learning achievements, through appropriate teaching and learning. Problem Based Learning (PBL) system, a student centered method, started in the second half of the previous century and is expanding progressively, organizes learning around problems and students learn about a subject through the experience of solving these problems.Objectives:To assess the opinions of undergraduate medical students regarding learning outcomes of PBL in small group teaching and to explore their views about the role of tutors and methods of evaluation. Type of the study: A cross-sectional study.Methods: This study was conducted in Kerbala Medical Colleges among second year students

This study aims to assess the accuracy of digital elevation model (DEM) created with utilization of handheld Global Positioning System (GPS) and comparing with Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM), version 2. It is known that the quality of the DEM is affected by both of accuracy of elevation at each pixel (absolute accuracy) and accuracy of presented morphology (relative accuracy). The University of Baghdad, Al Jadriya campus was selected as a study area to create and analysis the resulting DEM. Additionally, Geographic Information System (GIS) was used to visualize, analyses and interpolate GPS track points (elevation data) of the study area. In this

Traffic classification is referred to as the task of categorizing traffic flows into application-aware classes such as chats, streaming, VoIP, etc. Most systems of network traffic identification are based on features. These features may be static signatures, port numbers, statistical characteristics, and so on. Current methods of data flow classification are effective, they still lack new inventive approaches to meet the needs of vital points such as real-time traffic classification, low power consumption, ), Central Processing Unit (CPU) utilization, etc. Our novel Fast Deep Packet Header Inspection (FDPHI) traffic classification proposal employs 1 Dimension Convolution Neural Network (1D-CNN) to automatically learn more representational c

Digital change detection is the process that helps in determining the changes associated with land use and land cover properties with reference to geo-registered multi temporal remote sensing data. In this research change detection techniques have been employed to detect the changes in marshes in south of Iraq for two period the first one from 1973 to 1984 and the other from 1973 to 2014 three satellite images had been captured by land sat in different period. Preprocessing such as geo-registered, rectification and mosaic process have been done to prepare the satellite images for monitoring process. supervised classification techniques such maximum likelihood classification has been used to classify the studied area, change detection aft