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bsj-6909
Topological Indices Polynomials of Domination David Derived Networks
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The chemical properties of chemical compounds and their molecular structures are intimately connected. Topological indices are numerical values associated with chemical molecular graphs that help in understanding the physicochemical properties, chemical reactivity and biological activity of a chemical compound. This study obtains some topological properties of second and third dominating David derived (DDD) networks and computes several K Banhatti polynomial of second and third type of DDD.

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Publication Date
Mon Oct 01 2018
Journal Name
Iraqi Journal Of Physics
Classification of brain tumors using the multilayer perceptron artificial neural network
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Information from 54 Magnetic Resonance Imaging (MRI) brain tumor images (27 benign and 27 malignant) were collected and subjected to multilayer perceptron artificial neural network available on the well know software of IBM SPSS 17 (Statistical Package for the Social Sciences). After many attempts, automatic architecture was decided to be adopted in this research work. Thirteen shape and statistical characteristics of images were considered. The neural network revealed an 89.1 % of correct classification for the training sample and 100 % of correct classification for the test sample. The normalized importance of the considered characteristics showed that kurtosis accounted for 100 % which means that this variable has a substantial effect

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Publication Date
Mon Dec 05 2022
Journal Name
Baghdad Science Journal
Normal Self-injective Hyperrings
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       In this paper normal self-injective hyperrings are introduced and studied. Some new relations between this concept and essential hyperideal, dense hyperideal, and divisible hyperring are studied. 

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Publication Date
Sun Sep 06 2015
Journal Name
Baghdad Science Journal
Calculation of Radial Electron-Electron Distribution function and Expectation Values for Li-Atom in Excited States 1s 2p, 1s 3p and 1s 3d
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The electron correlation for inter-shells (1s 2p), (1s 3p) and (1s 3d) was described by the inter-particle radial distribution function f(r12). It was evaluated for Li-atom in the different excited states (1s2 2p), (1s2 3p) and (1s2 3d) using Hartree-Fock approximation (HF). The inter particle expectation values for these shells were also evaluated. The calculations were performed using Mathcad 14 program.

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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
Mon Sep 23 2024
Journal Name
American Journal Of Social And Humanitarian Research
Voices of Feminism in Monica Ali’s Brick Lane
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This paper explores the feminist voices in Monica Ali’s novel Brick Lane, focusing on the character development of Nazneen as she evolves from a compliant, dependent wife into a self-reliant, empowered individual. The analysis highlights how Nazneen’s journey toward financial independence through her sewing work plays a critical role in her personal transformation. The paper also examines the impact of female support networks on her empowerment, alongside the cultural obstacles she encounters as an immigrant woman living in London. Using feminist theory, this study discusses the complex interplay between culture, gender, and identity, emphasizing the multifaceted nature of women’s empowerment in a diverse cultural setting. Brick Lane

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Publication Date
Sun Jul 09 2023
Journal Name
Journal Of Engineering
PAPR Reduction of OFDM Signals Using Clipping and Coding
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The problem of the high peak to average ratio (PAPR) in OFDM signals is investigated with a brief presentation of the various methods used to reduce the PAPR with special attention to the clipping method. An alternative approach of clipping is presented, where the clipping is performed right after the IFFT stage unlike the conventional clipping that is performed in the power amplifier stage, which causes undesirable out of signal band spectral growth. In the proposed method, there is clipping of samples not clipping of wave, therefore, the spectral distortion is avoided. Coding is required to correct the errors introduced by the clipping and the overall system is tested for two types of modulations, the QPSK as a constant amplitude modul

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Publication Date
Sat Jan 01 2011
Journal Name
Iraqi Journal Of Physics
Single Particle Level Density in a Harmonic – Oscillator Potential Well
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The purpose of this paper is to study the properties of the
partial level density ( ) l g and the total level density g ( ),
numerically obtained as a l sum of ( ) l g up to 34 max l  , for
a Harmonic – Oscillator potential well. This method applied the
quantum – mechanical phase shift technique and concentrated
on the continuum region. Also a discussion of peculiarities of
quantal calculation for single particle level density of energy –
dependent potential

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Publication Date
Thu Aug 07 2025
Journal Name
Journal Of Image And Graphics
Analysis Evolution of Image Caption Techniques: Combining Conventional and Modern Methods for Improvement
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This study explores the challenges in Artificial Intelligence (AI) systems in generating image captions, a task that requires effective integration of computer vision and natural language processing techniques. A comparative analysis between traditional approaches such as retrieval- based methods and linguistic templates) and modern approaches based on deep learning such as encoder-decoder models, attention mechanisms, and transformers). Theoretical results show that modern models perform better for the accuracy and the ability to generate more complex descriptions, while traditional methods outperform speed and simplicity. The paper proposes a hybrid framework that combines the advantages of both approaches, where conventional methods prod

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Publication Date
Thu Sep 13 2018
Journal Name
Baghdad Science Journal
An Efficient Numerical Method for Solving Volterra-Fredholm Integro-Differential Equations of Fractional Order by Using Shifted Jacobi-Spectral Collocation Method
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The aim of this article is to solve the Volterra-Fredholm integro-differential equations of fractional order numerically by using the shifted Jacobi polynomial collocation method. The Jacobi polynomial and collocation method properties are presented. This technique is used to convert the problem into the solution of linear algebraic equations. The fractional derivatives are considered in the Caputo sense. Numerical examples are given to show the accuracy and reliability of the proposed technique.

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Publication Date
Thu May 18 2023
Journal Name
Journal Of Engineering
Spatial Prediction of Monthly Precipitation in Sulaimani Governorate using Artificial Neural Network Models
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ANN modeling is used here to predict missing monthly precipitation data in one station of the eight weather stations network in Sulaimani Governorate. Eight models were developed, one for each station as for prediction. The accuracy of prediction obtain is excellent with correlation coefficients between the predicted and the measured values of monthly precipitation ranged from (90% to 97.2%). The eight ANN models are found after many trials for each station and those with the highest correlation coefficient were selected. All the ANN models are found to have a hyperbolic tangent and identity activation functions for the hidden and output layers respectively, with learning rate of (0.4) and momentum term of (0.9), but with different data

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