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ON THE GREEDY RADIAL BASIS FUNCTION NEURAL NETWORKS FOR APPROXIMATION MULTIDIMENSIONAL FUNCTIONS
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The aim of this paper is to approximate multidimensional functions by using the type of Feedforward neural networks (FFNNs) which is called Greedy radial basis function neural networks (GRBFNNs). Also, we introduce a modification to the greedy algorithm which is used to train the greedy radial basis function neural networks. An error bound are introduced in Sobolev space. Finally, a comparison was made between the three algorithms (modified greedy algorithm, Backpropagation algorithm and the result is published in [16]).

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Publication Date
Sat May 01 2021
Journal Name
Journal Of Physics: Conference Series
On semi strongly (E, F)-convex functions and semi strongly (E, F)-convex optimization problems
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Abstract<p>In this paper, a new class of non-convex functions called semi strongly (<italic>E, F</italic>)-convex functions are presented. This class represents a natural extension of semi strongly <italic>E</italic>-convex functions shown in the literature. Different properties of this class of functions are discussed. Optimality properties of constrained optimization problems in which the objective function or the inequality constraints functions are semi strongly (<italic>E, F</italic>)-convex are proved for this class.</p>
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Publication Date
Sun Sep 07 2014
Journal Name
Baghdad Science Journal
Effect of Alcoholic Catechin Extract on Hyperglycemia, Hyperlipidemia and Liver Functions in Alloxan Diabetic Mice
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The objective of this study is to estimate the effect of the hydro-ethanolic catechin extract toward blood glucose, lipid profile and liver functions in alloxan diabetic mice. 50 healthy mice (25-30 g) were divided into five groups of ten animals for each. Group A received normal saline as normal control group. To induce diabetes, alloxan (150 mg/kg), intraperitoneal (i.p.) single dose was injected to groups B, C, D and E. Group B represents diabetic control group. Groups C, D and E received ethanolic catechin extract (30 mg/kg and 40 mg/kg) for different periods of 1, 2 and 3 weeks as treated groups. Blood glucose, serum lipids [Total Cholesterol (TC), Triglycerides (TGs) and High Density Lipoproteins (HDL)], asparagine transaminase (AST),

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Publication Date
Mon Oct 30 2023
Journal Name
Iraqi Journal Of Science
On a Subclass of Meromorphic Univalent Functions With Fixed Second Coefficientsassociated with q-Differed Operator
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The main object of this article is to study and introduce a subclass of meromorphic univalent functions with fixed second positive defined by q-differed operator. Coefficient bounds, distortion and Growth theorems, and various are  the obtained results.

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Publication Date
Mon Apr 01 2024
Journal Name
Iop Conference Series: Earth And Environmental Science
Effect of Inhalation Exposure to Nano-Imidacloprid on Liver and Kidney Functions In Male Rats
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Abstract<p>Pesticide poisoning is a serious global public health issue and is responsible for a sizable number of annual fatalities. This study was designed to examine the potentially harmful effects of adult rats being exposed to imidacloprid (IMD) as a nanoparticle by determining the chronic effect of inhalation of (5,10 and 20) mg/kg/b.w. of nano-imidacloprid for a duration of 60 days. The most important biochemical parameters of the serum liver function parameters were aspartate aminotransferase (AST), alanine aminotransferase (ALT), and alkaline phosphatase ALP, kidney function [blood urea, creatinine, and urea], and oxidative stress parameters (MDA, GSH, and CAT) in all treated groups when</p> ... Show More
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Publication Date
Wed Jan 01 2020
Journal Name
Periodicals Of Engineering And Natural Sciences
Bayesian and non-Bayesian estimation of the lomax model based on upper record values under weighted LINEX loss function
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In this article, we developed a new loss function, as the simplification of linear exponential loss function (LINEX) by weighting LINEX function. We derive a scale parameter, reliability and the hazard functions in accordance with upper record values of the Lomax distribution (LD). To study a small sample behavior performance of the proposed loss function using a Monte Carlo simulation, we make a comparison among maximum likelihood estimator, Bayesian estimator by means of LINEX loss function and Bayesian estimator using square error loss (SE) function. The consequences have shown that a modified method is the finest for valuing a scale parameter, reliability and hazard functions.

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Publication Date
Thu Jun 30 2022
Journal Name
Pakistan Journal Of Medical And Health Sciences
The Effect of Music Therapy on Clients' Vital Signs during Pulmonary Function Test: An Experimental Study
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Background: The patient's vital signs must stay stable pre a pulmonary function test since the approach demands the patient to breathe slowly and deeply and exhale quickly. However, events that cause variations in normal vital signs may impact the patient. Many strategies can be utilized to stabilize vital signs, including music treatment. The purpose of this study is to compare the effectiveness of music therapy on the vital signs of client's pre a pulmonary function test. Methods: An experimental design study with an application (pre-test, post-test), to determine the effectiveness of music therapy for clients pre pulmonary function test for 106 clients in Al-Diwaniyah, Al-Shamiya, and Al- Hamza public hospitals In Iraq. Parti

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Publication Date
Thu May 05 2016
Journal Name
Global Journal Of Engineering Science And Researches
EVALUATE THE RATE OF CONTAMINATION SOILS BY COPPER USING NEURAL NETWORK TECHNIQUE
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The aim of this paper is to design suitable neural network (ANN) as an alternative accurate tool to evaluate concentration of Copper in contaminated soils. First, sixteen (4x4) soil samples were harvested from a phytoremediated contaminated site located in Baghdad city in Iraq. Second, a series of measurements were performed on the soil samples. Third, design an ANN and its performance was evaluated using a test data set and then applied to estimate the concentration of Copper. The performance of the ANN technique was compared with the traditional laboratory inspecting using the training and test data sets. The results of this study show that the ANN technique trained on experimental measurements can be successfully applied to the rapid est

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Publication Date
Tue Aug 27 2024
Journal Name
Tem Journal
Preparing the Electrical Signal Data of the Heart by Performing Segmentation Based on the Neural Network U-Net
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Research on the automated extraction of essential data from an electrocardiography (ECG) recording has been a significant topic for a long time. The main focus of digital processing processes is to measure fiducial points that determine the beginning and end of the P, QRS, and T waves based on their waveform properties. The presence of unavoidable noise during ECG data collection and inherent physiological differences among individuals make it challenging to accurately identify these reference points, resulting in suboptimal performance. This is done through several primary stages that rely on the idea of preliminary processing of the ECG electrical signal through a set of steps (preparing raw data and converting them into files tha

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Publication Date
Wed Feb 01 2017
Journal Name
Journal Of Economics And Administrative Sciences
Using Fuzzy Games Theory to Determine the optimal Strategy for The Mobile Phone Networks in The Baghdad And Basra governorates
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      The objective of this research is employ the special cases of  function  trapezoid in the composition of fuzzy sets to make decision within the framework of the theory of games traditional to determine the best strategy for the mobile phone networks in the province of  Baghdad and Basra, has been the adoption of different periods of the  functions belonging to see the change happening in the matrix matches and the impact  that the strategies  and decision-making  available to each player and the impact on  societ

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Publication Date
Mon Feb 01 2016
Journal Name
Swarm And Evolutionary Computation
Improving the performance of evolutionary multi-objective co-clustering models for community detection in complex social networks
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