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bsj-2989
A Software Defined Network of Video Surveillance System Based on Enhanced Routing Algorithms
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Software Defined Network (SDN) is a new technology that separate the ‎control plane from the data plane. SDN provides a choice in automation and ‎programmability faster than traditional network. It supports the ‎Quality of Service (QoS) for video surveillance application. One of most ‎significant issues in video surveillance is how to find the best path for routing the packets ‎between the source (IP cameras) and destination (monitoring center). The ‎video surveillance system requires fast transmission and reliable delivery ‎and high QoS. To improve the QoS and to achieve the optimal path, the ‎SDN architecture is used in this paper. In addition, different routing algorithms are ‎used with different steps. First, we evaluate the video transmission over the SDN with ‎Bellman Ford algorithm. Then, because the limitation of Bellman ford ‎algorithm, the Dijkstra algorithm is used to change the path when a congestion occurs. Furthermore, the Dijkstra algorithm is used with two ‎controllers to reduce the time consumed by the SDN controller. ‎ POX and Pyretic SDN controllers are used such that POX controller is ‎responsible for the network monitoring, while Pyretic controller is responsible for the ‎routing algorithm and path selection. Finally, a modified Dijkstra algorithm is further proposed and evaluated with two ‎controllers to enhance the performance.  The results show that the modified Dijkstra algorithm outperformed the other approaches in the aspect of QoS parameters.

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Publication Date
Thu Oct 13 2022
Journal Name
Computation
A Pattern-Recognizer Artificial Neural Network for the Prediction of New Crescent Visibility in Iraq
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Various theories have been proposed since in last century to predict the first sighting of a new crescent moon. None of them uses the concept of machine and deep learning to process, interpret and simulate patterns hidden in databases. Many of these theories use interpolation and extrapolation techniques to identify sighting regions through such data. In this study, a pattern recognizer artificial neural network was trained to distinguish between visibility regions. Essential parameters of crescent moon sighting were collected from moon sight datasets and used to build an intelligent system of pattern recognition to predict the crescent sight conditions. The proposed ANN learned the datasets with an accuracy of more than 72% in comp

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Publication Date
Mon Apr 01 2019
Journal Name
Journal Of Educational And Psychological Researches
The Effectiveness of a Teaching Strategy Based on the Cognitive Model of Daniel in the Development of Achievement and the Motivation of learning the School Mathematics among the Third Intermediate Grade Students
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This research aims to examine the effectiveness of a teaching strategy based on the cognitive model of Daniel in the development of achievement and the motivation of learning the school mathematics among the third intermediate grade students in the light of their study of "Systems of Linear Equations”. The research was conducted in the first semester (1439/1440AH), at Saeed Ibn Almosaieb Intermediate School, in Arar, Saudi Arabia. A quasi-experimental design has been used. In addition, a (pre & post) achievement test (20 Questions) and a (pre & post) scale of learning motivation to the school mathematics (25 Items) have been applied on two groups: a control group (31Students), and an experimental group (29 Students). The resear

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Publication Date
Mon Jan 01 2024
Journal Name
Aip Conference Proceedings
Lab on-a-chip-based, an integrated microfluidic device lo-cost, rapid, and sensitive analysis of Augmentin
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Microfluidic devices provide distinct benefits for developing effective drug assays and screening. The microfluidic platforms may provide a faster and less expensive alternative. Fluids are contained in devices with considerable micrometer-scale dimensions. Owing to this tight restriction, drug assay quantities are minute (milliliters to femtoliters). In this research, a microfluidic chip consisting of micro-channels carved on substrate materials built using an Acrylic (Polymethyl Methacrylate, PMMA) chip was designed using a Carbon Dioxide (CO2) laser machine. The CO2 parameters influence the chip’s width, depth, and roughness. To have a regular channel surface, and low roughness, the laser power (60 W), with scanning speed (250 m/s)

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Publication Date
Mon Feb 01 2021
Journal Name
Journal Of Engineering
Electrical Conductivity as a General Predictor of Multiple Parameters in Tigris River Based on Statistical Regression Model
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Surface water samples from different locations within Tigris River's boundaries in Baghdad city have been analyzed for drinking purposes. Correlation coefficients among different parameters were determined. An attempt has been made to develop linear regression equations to predict the concentration of water quality constituents having significant correlation coefficients with electrical conductivity (EC). This study aims to find five regression models produced and validated using electrical conductivity as a predictor to predict total hardness (TH), calcium (Ca), chloride (Cl), sulfate (SO4), and total dissolved solids (TDS). The five models showed good/excellent prediction ability of the parameters mentioned

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Publication Date
Mon Feb 01 2021
Journal Name
Https://www.researchgate.net/journal/university-of-baghdad-engineering-journal-1726-4073
Electrical Conductivity as a General Predictor of Multiple Parameters in Tigris River Based on Statistical Regression Model
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Surface water samples from different locations within Tigris River's boundaries in Baghdad city have been analyzed for drinking purposes. Correlation coefficients among different parameters were determined. An attempt has been made to develop linear regression equations to predict the concentration of water quality constituents having significant correlation coefficients with electrical conductivity (EC). This study aims to find five regression models produced and validated using electrical conductivity as a predictor to predict total hardness (TH), calcium (Ca), chloride (Cl), sulfate (SO4), and total dissolved solids (TDS). The five models showed good/excellent prediction ability of the parameters mentioned above, which is a very

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Publication Date
Sun Jul 09 2023
Journal Name
Journal Of Engineering
Numerical Simulation of The Influence of Geometric Parameter on The Flow Behavior in a Solar Chimney Power Plant System
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Numerical simulations have been carried out on the solar chimney power plant systems. This paper gives the flow field analysis for a solar chimney power generation project located in Baghdad. The continuity, Naver-stockes, energy and radiation transfer equations have been solved and carried out by Fluent software. The governing equations are solved for incompressible, 3-D, steady state, turbulent is approximated by a standard k -  model with Boussiuesq approximation to study and evaluate the performance of solar chimney power plant in Baghdad city of Iraq. The different geometric parameters of project are assumed such as collector diameter and chimney height at different working conditions of solar radiation intensity (300,450,600,750

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Publication Date
Mon Jul 01 2024
Journal Name
Journal Of Engineering
Efficient Intrusion Detection Through the Fusion of AI Algorithms and Feature Selection Methods
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With the proliferation of both Internet access and data traffic, recent breaches have brought into sharp focus the need for Network Intrusion Detection Systems (NIDS) to protect networks from more complex cyberattacks. To differentiate between normal network processes and possible attacks, Intrusion Detection Systems (IDS) often employ pattern recognition and data mining techniques. Network and host system intrusions, assaults, and policy violations can be automatically detected and classified by an Intrusion Detection System (IDS). Using Python Scikit-Learn the results of this study show that Machine Learning (ML) techniques like Decision Tree (DT), Naïve Bayes (NB), and K-Nearest Neighbor (KNN) can enhance the effectiveness of an Intrusi

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Publication Date
Wed Dec 01 2021
Journal Name
Civil And Environmental Engineering
Prediction of the Delay in the Portfolio Construction Using Naïve Bayesian Classification Algorithms
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Abstract<p>Projects suspensions are between the most insistent tasks confronted by the construction field accredited to the sector’s difficulty and its essential delay risk foundations’ interdependence. Machine learning provides a perfect group of techniques, which can attack those complex systems. The study aimed to recognize and progress a wellorganized predictive data tool to examine and learn from delay sources depend on preceding data of construction projects by using decision trees and naïve Bayesian classification algorithms. An intensive review of available data has been conducted to explore the real reasons and causes of construction project delays. The results show that the postpo</p> ... Show More
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Publication Date
Tue Feb 05 2019
Journal Name
Journal Of The College Of Education For Women
Social Safety Nets and its role in the protection of widows (Problems and advancement options) A field study in the Social Welfare Department for Women
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Current search problem manifested and widows who community harsh to bear hardships and pains، The goals of continuing the sustainability of life and take responsibility, and especially in light of the difficult circumstances in which Iraq is going through, and the displacement of murder and terrorism, which generated huge numbers of widows and orphans Because of the loss of a breadwinner and which became women and children are the most harm to the victim and as a result of wars and armed tendencies So this research is an important and vital topic opens our horizons important for overlapping roles of women widows and their impact on the achievement and status of Iraqi women and that as long as aptly characterized and their ability to end

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Publication Date
Fri Jan 01 2016
Journal Name
Journal Of Engineering
Improve the Performance of PID Controller by Two Algorithms for Controlling the DC Servo Motor
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The paper uses the Direct Synthesis (DS) method for tuning the Proportional Integral Derivative (PID) controller for controlling the DC servo motor. Two algorithms are presented for enhancing the performance of the suggested PID controller. These algorithms are Back-Propagation Neural Network and Particle Swarm Optimization (PSO). The performance and characteristics of DC servo motor are explained. The simulation results that obtained by using Matlab program show that the steady state error is eliminated with shorter adjusted time when using these algorithms with PID controller. A comparative between the two algorithms are described in this paper to show their effectiveness, which is found that the PSO algorithm gives be

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