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Normalized-UNet Segmentation for COVID-19 Utilizing an Encoder-Decoder Connection Layer Block
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The COVID-19 pandemic has had a huge influence on human lives all around the world. The virus spread quickly and impacted millions of individuals, resulting in a large number of hospitalizations and fatalities. The pandemic has also impacted economics, education, and social connections, among other aspects of life. Coronavirus-generated Computed Tomography (CT) scans have Regions of Interest (ROIs). The use of a modified U-Net model structure to categorize the region of interest at the pixel level is a promising strategy that may increase the accuracy of detecting COVID-19-associated anomalies in CT images. The suggested method seeks to detect and isolate ROIs in CT scans that show the existence of ground-glass opacity, which is frequent in COVID-19 patients. This can assist healthcare practitioners in identifying and monitoring illness development, as well as making treatment decisions. Scale U-Net is a strong U-Net design modification that can increase the performance of semantic segmentation tasks. Our model, Normalized-UNet, uses batch normalization after each convolutional layer to decrease the internal covariate shift, which dramatically improves the network's learning efficiency.

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Publication Date
Wed May 10 2017
Journal Name
Australian Journal Of Basic And Applied Sciences
Block-based Image Steganography for Text Hiding Using YUV Color Model and Secret Key Cryptography Methods
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Publication Date
Tue Mar 28 2017
Journal Name
Iraqi Journal Of Pharmaceutical Sciences ( P-issn 1683 - 3597 E-issn 2521 - 3512)
Formulation and Evaluation of Bilayer Tablets Containing Immediate Release Aspirin Layer and Floating Clopidogrel Layer
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Aspirin and clopidogrel are considered the most important oral platelets aggregation inhibitors. So it is widely used for treatment and prophylaxis of cardiovascular and peripheral vascular diseases related to platelets aggregation .In this study aspirin and clopidogrel were formulated together as floating bilayer tablet system. Three different formulas of 75 mg aspirin were prepared by wet granulation method as immediate release layer; different disintegrants used to achieve rapid disintegration. Formula with crosscarmellose as disintegrant achieve rapid disintegration was selected for preparation of bilayer tablet.

Different formulas of 75 mg clopidogrel were prepared as sustained release floating layer by wet granulation (effe

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Publication Date
Fri May 01 2015
Journal Name
Journal Of Engineering
Experimental Study of the Thermal Performance of Flat Plate Solar Collectors Array by Different Connection Configurations
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The current research illustrates experimentally the effect of series and parallel connection (Z-I Configurations) of flat plate water solar collectors array on the thermal performance of closed loop solar heating system. The study includes the effect of changing the water flow rate on the thermal efficiency. The results show that, the collector's efficiency in series connection is higher than the parallel connection within flow rate level less than (100) ℓ/hr. Moreover, the collector efficiency in parallel connection of (I-Configurations) is more than the (Z- Configurations) with increasing the water flow rate .The maximum daily efficiency for parallel (I-Configurations) and (Z- Configurations) are (55%) and (51%) at w

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Publication Date
Fri Jan 01 2016
Journal Name
Bio-inspired Computing – Theories And Applications
Image Segmentation Using Membrane Computing: A Literature Survey
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Publication Date
Sat Feb 09 2019
Journal Name
Journal Of The College Of Education For Women
Medical Image Segmentation using Modified Interactive Thresholding Technique
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Medical image segmentation is one of the most actively studied fields in the past few decades, as the development of modern imaging modalities such as magnetic resonance imaging (MRI) and computed tomography (CT), physicians and technicians nowadays have to process the increasing number and size of medical images. Therefore, efficient and accurate computational segmentation algorithms become necessary to extract the desired information from these large data sets. Moreover, sophisticated segmentation algorithms can help the physicians delineate better the anatomical structures presented in the input images, enhance the accuracy of medical diagnosis and facilitate the best treatment planning. Many of the proposed algorithms could perform w

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Publication Date
Sun Mar 30 2025
Journal Name
Iraqi Journal Of Science
Segmentation of Aerial Images Using Different Clustering Techniques
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The segmentation of aerial images using different clustering techniques offers valuable insights into interpreting and analyzing such images. By partitioning the images into meaningful regions, clustering techniques help identify and differentiate various objects and areas of interest, facilitating various applications, including urban planning, environmental monitoring, and disaster management. This paper aims to segment color aerial images to provide a means of organizing and understanding the visual information contained within the image for various applications and research purposes. It is also important to look into and compare the basic workings of three popular clustering algorithms: K-Medoids, Fuzzy C-Mean (FCM), and Gaussia

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Publication Date
Fri Jan 19 2024
Journal Name
Research Journal Of Pharmacy And Technology
Serum Levels of Fetuin-A, Ischemia-modified Albumin (IMA), and Ferritin in Hospitalized patients with severe COVID-19. A Case-control Study
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Objective: To measure the serum levels of Fetuin-A, ischemia-modified albumin (IMA), and ferritin in hospitalized patients with severe COVID-19in Baghdad, Iraq. Moreover, to determine these biomarkers' cut-off valuesthat differentiate between severely ill patients and control subjects. Methods: This case-control study was done from 15 September to the end of December 2021 and involved a review of the files and collectionof blood samples from patients (n=45, group1) hospitalized in COVID-19 treatment centersbecause of severe symptoms compared tohealthy subjects as controls (n=44, group2). Results: Fetuin-A serum levels were not statistically different between patients and controls. In contrast, IMA and ferritin levels were significan

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Publication Date
Wed Dec 22 2021
Journal Name
Plos One
Awareness and preparedness of healthcare workers against the first wave of the COVID-19 pandemic: A cross-sectional survey across 57 countries
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Background

Since the COVID-19 pandemic began, there have been concerns related to the preparedness of healthcare workers (HCWs). This study aimed to describe the level of awareness and preparedness of hospital HCWs at the time of the first wave.

Methods

This multinational, multicenter, cross-sectional survey was conducted among hospital HCWs from February to May 2020. We used a hierarchical logistic regression multivariate analysis to adjust the influence of variables based on awareness and preparedness. We then used association rule mining to identify relationships between HCW confidence in handling suspected

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Publication Date
Tue Dec 01 2020
Journal Name
Eurasian Journal Of Biosciences
Utilizing remote sensing for studying Al-Saadya Marsh in the period 1987-2017
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The marshes are one of the important environmental features affecting human and animal systems, so the studying of changes they undergo is one of the important topics. This study is concerned with the changes occurring in the Al Saadya marsh for the period from 1987 to 2017 exclusively in the winter season (the marshes’ revival season in Iraq revive). In order to inspect the changes in this marsh, we choose 7 years to cover the study period as a criterion years, namely 1987, 1990, 1995, 2000, 2007, 2014 and 2017. The “Maximum Likelihood” classifier was used to separate the stacked land cover features, where the minimum overall accuracy ratio that recorded for all years of study was 96%. The results revealed that Al-Saadya marsh went t

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Publication Date
Sat Oct 18 2025
Journal Name
Pattern Recognition And Artificial Intelligence
Utilizing Energy-Efficient Deep Learning Technique for Age Estimation Through a Hybrid Methodology
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This study employs evolutionary optimization and Artificial Intelligence algorithms to determine an individual’s age using a single-faced image as the basis for the identification process. Additionally, we used the WIKI dataset, widely considered the most comprehensive collection of facial images to date, including descriptions of age and gender attributes. However, estimating age from facial images is a recent topic of study, even though much research has been undertaken on establishing chronological age from facial photographs. Retrained artificial neural networks are used for classification after applying reprocessing and optimization techniques to achieve this goal. It is possible that the difficulty of determining age could be reduce

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