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A New Model Design for Combating COVID -19 Pandemic Based on SVM and CNN Approaches
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       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 lung CT-scans into two groups (COVID-19 and NonCOVID-19) had been proposed. A dataset used is 960 slices of CT scan collected from Iraqi patients /Ibn Al-Nafis teaching hospital. The performance metrics are used in this study (accuracy, recall, precision, and F1 scores). The results indicate that the proposed approach generated a high-quality model for the collected dataset, with an overall accuracy of 98.95% and an overall recall of 97 %.

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Publication Date
Wed Nov 01 2017
Journal Name
Journal Of Economics And Administrative Sciences
" employees voices behaviors: Theoretical concepts and thinking approaches
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The concept of employees voice has received a great deal of attention by researchers in the field of organizational behavior and human resources management especially in the last three decades of the twentieth century , this importance has deep ranges limits in terms of its discussion history, so it became a behavioral variable received a great attention and care in managerial and organizational studied and basic pillar in the success and excellence of organizations in maintaining its human resources, the research explain the concept and benefits of paying attention to the voice of employees in business organizations , and the theories interpreted to employees voicing and clearing the motivations behind employees voicing, and dis

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Publication Date
Tue Aug 01 2023
Journal Name
International Journal Of Online And Biomedical Engineering (ijoe)
End-to-End Speaker Profiling Using 1D CNN Architectures and Filter Bank Initialization
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The automatic estimation of speaker characteristics, such as height, age, and gender, has various applications in forensics, surveillance, customer service, and many human-robot interaction applications. These applications are often required to produce a response promptly. This work proposes a novel approach to speaker profiling by combining filter bank initializations, such as continuous wavelets and gammatone filter banks, with one-dimensional (1D) convolutional neural networks (CNN) and residual blocks. The proposed end-to-end model goes from the raw waveform to an estimated height, age, and gender of the speaker by learning speaker representation directly from the audio signal without relying on handcrafted and pre-computed acou

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Publication Date
Sat Apr 15 2023
Journal Name
Journal Of Robotics
A New Proposed Hybrid Learning Approach with Features for Extraction of Image Classification
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Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

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Publication Date
Tue Jun 30 2020
Journal Name
Dialogo
The Problems of Applying Psychological, Educational and Political Measurement Instruments in light of the Prevalence of the Corona virus (Covid-19)
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Publication Date
Fri Jan 20 2023
Journal Name
Bionatura
Impact of inflammatory markers, dread diseases and cycle threshold (Ct) Values in COVID-19 progression. Revis Bionatura 2023; 8 (1) 33
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Publication Date
Sat Dec 30 2023
Journal Name
Journal Of The College Of Education For Women
Deception in Amber Heard and Johnny Depp’s Trials
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Deception is defined as a linguistic and non-linguistic behavior that is used in interaction in order to make the addressees believe what is believed to be false or lack evidence. McCornack (1992) classifies deception into four manipulative strategies (i.e., fabrication, distortion, equivocation and concealment), other scholars argue that deception encompasses the strategies of “fabrication (outright lying), equivocation (being vague and ambiguous), or concealment (with holding relevant information) Thus, the present study investigates the deception strategies and motives that are used by Johnny Depp and Amber Heard during their defamation trials. Qualitative and quantitative methods are employed when analyzing the data in question. Th

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Publication Date
Mon Mar 23 2020
Journal Name
Journal Of Engineering
Diagnosing Water Problem for Asmari Reservoir in Abu Ghirab Oilfield Using Analytical and Numerical Approaches
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Asmari is the main productive reservoir in Abu Ghirab oilfield in the south-east part of Iraq. It has history production extends from 1976 up to now with several close periods. Recently, the reservoir suffers some problems in production, which are abstracted as water production rising with oil production declining in most wells. The water problem type of the field and wells is identified by using Chan's diagnostic plots (water oil ratio (WOR) and derivative water oil ratio (WOR') against time). The analytical results show that water problem is caused by the channeling due to high permeability zones, high water saturation zones, and faults or fracturing. The numerical approach is also used to study the water movement insi

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Publication Date
Mon Mar 23 2020
Journal Name
Journal Of Engineering
Diagnosing Water Problem for Asmari Reservoir in Abu Ghirab Oilfield Using Analytical and Numerical Approaches
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Asmari is the main productive reservoir in Abu Ghirab oilfield in the south-east part of Iraq. It has history production extends from 1976 up to now with several close periods. Recently, the reservoir suffers some problems in production, which are abstracted as water production rising with oil production declining in most wells. The water problem type of the field and wells is identified by using Chan's diagnostic plots (water oil ratio (WOR) and derivative water oil ratio (WOR') against time). The analytical results show that water problem is caused by the channeling due to high permeability zones, high water saturation zones, and faults or fracturing. The numerical approach is also used to study the water movement inside the reser

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Publication Date
Mon Dec 03 2018
Journal Name
Journal Of Engineering
Variable Structure Control Design for a Magnetic Levitation System
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In this paper the variable structure control theory is utilized to derive a discontinuous controller to the magnetic levitation system. The magnetic levitation system model is considered uncertain, which subjected to the uncertainty in system parameters, also it is open-loop unstable and strongly nonlinear. The proposed variable structure control to magnetic levitation system is proved, and the area of attraction is determined. Additionally, the chattering, which induced due to the discontinuity in control law, is attenuated by using a non-smooth approximate. With this approximation the resulted controller is a continuous variable structure controller with a determined steady state error according to the selected control

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Publication Date
Sun Jan 01 2023
Journal Name
Journal Of Robotics And Control (jrc)
Automated Stand-alone Surgical Safety Evaluation for Laparoscopic Cholecystectomy (LC) using Convolutional Neural Network and Constrained Local Models (CNN-CLM)
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In this golden age of rapid development surgeons realized that AI could contribute to healthcare in all aspects, especially in surgery. The aim of the study will incorporate the use of Convolutional Neural Network and Constrained Local Models (CNN-CLM) which can make improvement for the assessment of Laparoscopic Cholecystectomy (LC) surgery not only bring opportunities for surgery but also bring challenges on the way forward by using the edge cutting technology. The problem with the current method of surgery is the lack of safety and specific complications and problems associated with safety in each laparoscopic cholecystectomy procedure. When CLM is utilize into CNN models, it is effective at predicting time series tasks like iden

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