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Survey on intrusion detection system based on analysis concept drift: Status and future directions
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Nowadays, internet security is a critical concern; the One of the most difficult study issues in network security is "intrusion detection". Fight against external threats. Intrusion detection is a novel method of securing computers and data networks that are already in use. To boost the efficacy of intrusion detection systems, machine learning and deep learning are widely deployed. While work on intrusion detection systems is already underway, based on data mining and machine learning is effective, it requires to detect intrusions by training static batch classifiers regardless considering the time-varying features of a regular data stream. Real-world problems, on the other hand, rarely fit into models that have such constraints. Furthermore, various uses in the real world, Data distributions in intrusion detection systems, for example, are non-stationary, which produce concept drift over time or non-stationary learning. The word "concept drift" is used to describe the process of changing one's mind about something in an online-supervised learning scenario, the connection between the input data and the target variable changes over time. We define adaptive learning, classify existing concept drift strategies, evaluate the most typical, distinct, and widely used approaches and algorithms, describe adaptive algorithm assessment methodology, and show a collection of examples, all of this is based on the assumption that you have a basic understanding of supervised learning. The survey examines the various aspects of concept drift in a comprehensive manner in order to think about the current fragmented "state-of-the-art". As a result, which intends to give scholars, industry analysts, and practitioners a comprehensive introduction to idea drift adaptability.

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Publication Date
Tue Jan 12 2016
Journal Name
Wireless Networks
Low communication cost (LCC) scheme for localizing mobile wireless sensor networks
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In recent years, the number of applications utilizing mobile wireless sensor networks (WSNs) has increased, with the intent of localization for the purposes of monitoring and obtaining data from hazardous areas. Location of the event is very critical in WSN, as sensing data is almost meaningless without the location information. In this paper, two Monte Carlo based localization schemes termed MCL and MSL* are studied. MCL obtains its location through anchor nodes whereas MSL* uses both anchor nodes and normal nodes. The use of normal nodes would increase accuracy and reduce dependency on anchor nodes, but increases communication costs. For this reason, we introduce a new approach called low communication cost schemes to reduce communication

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Publication Date
Tue May 30 2023
Journal Name
Iraqi Journal Of Science
Deep Convolutional Neural Network Architecture to Detect COVID-19 from Chest X-Ray Images
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      Today, the world is living in a time of epidemic diseases that spread unnaturally and infect and kill millions of people worldwide. The COVID-19 virus, which is one of the most well-known epidemic diseases currently spreading, has killed more than six million people as of May 2022. The World Health Organization (WHO) declared the 2019 coronavirus disease (COVID-19) after an outbreak of SARS-CoV-2 infection. COVID-19 is a severe and potentially fatal respiratory disease caused by the SARS-CoV-2 virus, which was first noticed at the end of 2019 in Wuhan city. Artificial intelligence plays a meaningful role in analyzing medical images and giving accurate results that serve healthcare workers, especially X-ray images, which are co

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Publication Date
Mon Mar 11 2019
Journal Name
Baghdad Science Journal
Solving Mixed Volterra - Fredholm Integral Equation (MVFIE) by Designing Neural Network
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       In this paper, we focus on designing feed forward neural network (FFNN) for solving Mixed Volterra – Fredholm Integral Equations (MVFIEs) of second kind in 2–dimensions. in our method, we present a multi – layers model consisting of a hidden layer which has five hidden units (neurons) and one linear output unit. Transfer function (Log – sigmoid) and training algorithm (Levenberg – Marquardt) are used as a sigmoid activation of each unit. A comparison between the results of numerical experiment and the analytic solution of some examples has been carried out in order to justify the efficiency and the accuracy of our method.

         

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Publication Date
Mon Dec 20 2021
Journal Name
Baghdad Science Journal
Recurrent Stroke Prediction using Machine Learning Algorithms with Clinical Public Datasets: An Empirical Performance Evaluation
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Recurrent strokes can be devastating, often resulting in severe disability or death. However, nearly 90% of the causes of recurrent stroke are modifiable, which means recurrent strokes can be averted by controlling risk factors, which are mainly behavioral and metabolic in nature. Thus, it shows that from the previous works that recurrent stroke prediction model could help in minimizing the possibility of getting recurrent stroke. Previous works have shown promising results in predicting first-time stroke cases with machine learning approaches. However, there are limited works on recurrent stroke prediction using machine learning methods. Hence, this work is proposed to perform an empirical analysis and to investigate machine learning al

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Publication Date
Tue Feb 28 2023
Journal Name
Journal Of Accounting And Financial Studies ( Jafs )
The Importance of Using Concentrator Solar Energy Technologies in Anbar Province
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The aim of the research is a techno-economic analysis of the use of concentrated solar energy technologies in the Iraqi city, considering the concentrated solar energy technology is a renewable energy technology that derives its resources from the sun and is replenished at a rate that exceeds its use. It is also inexhaustible and environmentally friendly energy from its environmental footprint, unlike traditional fossil energy which produces greenhouse gases and a major cause of global warming.

This research measures the costs of concentrated solar energy technology to Reduce the effects caused by other energies and work to fill part of the shortfall in the total electricity production, even at a specific percentage, in preparati

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Publication Date
Sat Jul 31 2021
Journal Name
Iraqi Journal Of Science
Studying the Chaotic Dynamics Using Rossler-Chua Systems Combined with A Semiconductor Laser
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     In this paper, two different chaotic dynamic systems are coupled using a semiconductor laser to produce a new chaotic system. These two chaotic systems are Rossler and Chua systems. X-dynamic of Rossler system was coupled optically using optical fiber as a carrier of signal with x, y, and z-dynamics of Chua system. The results were analyzed and the behavior of Chua system was found to be changing in time series which, in turn, changed the attractor. The Chua attractor was converted from double scroll to single scroll. The results obtained from connecting two different systems in chaotic behavior showed a remarkable increase in the bandwidth of Chua system. This increase in bandwidth opens up a wide field for many

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Publication Date
Sat Mar 30 2013
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Studying the Performance of Refrigeration Units Powered by Solar Panel
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An experimental study was conducted to determine the performance of a solar electric refrigeration system. The system contained flat photovoltaic solar panel which absorbs the solar energy and convert it to electrical energy, used to run the refrigeration cycle. Two refrigeration cycles with electrical solar panel were used over a period of 12 months, the first one with classical parts known in refrigeration cycle, while the second one introduced heat exchanger which improves the coefficient of performance by saving the consumed energy. The coefficient of performance of these refrigeration cycles with compressor efficiency 85% are 2.102 and 2.57 respectively. The overall efficiency of the two systems are 18.9% and 23.13%.

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Publication Date
Sat Dec 30 2023
Journal Name
Iraqi Journal Of Science
Agriculture Cadaster Map of Al-Shehimea
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The cadastral map is very important because it has technical and materialist
specification of the property borders and these maps which are land registration
based on it in Iraq, the problem is an ancient maps and unfit for use, despite its
importance, Therefor the updating and digitize the cadastral map is very pivotal, this
is what we have done in the present work.
In the present work, we have an old cadastral map (as a paper) was made in 1932
with modern satellite image (Quick Bird ) 2006, which has 61 cm resolution for the
same area after. Geometric correction technique has been applied by using image-toimage
method or (image registration ) and after that we get new agricultural cadaster
map and connect the

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Publication Date
Tue Jun 26 2018
Journal Name
Oriental Journal Of Chemistry
Excess Molar Quantities of Binary Mixture of Dipropyl amine with Aliphatic Alcohols at 298.15 K
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Refractive indices (nD), viscosities (η) and densities (r) were deliberated for the binary mixtures created by dipropyl amine with 1-octanol, 1-heptanol, 1-hexanol, 1-pentanol and tert-pentyl alcohol at temperature 298.15 K over the perfect installation extent. The function of Redlich-Kister were used to calculate and renovated of the refractive index deviations (∆nD), viscosity deviations (ηE), excess molar Gibbs free energy (∆G*E) and excess molar volumes(Vm E). The standard errors and coefficients were respected by this function. The values of ∆nD, ηE, Vm E and ∆G*E were plotted against mole fraction of dipropyl amine. In all cases the obtained ηE, ∆G*E, Vm E and ∆nD values were negative at 298.15K. Effect of carbon atoms

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Publication Date
Wed Sep 01 2021
Journal Name
International Journal Of Nonlinear Analysis And Application
Suggested methods for prediction using semiparametric regression function
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Ferritin is a key organizer of protected deregulation, particularly below risky hyperferritinemia, by straight immune-suppressive and pro-inflammatory things. , We conclude that there is a significant association between levels of ferritin and the harshness of COVID-19. In this paper we introduce a semi- parametric method for prediction by making a combination between NN and regression models. So, two methodologies are adopted, Neural Network (NN) and regression model in design the model; the data were collected from مستشفى دار التمريض الخاص for period 11/7/2021- 23/7/2021, we have 100 person, With COVID 12 Female & 38 Male out of 50, while 26 Female & 24 Male non COVID out of 50. The input variables of the NN m

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