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Performance Improvement of Neural Network Based RLS Channel Estimators in MIMO-OFDM Systems
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The objective of this study was tointroduce a recursive least squares (RLS) parameter estimatorenhanced by using a neural network (NN) to facilitate the computing of a bit error rate (BER) (error reduction) during channels estimation of a multiple input-multiple output orthogonal frequency division multiplexing (MIMO-OFDM) system over a Rayleigh multipath fading channel.Recursive least square is an efficient approach to neural network training:first, the neural network estimator learns to adapt to the channel variations then it estimates the channel frequency response. Simulation results show that the proposed method has better performance compared to the conventional methods least square (LS) and the original RLS and it is more robust at high speed mobility.

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Publication Date
Tue Dec 07 2021
Journal Name
2021 14th International Conference On Developments In Esystems Engineering (dese)
Content Based Image Retrieval Based on Feature Fusion and Support Vector Machine
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Scopus (10)
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Publication Date
Wed Mar 29 2023
Journal Name
Journal Of Robotics
Real-Time SLAM Mobile Robot and Navigation Based on Cloud-Based Implementation
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This study investigates the feasibility of a mobile robot navigating and discovering its location in unknown environments, followed by the creation of maps of these navigated environments for future use. First, a real mobile robot named TurtleBot3 Burger was used to achieve the simultaneous localization and mapping (SLAM) technique for a complex environment with 12 obstacles of different sizes based on the Rviz library, which is built on the robot operating system (ROS) booted in Linux. It is possible to control the robot and perform this process remotely by using an Amazon Elastic Compute Cloud (Amazon EC2) instance service. Then, the map to the Amazon Simple Storage Service (Amazon S3) cloud was uploaded. This provides a database

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Scopus (21)
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Publication Date
Sun Jun 01 2014
Journal Name
Journal Of Accounting And Financial Studies ( Jafs )
Performance evaluation of vocational and administrative institutions in the media using the bench-marking of new media: analytical study in the Journal of time
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Provides the style of benchmarking the best possible use whenevaluating the performance and evaluation, as well as improved performance,due to its consistency with the principles of good evaluation of theperformance, an extension of the completion of several functions of the timeand cost less, thereby increasing the efficiency of the management of theinstitutions, especially institutions, the media, as it became public the future ofthe message sender to the same time Zaorosaúl new media is challenging thetraditional media of what distinguishes this new interactive media and mass ledto this transition . However, the media Aljdidhoosaúl traditional mediacontinue to coexist and reinforce each Menhmaalakhr, for his wealth offreedom of opin

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Publication Date
Sat Sep 01 2018
Journal Name
Journal Of Accounting And Financial Studies ( Jafs )
Functional rotation and its impact on tax performance: Applied research in the General Authority for Taxation
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The research aimed at finding out the effect of functional rotation on the tax performance of the employees of the General Authority for Taxes through the five-meter questionnaire prepared according to the required data. The data obtained from the research sample were analyzed. The results showed a significant effect of recycling In the tax performance, and this is evident from the proportion of his contribution to the interpretation of the total variation, which is a good indicator of the impact of functional rotation in the performance of the body sample research.

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Publication Date
Fri May 15 2009
Journal Name
Journal Of Planner And Development
Study of Change in Regional Land Use Using Remote Sensing Technology and Geographic Information Systems / Study Area (Hawr Al Huweiza) for the Years 1973-1990-2004
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his project try to explain the using ability of spatial techniques for land cover change detection on regional level with the time parameter and did select for explain these abilities study case (Hewaizah marsh ) . this area apply to many big changes with the time. These changes made action on characters and behaviors of this area as well as all activities in it . This Project concerting to recognize the Using importance of remote sensing and GIS Methodology in data collecting for the changes of land use and the methodology for the analyses and getting the results for the next using as a base data for development and drawing the plans as well as in regional planning .This project focus on practical

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Publication Date
Thu Feb 01 2024
Journal Name
Baghdad Science Journal
Improving the efficiency and security of passport control processes at airports by using the R-CNN object detection model
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The use of real-time machine learning to optimize passport control procedures at airports can greatly improve both the efficiency and security of the processes. To automate and optimize these procedures, AI algorithms such as character recognition, facial recognition, predictive algorithms and automatic data processing can be implemented. The proposed method is to use the R-CNN object detection model to detect passport objects in real-time images collected by passport control cameras. This paper describes the step-by-step process of the proposed approach, which includes pre-processing, training and testing the R-CNN model, integrating it into the passport control system, and evaluating its accuracy and speed for efficient passenger flow

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Scopus (3)
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Publication Date
Thu Sep 01 2016
Journal Name
Journal Of Engineering
Calculating the Transport Density Index from Some of the Productivity Indicators for Railway Lines by Using Neural Networks
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The efficiency evaluation of the railway lines performance is done through a set of indicators and criteria, the most important are transport density, the productivity of enrollee, passenger vehicle production, the productivity of freight wagon, and the productivity of locomotives. This study includes an attempt to calculate the most important of these indicators which transport density index from productivity during the four indicators, using artificial neural network technology. Two neural networks software are used in this study, (Simulnet) and (Neuframe), the results of second program has been adopted. Training results and test to the neural network data used in the study, which are obtained from the international in

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Publication Date
Wed Apr 01 2015
Journal Name
Journal Of Economics And Administrative Sciences
Health Care Quality according to the measurement of the nursing performance efficiency in IBN AL-NAFIS Hospital
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The study aimed to spread the culture of efficient performance between nursing staffs, which would contribute and achieve health care quality, and to clarify the role of nursing in improving the quality of high-quality health care, as well as to clarify how to reach national standards for the quality of health care in Iraq, Therefore, the study dealt with the efficiency of nursing performance as an explanatory variable, and the quality of health care as a dependent variable. The fact that the health sector is the foundation for building a healthy society free from diseases, so hospital of IBN AL-NAFIS as an institution and it's nursing teams were taken as a community for this study. The results to be objective and reflect the rea

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Publication Date
Fri Jun 05 2026
Journal Name
Iraq Journal Of Market Research And Consumer Protection
Study of the qualitative characteristics of oils derived from fish waste running Asian Silurus triostequs and use of organic solvents in certain food systems
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Publication Date
Sun Feb 25 2024
Journal Name
Baghdad Science Journal
Exploring Important Factors in Predicting Heart Disease Based on Ensemble- Extra Feature Selection Approach
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Heart disease is a significant and impactful health condition that ranks as the leading cause of death in many countries. In order to aid physicians in diagnosing cardiovascular diseases, clinical datasets are available for reference. However, with the rise of big data and medical datasets, it has become increasingly challenging for medical practitioners to accurately predict heart disease due to the abundance of unrelated and redundant features that hinder computational complexity and accuracy. As such, this study aims to identify the most discriminative features within high-dimensional datasets while minimizing complexity and improving accuracy through an Extra Tree feature selection based technique. The work study assesses the efficac

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Scopus (7)
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