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An Efficient Mixture of Deep and Machine Learning Models for COVID-19 and Tuberculosis Detection Using X-Ray Images in Resource Limited Settings
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Publication Date
Wed Mar 10 2021
Journal Name
Baghdad Science Journal
Detecting Textual Propaganda Using Machine Learning Techniques
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Social Networking has dominated the whole world by providing a platform of information dissemination. Usually people share information without knowing its truthfulness. Nowadays Social Networks are used for gaining influence in many fields like in elections, advertisements etc. It is not surprising that social media has become a weapon for manipulating sentiments by spreading disinformation.  Propaganda is one of the systematic and deliberate attempts used for influencing people for the political, religious gains. In this research paper, efforts were made to classify Propagandist text from Non-Propagandist text using supervised machine learning algorithms. Data was collected from the news sources from July 2018-August 2018. After annota

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Publication Date
Fri Dec 01 2023
Journal Name
Bulletin Of Electrical Engineering And Informatics
A comparative study of Gaussian mixture algorithm and K-means algorithm for efficient energy clustering in MWSN
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Wireless sensor networks (WSNs) represent one of the key technologies in internet of things (IoTs) networks. Since WSNs have finite energy sources, there is ongoing research work to develop new strategies for minimizing power consumption or enhancing traditional techniques. In this paper, a novel Gaussian mixture models (GMMs) algorithm is proposed for mobile wireless sensor networks (MWSNs) for energy saving. Performance evaluation of the clustering process with the GMM algorithm shows a remarkable energy saving in the network of up to 92%. In addition, a comparison with another clustering strategy that uses the K-means algorithm has been made, and the developed method has outperformed K-means with superior performance, saving ener

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Publication Date
Fri Jan 21 2022
Journal Name
Environmental Science And Pollution Research
Development of new computational machine learning models for longitudinal dispersion coefficient determination: case study of natural streams, United States
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Publication Date
Mon Jan 01 2024
Journal Name
Journal Of Image And Graphics
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 fre

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Publication Date
Tue Sep 11 2018
Journal Name
Iraqi Journal Of Physics
Contrast enhancement of infrared images using Adaptive Histogram Equalization (AHE) with Contrast Limited Adaptive Histogram Equalization (CLAHE)
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The objective of this paper is to improve the general quality of infrared images by proposes an algorithm relying upon strategy for infrared images (IR) enhancement. This algorithm was based on two methods: adaptive histogram equalization (AHE) and Contrast Limited Adaptive Histogram Equalization (CLAHE). The contribution of this paper is on how well contrast enhancement improvement procedures proposed for infrared images, and to propose a strategy that may be most appropriate for consolidation into commercial infrared imaging applications.
The database for this paper consists of night vision infrared images were taken by Zenmuse camera (FLIR Systems, Inc) attached on MATRIC100 drone in Karbala city. The experimental tests showed sign

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Publication Date
Fri Jan 15 2021
Journal Name
المجلة العربية للبحوث الادبية والانسانية
Service Leadership and Customer Satisfaction Under COVID-19 Pandemic Crisis : An Empirical Research for Leaders of Public Organizations in Iraq
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The current research aims to identify the degree to which a sample of managers in public organizations appreciated the level of application of the service leadership style from their point of view, and its relationship to the customer satisfaction index in light of the (Covid-19) pandemic, to achieve this, the researcher followed the experimental approach by applying a questionnaire that included two axes, The first: to measure the level of service leadership according to the scale (D. Van Dierendonck and I. Nuijten, 2011), which includes (8) dimensions (empowerment, stand back, accountability, courage, forgiveness, Authenticity, humility, stewardship). The second axis: to measure the level of customer satisfaction according to (Askim, 2004

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Publication Date
Mon Mar 14 2022
Journal Name
Periodicals Of Engineering And Natural Sciences (pen)
Mathematical simulation of memristive for classification in machine learning
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Publication Date
Mon Dec 12 2022
Journal Name
Geodesy And Cartography
SPATIOTEMPORAL ANALYSIS FOR FIGHTING COVID-19 IN IRAQ
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At the end of 2019, a new form of Coronavirus (later dubbed COVID-19) emerged in China and quickly spread to other regions of the globe. Despite the virus’s unique and unknown characteristics, it is a widely distributed infectious illness. Finding the geographical distribution of the virus transmission is therefore critical for epidemiologists and governments in order to respond to the illness epidemic rapidly and effectively. Understanding the dynamics of COVID-19’s spatial distribution can help to understand the pandemic’s scope and effects, as well as decision-making, planning, and community action aimed at preventing transmission. The main focus of this study is to investigate the geographic patterns of COVID-19 disseminat

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Publication Date
Tue Jan 11 2022
Journal Name
3rd International Scientific Conference Of Alkafeel University (iscku 2021)
Elimination of the broadening in X-ray diffraction lines profile for nanoparticles by using the analysis of diffraction lines method
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In this research, the results of the Integral breadth method were used to analyze the X-ray lines to determine the crystallite size and lattice strain of the zirconium oxide nanoparticles and the value of the crystal size was equal to (8.2nm) and the lattice strain (0.001955), and then the results were compared with three other methods, which are the Scherer and Scherer dynamical diffraction theory and two formulas of the Scherer and Wilson method.the results were as followsScherer crystallite size(7.4nm)and lattice strain(0.011968),Schererdynamic method crystallite size(7.5 nm),Scherrer and Wilson methodcrystallite size( 8.5nm) and lattice strain( 0.001919).And using another formula for Schearer and Wilson methodwe obtain the size of the c

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Publication Date
Wed May 17 2023
Journal Name
International Journal Of Computational Intelligence Systems
Prediction of ROP Zones Using Deep Learning Algorithms and Voting Classifier Technique
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Abstract<p>Retinopathy of prematurity (ROP) can cause blindness in premature neonates. It is diagnosed when new blood vessels form abnormally in the retina. However, people at high risk of ROP might benefit significantly from early detection and treatment. Therefore, early diagnosis of ROP is vital in averting visual impairment. However, due to a lack of medical experience in detecting this condition, many people refuse treatment; this is especially troublesome given the rising cases of ROP. To deal with this problem, we trained three transfer learning models (VGG-19, ResNet-50, and EfficientNetB5) and a convolutional neural network (CNN) to identify the zones of ROP in preterm newborns. The dataset to train th</p> ... Show More
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