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Studying the Effects of Contamination on Soil Properties Using Remote Sensing
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The problem of soil contamination is increased recently due to increasing the industrial wastes such as petroleum hydrocarbon, organic solvents, and heavy metals as well as maximizing the use of agricultural fertilizers. During this period, wide development of data collection methods, using remote sensing techniques in the field of soil and environment applications appear and state the suitable technique for remediation. This study deals with the application of remote sensing techniques in geoenvironmental engineering through a field spectral reflectance measurements at nine spots of naturally hydrocarbons contaminated soil in Al-Daura Refinery Company site which is located to the south west of Baghdad using radiometer device to get standard curves of wavelengths and analyzing the satellite imagery of the site to get the spectral reflectance curves using GIS technique and EARDAS software package which help in producing thematic maps for the spatial distribution and concentration of contaminants. The comparison of results showed a good correlation between the spectral reflectance from field measurements and the spectral reflectance obtained from analyzing the satellite imagery. The study also improves a method to save cost, time, efforts and staff.

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Publication Date
Thu Jun 30 2011
Journal Name
Al-khwarizmi Engineering Journal
Characterization of Delamination Effect on Free Vibration of Composite Laminates Plate Using High Order Shear Deformation Theory
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 A dynamic analysis method has been developed to investigate and characterize embedded delamination on the dynamic response of composite laminated structures. A nonlinear finite element model for geometrically large amplitude free vibration intact plate and delamination plate analysis is presented using higher order shear deformation theory where the nonlinearity was introduced  in the Green-Lagrange sense. The governing equation of the vibrated plate were derived using the Variational approach. The effect of different orthotropicity ratio, boundary condition and delamination size on the non-dimenational fundamental frequency and frequency ratios of plate for different stacking sequences are studied. Finally th

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Publication Date
Sun Sep 26 2021
Journal Name
Modern Sport
Effect of Rehabilitation Program by Using Hypermedia on Treatment Some of Shoulder Tissues Injuries for Badminton Players
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The important of present study is to design rehabilitation program by using hypermedia for some injuries of smooth tissues in shoulder joint.   This joint is most important to help badminton players in achieving their daily and sport tasks due to upper limp movements depend on health and active of this joint. Experimental approach with a manner of equal single station was used in present study and study simple consisted of 6 badminton players from Babylon and Al-Mahaweel clubs who have less sharp tissue smooth injury such (muscles, ligaments, pocket). We used (SPSS) to analyses pre, medal, post-tests data. In conclusion, hypermedia is positive benefit to rehabilitee of injuries of smooth tissues in shoulder joint for badminton pla

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Publication Date
Sun Sep 07 2008
Journal Name
Baghdad Science Journal
The Extent of Homogeneity in the Distribution of Petrophysical Properties that Affecting the Primary and Enhanced Oil Recoveries of Reservoir Rocks in Zubair Formation of South Iraq
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Records of two regionalized variables were processed for each of porosity and permeability of reservoir rocks in Zubair Formation (Zb-109) south Iraq as an indication of the most important reservoir property which is the homogeneity,considering their important results in criterion most needed for primary and enhanced oil reservoirs.The results of dispersion treatment,the statistical incorporeal indications,boxes plots,rhombus style and tangents angles of intersected circles indicated by confidence interval of porosity and permeability data, have shown that the reservoir rocks of Zubair units (LS),(1L) and (DJ) have reservoir properties of high quality,in contrast to that of Zubair units (MS) and (AB)which have reservoir properties of less q

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Publication Date
Fri Mar 31 2017
Journal Name
Al-khwarizmi Engineering Journal
Big-data Management using Map Reduce on Cloud: Case study, EEG Images' Data
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Database is characterized as an arrangement of data that is sorted out and disseminated in a way that allows the client to get to the data being put away in a simple and more helpful way. However, in the era of big-data the traditional methods of data analytics may not be able to manage and process the large amount of data. In order to develop an efficient way of handling big-data, this work studies the use of Map-Reduce technique to handle big-data distributed on the cloud. This approach was evaluated using Hadoop server and applied on EEG Big-data as a case study. The proposed approach showed clear enhancement for managing and processing the EEG Big-data with average of 50% reduction on response time. The obtained results provide EEG r

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Publication Date
Fri Jan 01 2016
Journal Name
Machine Learning And Data Mining In Pattern Recognition
A New Strategy for Case-Based Reasoning Retrieval Using Classification Based on Association
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Publication Date
Wed Jun 01 2022
Journal Name
Baghdad Science Journal
Variable Selection Using aModified Gibbs Sampler Algorithm with Application on Rock Strength Dataset
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Variable selection is an essential and necessary task in the statistical modeling field. Several studies have triedto develop and standardize the process of variable selection, but it isdifficultto do so. The first question a researcher needs to ask himself/herself what are the most significant variables that should be used to describe a given dataset’s response. In thispaper, a new method for variable selection using Gibbs sampler techniqueshas beendeveloped.First, the model is defined, and the posterior distributions for all the parameters are derived.The new variable selection methodis tested usingfour simulation datasets. The new approachiscompared with some existingtechniques: Ordinary Least Squared (OLS), Least Absolute Shrinkage

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Publication Date
Thu Jan 11 2018
Journal Name
Al-khwarizmi Engineering Journal
Control on a 2-D Wing Flutter Using an Adaptive Nonlinear Neural Controller
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An adaptive nonlinear neural controller to reduce the nonlinear flutter in 2-D wing is proposed in the paper. The nonlinearities in the system come from the quasi steady aerodynamic model and torsional spring in pitch direction. Time domain simulations are used to examine the dynamic aero elastic instabilities of the system (e.g. the onset of flutter and limit cycle oscillation, LCO). The structure of the controller consists of two models :the modified Elman neural network (MENN) and the feed forward multi-layer Perceptron (MLP). The MENN model is trained with off-line and on-line stages to guarantee that the outputs of the model accurately represent the plunge and pitch motion of the wing and this neural model acts as the identifier. Th

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Publication Date
Tue Sep 01 2026
Journal Name
Al-khwarizmi Engineering Journal
Conveyor Belt Speed Control Based on Object Classes Using a Convolutional Neural Network
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Conveyor belt systems facilitate the movement of goods and materials, making them an essential part of various fields. Deep learning control methods are therefore indispensable to assure their energy efficiency and optimal motor speed. Here, training is conducted via a convolutional neural network with transfer learning using VGG16 base net. Feature extraction layers are fixed, whilst fully connected layers are replaced. Thirty-three appropriate object classes are considered for classification, and images are acquired using an overhead camera. The obtained weight values are transmitted to Arduino that uses these data to control the stepper motor speed for optimal performance and minimum energy consumption, achieving approximate esti

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Publication Date
Tue Aug 10 2021
Journal Name
Design Engineering
Lossy Image Compression Using Hybrid Deep Learning Autoencoder Based On kmean Clusteri
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Image compression plays an important role in reducing the size and storage of data while increasing the speed of its transmission through the Internet significantly. Image compression is an important research topic for several decades and recently, with the great successes achieved by deep learning in many areas of image processing, especially image compression, and its use is increasing Gradually in the field of image compression. The deep learning neural network has also achieved great success in the field of processing and compressing various images of different sizes. In this paper, we present a structure for image compression based on the use of a Convolutional AutoEncoder (CAE) for deep learning, inspired by the diversity of human eye

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Publication Date
Sat May 04 2024
Journal Name
Anais Da Academia Brasileira De Ciências
Using decapsulated local brine shrimp cysts on feeding common carp larvae in hatcheries
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The current study aims at using non-hatchable artemia eggs of local origin and making use of these eggs by decapsulating and presenting them as food for the larvae of the Cyprinus carpio as a source of animal protein with high nutritional value instead of throwing them away. The results showed that the second parameter (A2) was highly significant at the level (P≤0.05) in the growth rates of the larvae that were fed on decapsulated artemia eggs alone, and it was better than the two control parameters (A1), in which the larvae were fed with feed designated for Cyprinus carpio fish. It also outperformed the third parameter (A3), in which the feed was mixed with artemia eggs with 50% decapsulation, which also outperformed the control paramete

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