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Improving Performance Classification in Wireless Body Area Sensor Networks Based on Machine Learning Techniques
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Wireless Body Area Sensor Networks (WBASNs) have garnered significant attention due to the implementation of self-automaton and modern technologies. Within the healthcare WBASN, certain sensed data hold greater significance than others in light of their critical aspect. Such vital data must be given within a specified time frame. Data loss and delay could not be tolerated in such types of systems. Intelligent algorithms are distinguished by their superior ability to interact with various data systems. Machine learning methods can analyze the gathered data and uncover previously unknown patterns and information. These approaches can also diagnose and notify critical conditions in patients under monitoring. This study implements two supervised machine learning classification techniques, Learning Vector Quantization (LVQ) and Support Vector Machine (SVM) classifiers, to achieve better search performance and high classification accuracy in a heterogeneous WBASN. These classification techniques are responsible for categorizing each incoming packet into normal, critical, or very critical, depending on the patient's condition, so that any problem affecting him can be addressed promptly. Comparative analyses reveal that LVQ outperforms SVM in terms of accuracy at 91.45% and 80%, respectively.

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Publication Date
Fri Jun 30 2017
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Study the Performance of Nanozeolite NaA on CO2 Gas Uptake
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The adsorption isotherms and kinetic uptakes of CO2 were measured. Adsorption isotherms were measured at two temperatures 309 K and 333 K and over a pressure range of 1 to 7 bar. Experimental data of CO2 adsorption isotherms were modeled using Langmuir, Freundlich and Temkin. Based on coefficient of correlation it was found that Langmuir isotherm model was well suited with the experimental data of CO2 adsorption isotherms.  In addition, Adsorption kinetic of CO2 mixture with N2 containing 10 % by volume CO2 and 90 % by volume N2 were determined in a temperature 36 °C and under the atmospheric pressure .When the flow rate was increased from

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Publication Date
Sun Jul 09 2023
Journal Name
Journal Of Engineering
Effect of Operating Conditions on Reverse Osmosis (RO) Membrane Performance
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The aim of this research is to study the effect of high concentrations of salts, pressure and temperature on the performance of the RO membrane with time. Four different (Na2CO3) concentrations (5000, 15000, 25000 and 35000) ppm and various pressures such as (1, 3 and 5) bars at different temperatures of the feed solution (i.e., 25, 35 and 45) ◦C were used in this work. It was found that, as the concentration of salt and feed temperatures increase, the rejection of the salt decrease. While the salt rejection of the membranes increases with increase of transmembrane pressure.

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Publication Date
Mon Mar 07 2022
Journal Name
Construction Research Congress 2022
Annual Revenue’s Influence on the Safety Performance of Construction Firms
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Publication Date
Wed Feb 29 2012
Journal Name
Al-khwarizmi Engineering Journal
Effect of Construction Joints on Performance of Reinforced Concrete Beams
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Construction joints are stopping places in the process of placing concrete, and they are required because in many structures it is impractical to place concrete in one continuous operation. The amount of concrete that can be placed at one time is governed by the batching and mixing capacity and by the strength of the formwork. A good construction joint should provide adequate flexural and shear continuity through the interface.

In this study, the effect of location of construction joints on the performance of reinforced concrete structural elements is experimentally investigated.

Nineteen beam specimens with dimensions of 200×200×950 mm were tested. The variables investigated are the location of the construction joints

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Publication Date
Thu Dec 26 2019
Journal Name
Journal Of Accounting And Financial Studies ( Jafs )
The impact of computerized planning on audit performance (Proposed model)
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The development of technology used in computerized programs is considered on of the most important topics that is responsible for creating tools that can be used in the business environment, the audit profession is one of those professions that received this development. In order for this profession to be more effective, there must be a tool based on sound (correct) scientific basis that can be based upon to enhance the quality of auditing. The research also aims to propose a computerized program to plan the auditing process according to the methods appropriate to the working environment in the audit offices and companies in Iraq. The computerized program was applied to the research sample and the hypothesis of the research has been prov

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Publication Date
Fri Sep 30 2022
Journal Name
Iraqi Journal Of Science
Survey of Some Heavy Metals and Radioactivity in the Dust in A Selected Area in Kirkuk Governorate- Northern Iraq
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      Air pollution means the release of pollutants into the atmosphere, which are harmful to human health and the planet as a whole. Almost all air pollutants come from production and energy use. In the present work, an assessment of some heavy metals, natural radioactivity and the quantity of dust fallen in three sites (Tessen, Rahemawa, and Laylan) in Kirkuk Governorate, northern Iraq. Three dust samples were collected from three locations (residential, commercial and industrial areas). The collected samples were analyzed for Cd, Cr, Cu, Ni, Pb, Zn, and radioactivity (Gamma rays). The studied heavy metals (Fe, Ni, Pb, and Zn) exceeded their limits in the atmosphere due to the increase in the number of automobiles, which

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Publication Date
Tue Jun 01 2021
Journal Name
2021 Ieee/cvf Conference On Computer Vision And Pattern Recognition Workshops (cvprw)
Alps: Adaptive Quantization of Deep Neural Networks with GeneraLized PositS
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Publication Date
Tue Dec 01 2015
Journal Name
Journal Of Engineering
Modeling and Control of Fuel Cell Using Artificial Neural Networks
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This paper includes an experimental study of hydrogen mass flow rate and inlet hydrogen pressure effect on the fuel cell performance. Depending on the experimental results, a model of fuel cell based on artificial neural networks is proposed. A back propagation learning rule with the log-sigmoid activation function is adopted to construct neural networks model. Experimental data resulting from 36 fuel cell tests are used as a learning data. The hydrogen mass flow rate, applied load and inlet hydrogen pressure are inputs to fuel cell model, while the current and voltage are outputs. Proposed model could successfully predict the fuel cell performance in good agreement with actual data. This work is extended to developed fuel cell feedback

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Publication Date
Thu Nov 21 2019
Journal Name
Journal Of Engineering
Simple 2D chaotic remapping scheme for securing optical communication networks
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In this work, a simple and new method is proposed to simultaneously improve the physical layer security and the transmission performance of the optical orthogonal frequency division multiplexing system, by combining orthogonal frequency division multiplexing technique with chaotic theory principles. In the system, a 2-D chaotic map is employed. The introduced system replaces complex operations such as matrix multiplication with simple operations such as multiplexing and inverting. The system performance in terms of bit error rate (BER) and peak to average ratio (PAPR) is enhanced. The system is simulated using Optisystem15 with a MATLAB2016 and for different constellations. The simulation results showed that the  BE

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Publication Date
Thu Mar 01 2007
Journal Name
Al-khwarizmi Engineering Journal
The Inverse Solution Of Dexterous Robot By Using Neural Networks
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The inverse kinematics of redundant manipulators has infinite solutions by using conventional methods, so that, this work presents applicability of intelligent tool (artificial neural network ANN) for finding one desired solution from these solutions. The inverse analysis and trajectory planning of a three link redundant planar robot have been studied in this work using a proposed dual neural networks model (DNNM), which shows a predictable time decreasing in the training session. The effect of the number of the training sets on the DNNM output and the number of NN layers have been studied. Several trajectories have been implemented using point to point trajectory planning algorithm with DNNM and the result shows good accuracy of the end

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