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Using artificial neural networks to assign soccer players by physical and motor abilities
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Introduction: The introduction of analytics tools in sports indicates that artificial neural networks can be one of the intelligent approaches to process complex data and identify patterns that help players move according to their most suitable positions. Objective: The purpose of this research is to investigate the possibility of using artificial neural networks to determine the physical and motor abilities of football players and determine their suitable playing positions based on exact quantitative indicators. Method: The study sample consists of 45 youth players aged (15–16) years from the Espanyol Football Academy in Baghdad. The results are analyzed using a multilayer perceptron (MLP) artificial neural network model to identify the relationships between physical variables and playing positions. Results: The Pearson correlation analysis reveals statistically significant relationships between physical and motor abilities and the players’ actual playing positions (p < 0.05). In addition, the artificial neural network (MLP) model demonstrated the ability to assign players to different playing positions based on the relative weights of the variables. Speed, endurance, and explosive power were identified as the most influential factors in determining offensive positions, whereas flexibility and visual–motor coordination played a significant role in determining defensive positions and goalkeeping. The model achieved a classification accuracy exceeding 85%. Discussion: The artificial neural network model demonstrates a high capacity to exploit correlational relationships and transform them from conventional statistical associations into accurate predictive patterns. This enables the model to guide players toward the most suitable playing positions based on their physical and motor characteristics. Conclusions: The findings of the study confirm the feasibility of adopting artificial neural networks as an intelligent tool for sports performance analysis and for guiding youth players toward the playing positions most suited to their physical and motor abilities.

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Publication Date
Wed Nov 28 2018
Journal Name
International Journal Of Engineering &amp; Technology
Crude Oil Effect on the Clayey Soil Mechanical and Physical Properties
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Soil defilement with "raw petroleum" is a standout amongst the most across the board and genuine ecological issues going up against both the industrialized and oil country like Iraq. Along these lines, the impact of "raw petroleum" on soil contamination is one of most critical subjects that review these days. The present examination expects to research "unrefined oil"effectson the mechanical and physical properties of clayey soils. The dirt examples were acquired from Al-Doura area in Baghdad city and arranged by the "Brought together Soil Grouping Framework (USCS)" as silty mud of low pliancy (CL). Research center tests were done on contaminated and unpolluted soil tests with same thickness. The dirtied tests are set up by blending

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Publication Date
Fri Oct 25 2024
Journal Name
Infrastructures
Physical, Mechanical and Durability Properties of Eco-Friendly Engineered Geopolymer Composites
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Engineered geopolymer composite (EGC) is a high-performance material with enhanced mechanical and durability capabilities. Ground granulated blast furnace slag (GGBFS) and silica fume (SF) are common binder materials in producing EGC. However, due to the scarcity and high cost of these materials in some countries, sustainable alternatives are needed. This research focused on producing eco-friendly EGC made of cheaper and more common pozzolanic waste materials that are rich in aluminum and silicon. Rice husk ash (RHA), granite waste powder (GWP), and volcanic pumice powder (VPP) were used as partial substitutions (10–50%) of GGBFS in EGC. The effects of these wastes on workability, unit weight, compressive strength, tensile strengt

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Publication Date
Fri Jan 01 2016
Journal Name
Journal Of Engineering
Enhanced Chain-Cluster Based Mixed Routing Algorithm for Wireless Sensor Networks
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Energy efficiency is a significant aspect in designing robust routing protocols for wireless sensor networks (WSNs). A reliable routing protocol has to be energy efficient and adaptive to the network size. To achieve high energy conservation and data aggregation, there are two major techniques, clusters and chains. In clustering technique, sensor networks are often divided into non-overlapping subsets called clusters. In chain technique, sensor nodes will be connected with the closest two neighbors, starting with the farthest node from the base station till the closest node to the base station. Each technique has its own advantages and disadvantages which motivate some researchers to come up with a hybrid routing algorit

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Publication Date
Tue Dec 01 2020
Journal Name
Baghdad Science Journal
Seasonal variation and correlation between the physical, chemical and microbiological parameters of Nile water in selected area in Egypt (Case study): physical, chemical and microbiological parameters of Nile water
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Fresh water resources in terms of water quality is a crucial issue worldwide. In Egypt, the Nile River is the main source of fresh water in the country and monitoring its water quality is a major task on governments and research levels. In the present case study, the physical, chemical and algal distribution in Nile River was monitored over two seasons (winter and summer) in 2019. The aims of the study were to check the seasonal variation among the different water parameters and also to check the correlations between those parameters. Water samples were collected from the Nile in Cairo governorate in EGYPT. The different physiochemical and microbiological properties in water samples were assessed. The studied parameters were included: te

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Publication Date
Wed Jan 21 2015
Journal Name
Wireless Personal Communications
Survey and Analysis of Integrating PMIPv6 and MIH Mobility Management Approaches for Heterogeneous Wireless Networks
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Publication Date
Sun Dec 31 2017
Journal Name
Al-khwarizmi Engineering Journal
Solving the Inverse Kinematic Equations of Elastic Robot Arm Utilizing Neural Network
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The inverse kinematic equation for a robot is very important to the control robot’s motion and position. The solving of this equation is complex for the rigid robot due to the dependency of this equation on the joint configuration and structure of robot link. In light robot arms, where the flexibility exists, the solving of this problem is more complicated than the rigid link robot because the deformation variables (elongation and bending) are present in the forward kinematic equation. The finding of an inverse kinematic equation needs to obtain the relation between the joint angles and both of the end-effector position and deformations variables. In this work, a neural network has been proposed to solve the problem of inverse kinemati

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Publication Date
Sun Jun 20 2021
Journal Name
Baghdad Science Journal
Arabic Speech Classification Method Based on Padding and Deep Learning Neural Network
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Deep learning convolution neural network has been widely used to recognize or classify voice. Various techniques have been used together with convolution neural network to prepare voice data before the training process in developing the classification model. However, not all model can produce good classification accuracy as there are many types of voice or speech. Classification of Arabic alphabet pronunciation is a one of the types of voice and accurate pronunciation is required in the learning of the Qur’an reading. Thus, the technique to process the pronunciation and training of the processed data requires specific approach. To overcome this issue, a method based on padding and deep learning convolution neural network is proposed to

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Publication Date
Thu Jul 02 2026
Journal Name
Journal Of Engineering
Design and Implementation of Super Elman Neural Network for SDN Robotics System
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Path planning in autonomous robotic systems (ARS) is challenging, especially in dynamic or uncertain environments. Many classical methods are computationally expensive and lack adaptability to real-world scenarios. In order to improve the overall path-planning capabilities of robots; this paper introduces a new smart robotic navigation system which uses Software Defined Network (SDN) and Multi-Spike Elman Neural Network (MS-ENN). The introduced system includes an innovative way to encode temporal information using multiple spikes which can capture much greater amounts of detail about changing environmental characteristics than conventional artificial neural networks. Additionally, it includes a spiking wave-front planner (SWP) to pr

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Publication Date
Wed Jan 01 2020
Journal Name
Ieee Access
Modified Elman Spike Neural Network for Identification and Control of Dynamic System
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Publication Date
Mon Oct 22 2018
Journal Name
Journal Of Economics And Administrative Sciences
Using simulation to compare between parametric and nonparametric transfer function model
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In this paper, The transfer function model in the time series was estimated using different methods, including parametric Represented by the method of the Conditional Likelihood Function, as well as the use of abilities nonparametric are in two methods  local linear regression and cubic smoothing spline method, This research aims to compare those capabilities with the nonlinear transfer function model by using the style of simulation and the study of two models as output variable and one model as input variable in addition t

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