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Diagnose COVID-19 by using hybrid CNN-RNN for Chest X-ray
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<p>Combating the COVID-19 epidemic has emerged as one of the most promising healthcare the world's challenges have ever seen. COVID-19 cases must be accurately and quickly diagnosed to receive proper medical treatment and limit the pandemic. Imaging approaches for chest radiography have been proven in order to be more successful in detecting coronavirus than the (RT-PCR) approach. Transfer knowledge is more suited to categorize patterns in medical pictures since the number of available medical images is limited. This paper illustrates a convolutional neural network (CNN) and recurrent neural network (RNN) hybrid architecture for the diagnosis of COVID-19 from chest X-rays. The deep transfer methods used were VGG19, DenseNet121, InceptionV3, and Inception-ResNetV2. RNN was used to classify data after extracting complicated characteristics from them using CNN. The VGG19-RNN design had the greatest accuracy of all of the networks with 97.8% accuracy. Gradient-weighted the class activation mapping (Grad-CAM) method was then used to show the decision-making areas of pictures that are distinctive to each class. In comparison to other current systems, the system produced promising findings, and it may be confirmed as additional samples become available in the future. For medical personnel, the examination revealed an excellent alternative way of diagnosing COVID-19.</p>

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Publication Date
Fri Aug 05 2016
Journal Name
Wireless Communications And Mobile Computing
A comparison study on node clustering techniques used in target tracking WSNs for efficient data aggregation
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Wireless sensor applications are susceptible to energy constraints. Most of the energy is consumed in communication between wireless nodes. Clustering and data aggregation are the two widely used strategies for reducing energy usage and increasing the lifetime of wireless sensor networks. In target tracking applications, large amount of redundant data is produced regularly. Hence, deployment of effective data aggregation schemes is vital to eliminate data redundancy. This work aims to conduct a comparative study of various research approaches that employ clustering techniques for efficiently aggregating data in target tracking applications as selection of an appropriate clustering algorithm may reflect positive results in the data aggregati

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Publication Date
Fri Jan 31 2020
Journal Name
Iraqi Geological Journal
ESTIMATION OF SHEAR WAVE VELOCITY FROM WIRELINE LOGS DATA FOR AMARA OILFIELD, MISHRIF FORMATION, SOUTHERN IRAQ
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Shear wave velocity is an important feature in the seismic exploration that could be utilized in reservoir development strategy and characterization. Its vital applications in petrophysics, seismic, and geomechanics to predict rock elastic and inelastic properties are essential elements of good stability and fracturing orientation, identification of matrix mineral and gas-bearing formations. However, the shear wave velocity that is usually obtained from core analysis which is an expensive and time-consuming process and dipole sonic imager tool is not commonly available in all wells. In this study, a statistical method is presented to predict shear wave velocity from wireline log data. The model concentrated to predict shear wave velocity fr

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Publication Date
Sun Apr 30 2023
Journal Name
Iraqi Journal Of Science
Doubly Type II Censoring of Two Stress-Strength System Reliability Estimation for Generalized Exponential-Poisson Distribution
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 In this paper, a Bayesian analysis is made to estimate the Reliability of two stress-strength model systems. First: the reliability  of a one component strengths X under stress Y. Second, reliability  of one component strength under three stresses. Where X and Y are independent generalized exponential-Poison random variables with parameters (α,λ,θ) and (β,λ,θ) . The analysis is concerned with and based on doubly type II censored samples using gamma prior under four different loss functions, namely   quadratic loss function, weighted loss functions,  linear and non-linear exponential loss function. The estimators are compared by mean squared error criteria due to a simulation study. We also find that the mean square error is

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Publication Date
Thu Jun 30 2022
Journal Name
Iraqi Geological Journal
Integrating Petrophysical and Geomechanical Rock Properties for Determination of Fracability of the Iraqi Tight Oil Reservoir
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Tight oil reservoirs have been a concerned of the oil industry due to their substantial influence on oil production. Due to their poor permeability, numerous problems are encountered while producing from tight reservoirs. Petrophysical and geomechanical rock properties are essential for understanding and assessing the fracability of reservoirs, especially tight reservoirs, to enhance permeability. In this study, Saadi B reservoir in Halfaya Iraqi oil field is considered as the main tight reservoir. Petrophysical and geomechanical properties have been estimated using full-set well logs for a vertical well that penetrates Saadi reservoir and validated with support of diagnostic fracture injection test data employing standard equations

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Publication Date
Sun Jan 01 2023
Journal Name
Aip Conference Proceedings
Investigations about the effect of electrons screening potential on fusion reactivity for cold fusion of metals
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Publication Date
Sat Jan 01 2022
Journal Name
Journal Of Engineering
Efficient Energy Management for a Proposed Integrated Internet of Things-Electric Smart Meter (2IOT-ESM) System
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In this work, an efficient energy management (EEM) approach is proposed to merge IoT technology to enhance electric smart meters by working together to satisfy the best result of the electricity customer's consumption. This proposed system is called an integrated Internet of things for electrical smart meter (2IOT-ESM) architecture. The electric smart meter (ESM) is the first and most important technique used to measure the active power, current, and energy consumption for the house’s loads. At the same time, the effectiveness of this work includes equipping ESM with an additional storage capacity that ensures that the measurements are not lost in the event of a failure or sudden outage in WiFi network. Then then these measurement

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Publication Date
Sun Mar 04 2018
Journal Name
Iraqi Journal Of Science
Influence of heat transfer on Magneto hydrodynamics oscillatory flow for Williamson fluid through a porous medium
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In this paper, we have examined the influence of heat- transfer on the magnetohydrodynamics oscillatory flow of Williamson fluid during porous medium for two types of geometries "Poiseuille flow and Couette flow". We use perturbation technique in terms of the Weissenberg number to obtain explicit forms for velocity profiles. The results that obtained are illustrated by graphs.

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Publication Date
Thu Mar 30 2023
Journal Name
Iraqi Journal Of Science
Theoretical Study of Bonds length, Energetic and Vibration Frequencies for Construction Units of (6,0) ZigZag SWCNTs
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Density Functional Theory (DFT) calculation of the type (B3LYP) and 6-311G basis set level using Gaussian-03 program were carried out for equilibrium geometry of construction units of (6,0) linear ZigZag SWCNT (mono, Di, Tri and Tetra ring layers), to evaluate the geometrical structure (bond length), symmetries, physical properties and energetic such as standard heat of formation (ΔH0f), total energy (Etot.), dipole moment (μ), Highest Occupied Molecular Orbital Energy (EHOMO), Lowest Unoccupied Molecular Orbital Energy (ELUMO), energy gap (ΔEHOMO-LUMO), the distribution of electron density () and vibration frequencies, all at their equilibrium geometries. Assignment of the vibration frequencies according to the group theory was do

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Publication Date
Sat Oct 01 2011
Journal Name
Journal Of Engineering
MODIFIED TRAINING METHOD FOR FEEDFORWARD NEURAL NETWORKS AND ITS APPLICATION in 4-LINK SCARA ROBOT IDENTIFICATION
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In this research the results of applying Artificial Neural Networks with modified activation function to
perform the online and offline identification of four Degrees of Freedom (4-DOF) Selective Compliance
Assembly Robot Arm (SCARA) manipulator robot will be described. The proposed model of
identification strategy consists of a feed-forward neural network with a modified activation function that
operates in parallel with the SCARA robot model. Feed-Forward Neural Networks (FFNN) which have
been trained online and offline have been used, without requiring any previous knowledge about the
system to be identified. The activation function that is used in the hidden layer in FFNN is a modified
version of the wavelet func

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Publication Date
Fri Aug 13 2021
Journal Name
Journal Européen Des Systèmes Automatisés
Proxy-based sliding mode vibration control with an adaptive approximation compensator for euler-bernoulli smart beams
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Proxy-based sliding mode control PSMC is an improved version of PID control that combines the features of PID and sliding mode control SMC with continuously dynamic behaviour. However, the stability of the control architecture maybe not well addressed. Consequently, this work is focused on modification of the original version of the proxy-based sliding mode control PSMC by adding an adaptive approximation compensator AAC term for vibration control of an Euler-Bernoulli beam. The role of the AAC term is to compensate for unmodelled dynamics and make the stability proof more easily. The stability of the proposed control algorithm is systematically proved using Lyapunov theory. Multi-modal equation of motion is derived using the Galerkin metho

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