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Network Traffic Prediction Based on Time Series Modeling
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    Predicting the network traffic of web pages is one of the areas that has increased focus in recent years. Modeling traffic helps find strategies for distributing network loads, identifying user behaviors and malicious traffic, and predicting future trends. Many statistical and intelligent methods have been studied to predict web traffic using time series of network traffic. In this paper, the use of machine learning algorithms to model Wikipedia traffic using Google's time series dataset is studied. Two data sets were used for time series, data generalization, building a set of machine learning models (XGboost, Logistic Regression, Linear Regression, and Random Forest), and comparing the performance of the models using (SMAPE) and (MAPE). The results showed the possibility of modeling the network traffic time series and that the performance of the linear regression model is the best compared to the rest of the models for both series.

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Publication Date
Wed Feb 29 2012
Journal Name
Al-khwarizmi Engineering Journal
WLAN Integrated with GPRS Network Securely
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In this paper a WLAN network that accesses the Internet through a GPRS network was implemented and tested. The proposed network is managed by the Linux based server. Because of the limited facilities of GPRS such as dynamic IP addressing besides to its limited bandwidth a number of techniques are implemented to overcome these limitations.

      Dynamic Host Configuration Protocol (DHCP) server was added to provide a single central control for all TCP/IP resources. Squid Proxy  was added to provide caching of the redundant accessed Web content to reduce the Internet bandwidth usage and speeding up the client’s download time. Network Address Translation (NAT) service was configured to share one IP ad

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Publication Date
Sun Jun 06 2010
Journal Name
Baghdad Science Journal
Using Neural Network with Speaker Applications
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In Automatic Speech Recognition (ASR) the non-linear data projection provided by a one hidden layer Multilayer Perceptron (MLP), trained to recognize phonemes, and has previous experiments to provide feature enhancement substantially increased ASR performance, especially in noise. Previous attempts to apply an analogous approach to speaker identification have not succeeded in improving performance, except by combining MLP processed features with other features. We present test results for the TIMIT database which show that the advantage of MLP preprocessing for open set speaker identification increases with the number of speakers used to train the MLP and that improved identification is obtained as this number increases beyond sixty.

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Publication Date
Sun Sep 07 2008
Journal Name
Baghdad Science Journal
Hybrid Cipher System using Neural Network
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The objective of this work is to design and implement a cryptography system that enables the sender to send message through any channel (even if this channel is insecure) and the receiver to decrypt the received message without allowing any intruder to break the system and extracting the secret information. In this work, we implement an interaction between the feedforward neural network and the stream cipher, so the secret message will be encrypted by unsupervised neural network method in addition to the first encryption process which is performed by the stream cipher method. The security of any cipher system depends on the security of the related keys (that are used by the encryption and the decryption processes) and their corresponding le

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Publication Date
Tue Jan 01 2019
Journal Name
Journal Of Communications
SDN Implementation in Data Center Network
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Publication Date
Fri Dec 24 2021
Journal Name
Iraqi Journal Of Science
Drought Spatiotemporal Characteristics Based on a Vegetation Condition Index in Erbil, Kurdistan Region, Iraq
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     Drought is a complex phenomenon that has severe impacts on the environment. Vegetation and its conditions are very sensitive to drought effects. This study aimed to monitor and assess the drought severity and its relationships to some ecological variables in ten districts of Erbil Governorate (Kurdistan Region), Iraq, throughout 20 years (1998-2017). The results revealed that droughts frequently hit Erbil throughout the study period. The Landsat time-series- based on Vegetation Condition Index (VCI) significantly correlated with precipitation, Digital Elevation Model (DEM), and latitude. Extreme VCI-based drought area percentages were recorded in 1999, 2000, 2008, and 2011 by 43.4%, 67.9%, 43.3%, and 40.0%, respe

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Publication Date
Tue Jun 30 2015
Journal Name
Al-khwarizmi Engineering Journal
Studying and Modeling the Effect of Graphite Powder Mixing Electrical Discharge Machining on the Main Process Characteristics
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Abstract

This paper concerned with study the effect of a graphite micro powder mixed in the kerosene dielectric fluid during powder mixing electric discharge machining (PMEDM) of high carbon high chromium AISI D2 steel. The type of electrode (copper and graphite), the pulse current and the pulse-on time and mixing powder in kerosene dielectric fluid are taken as the process main input parameters. The material removal rate MRR, the tool wear ratio TWR and the work piece surface roughness (SR) are taken as output parameters to measure the process performance. The experiments are planned using response surface methodology (RSM) design procedure. Empirical models are developed for MRR, TWR and SR, using the analysis

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Publication Date
Fri Jun 29 2018
Journal Name
Journal Of Engineering
Finite Element Modeling and Parametric Study on Floor Steel Beam Concrete Slab System in Non-Composite Action.
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This study aims to show, the strength of steel beam-concrete slab system without using shear connectors (known as a non-composite action), where the effect of the friction force between the concrete slab and the steel beam has been investigated, by using finite element simulation.

The proposed finite element model has been verified based on comparison with an experimental work. Then, the model was adopted to study the system strength with a different steel beam and concrete slab profile. ABAQUS has been adopted in the preparation of all numerical models for this study.

After validation of the numerical models, a parametric study was conducted, with linear and non-linear Regression analysis. An equation re

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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 Mar 30 2023
Journal Name
Iraqi Journal Of Science
Low Energy Consumption Scheme Based on PEGASIS Protocol in WSNs
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Wireless Sensor Networks (WSNs) are composed of a collection of rechargeable sensor nodes. Typically, sensor nodes collect and deliver the necessary data in response to a user’s specific request in many application areas such as health, military and domestic purposes. Applying routing protocols for sensor nodes can prolong the lifetime of the network. Power Efficient GAthering in Sensor Information System (PEGASIS) protocol is developed as a chain based protocol that uses a greedy algorithm in selecting one of the nodes as a head node to transmit the data to the base station. The proposed scheme Multi-cluster Power Efficient GAthering in Sensor Information System (MPEGASIS) is developed based on PEGASIS routing protocol in WSN. The aim

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Publication Date
Sat Oct 28 2023
Journal Name
Baghdad Science Journal
Numerical Modeling of Renal Ionic Equilibrium for Implantable Kidney Applications
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The human kidney is one of the most important organs in the human body; it performs many functions
and has a great impact on the work of the rest of the organs. Among the most important possible treatments is
dialysis, which works as an external artificial kidney, and several studies have worked to enhance the
mechanism of dialysate flow and improve the permeability of its membrane. This study introduces a new
numerical model based on previous research discussing the variations in the concentrations of sodium,
potassium, and urea in the extracellular area in the blood during hemodialysis. We simulated the differential
equations related to mass transfer diffusion and we developed the model in MATLAB Simu

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