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Multi-Layer Feedforward Neural Network Modelling of a Kinematics Solution of A 3-DoF Manipulator Robot
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Modeling forward kinematics with neural networks allows for efficient handling of nonlinear relationships and realistic error correction in time-critical applications by relying on accurate training data. This paper presents a Multi-Layer Feed-Forward Neural Network (MLFFNN) to solve the forward kinematics of a 3-DOF robot. The proposed MLFFNN consists of 50 hidden neurons and was trained using 628319 samples to find only the position (x, y, z) of the end-effector. Data were generated by MATLAB, assuming an incremental motion of joints. The joint variables ( , , and ) are the inputs of the NN, which outputs the positions of the end effector (x, y, z) calculated using the Denavit-Hartenberg (DH) method. The results demonstrate that the proposed MLFFNN has high performance and is efficient for solving the forward kinematics, with a Mean Squared Error (MSE) between the desired and estimated position of 4.3881×10-11. This performance clearly demonstrates that, despite the large size of the dataset, it can be effectively mastered with only a small number of neurons. The simplicity of the network allows it to learn a compact and efficient representation of the data. This improves the reliability of using the proposed network for similar applications in other robotic systems.

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Publication Date
Wed May 10 2023
Journal Name
Journal Of Engineering
ANALYTICAL SOLUTION OF UNSATURATED SOIL WATER FLOW FROM A POINT SOURCE
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Water flow into unsaturated porous media is governed by the Richards’ partial differential equation expressing the mass conservation and Darcy’s laws. The Richards’ equation may be written in three forms,where the dependent variable is pressure head or moisture content, and the constitutive relationships between water content and pressure head allow for conversion of one form into the other. In the present paper, the “moisture-based" form of Richards’ equation is linearized by applying Kirchhoff’s transformation, which
combines the soil water diffusivity and soil water content. Then the similarity method is used to obtain the analytical solution of wetting front position. This exact solution is obtained by means of Lie’s

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Publication Date
Tue Jan 20 2026
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Effects of a Chelated Multi-Micronutrient Nanofertilizer on the Liver ofAlbino Rats: A Biochemical and Histopathological Assessment
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Nanofertilizers offer a promising solution to increase crop yields amid increasing population pressure. Yet  there have been safety concerns about their use, particularly in challenging conditions. This work was designed to assess the toxicological effects of a chelated multi-micronutrient nanofertilizer on albino rats (Rattus norvegicus) as non-target organisms, focusing on the liver, which has important metabolic functions. Thirty-five animals were weighed and randomly divided into 5 groups (n=7). A negative control group was included, and four experimental groups (C1-C4) were given the test item. The dosing regimen for the experimental groups was 15 oral doses of nanofertilizer every other day for 29 days. All the groups were

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Publication Date
Sun Jul 09 2023
Journal Name
Journal Of Engineering
Development a Proposed System of Organization Structure to Management Multi Construction Projects
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The purpose of this study is aimed to lay down an arranged platform suited to Iraqi constructional associations which in charge to carry out multi constructional projects, as it fulfilled management requirements and supervising, so that low - cost projects will be controlled in due term and quality. Based on primary info and observed data collected, the study thesis has been formulated in this way: Iraqi constructional sector bodies which are in charge to implement simultaneously multi constructional projects in need to reformulate its organized structure so that it will be more fitted to management and control of these projects. This thesis includes a
theoretical part contained presenting the most important resources locally and int

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Publication Date
Fri Oct 20 2023
Journal Name
Al-ameed Journal For Medical Research And Health Sciences
Perceptions of Nursing Students about COVID-19 Transmission: A Multi University Study
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Publication Date
Sun Dec 01 2024
Journal Name
Chilean Journal Of Statistics
A method of multi-dimensional variable selection for additive partial linear models.
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In high-dimensional semiparametric regression, balancing accuracy and interpretability often requires combining dimension reduction with variable selection. This study intro- duces two novel methods for dimension reduction in additive partial linear models: (i) minimum average variance estimation (MAVE) combined with the adaptive least abso- lute shrinkage and selection operator (MAVE-ALASSO) and (ii) MAVE with smoothly clipped absolute deviation (MAVE-SCAD). These methods leverage the flexibility of MAVE for sufficient dimension reduction while incorporating adaptive penalties to en- sure sparse and interpretable models. The performance of both methods is evaluated through simulations using the mean squared error and variable selection cri

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Publication Date
Thu Feb 01 2018
Journal Name
Applied Mathematical Modelling
Identification of a multi-dimensional space-dependent heat source from boundary data
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Publication Date
Sun Jan 27 2019
Journal Name
Civil Engineering Journal
Prediction of Sediment Accumulation Model for Trunk Sewer Using Multiple Linear Regression and Neural Network Techniques
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Sewer sediment deposition is an important aspect as it relates to several operational and environmental problems. It concerns municipalities as it affects the sewer system and contributes to sewer failure which has a catastrophic effect if happened in trunks or interceptors. Sewer rehabilitation is a costly process and complex in terms of choosing the method of rehabilitation and individual sewers to be rehabilitated.  For such a complex process, inspection techniques assist in the decision-making process; though, it may add to the total expenditure of the project as it requires special tools and trained personnel. For developing countries, Inspection could prohibit the rehabilitation proceeds. In this study, the researchers propos

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Publication Date
Mon Apr 09 2018
Journal Name
Al-khwarizmi Engineering Journal
Neural Network Modeling of Cutting Force and Chip Thickness Ratio for Turning Aluminum Alloy 7075-T6
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The turning process has various factors, which affecting machinability and should be investigated. These are surface roughness, tool life, power consumption, cutting temperature, machining force components, tool wear, and chip thickness ratio. These factors made the process nonlinear and complicated. This work aims to build neural network models to correlate the cutting parameters, namely cutting speed, depth of cut and feed rate, to the machining force and chip thickness ratio. The turning process was performed on high strength aluminum alloy 7075-T6. Three radial basis neural networks are constructed for cutting force, passive force, and feed force. In addition, a radial basis network is constructed to model the chip thickness ratio. T

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Publication Date
Mon Oct 13 2025
Journal Name
Mesopotamian Journal Of Cybersecurity
Improvement of the Face Recognition Systems Security Against Morph Attacks using the Developed Siamese Neural Network
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Face Recognition Systems (FRS) are increasingly targeted by morphing attacks, where facial features of multiple individuals are blended into a synthetic image to deceive biometric verification. This paper proposes an enhanced Siamese Neural Network (SNN)-based system for robust morph detection. The methodology involves four stages. First, a dataset of real and morphed images is generated using StyleGAN, producing high-quality facial images. Second, facial regions are extracted using Faster Region-based Convolutional Neural Networks (R-CNN) to isolate relevant features and eliminate background noise. Third, a Local Binary Pattern-Convolutional Neural Network (LBP-CNN) is used to build a baseline FRS and assess its susceptibility to d

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Publication Date
Fri Sep 30 2016
Journal Name
Al-khwarizmi Engineering Journal
Modeling the removal of Cadmium Ions from Aqueous Solutions onto Olive Pips Using Neural Network Technique
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The uptake of Cd(II) ions from simulated wastewater onto olive pips was modeled using artificial neural network (ANN) which consisted of three layers. Based on 112 batch experiments, the effect of contact time (10-240 min), initial pH (2-6), initial concentration (25-250 mg/l), biosorbent dosage (0.05-2 g/100 ml), agitation speed (0-250 rpm) and temperature (20-60ºC) were studied. The maximum uptake (=92 %) of Cd(II) was achieved at optimum parameters of 60 min, 6, 50 mg/l, 1 g/100 ml, 250 rpm and 25ºC respectively.

Tangent sigmoid and linear transfer functions of ANN for hidden and output layers respectively with 7 neurons were sufficient to present good predictions for cadmium removal efficiency with coefficient of correlatio

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