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On Training Of Feed Forward Neural Networks
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In this paper we describe several different training algorithms for feed forward neural networks(FFNN). In all of these algorithms we use the gradient of the performance function, energy function, to determine how to adjust the weights such that the performance function is minimized, where the back propagation algorithm has been used to increase the speed of training. The above algorithms have a variety of different computation and thus different type of form of search direction and storage requirements, however non of the above algorithms has a global properties which suited to all problems.

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Publication Date
Fri Jan 01 2021
Journal Name
Journal Of Accounting And Financial Studies ( Jafs )
The role of training in promote banking compliance monitor procedures: An applied research in a sample of Iraqi banks
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 Training has occupied a leading position in a large number of developed and developing countries alike in order to develop the skills of workers in line with the changes and developments of the era, including monitoring compliance in banks, which is one of the most important jobs in banking work to trailing and monitor the bank’s compliance with laws, regulations and instructions in order to achieve its goals Therefore, the problem of this research focuses on the following question: What is the role of training in enhancing banking compliance at the present time? In order to clarify the relationship between the main and sub-research variables, two main hypotheses and three sub-hypotheses were formulated for each hypothesis, and t

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Publication Date
Wed Mar 10 2021
Journal Name
Baghdad Science Journal
Smart Flow Steering Agent for End-to-End Delay Improvement in Software-Defined Networks
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To ensure fault tolerance and distributed management, distributed protocols are employed as one of the major architectural concepts underlying the Internet. However, inefficiency, instability and fragility could be potentially overcome with the help of the novel networking architecture called software-defined networking (SDN). The main property of this architecture is the separation of the control and data planes. To reduce congestion and thus improve latency and throughput, there must be homogeneous distribution of the traffic load over the different network paths. This paper presents a smart flow steering agent (SFSA) for data flow routing based on current network conditions. To enhance throughput and minimize latency, the SFSA distrib

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Publication Date
Mon Dec 14 2020
Journal Name
Baghdad Science Journal
Smart Flow Steering Agent for End-to-End Delay Improvement in Software-Defined Networks
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Publication Date
Thu Oct 31 2019
Journal Name
Journal Of Theoretical And Applied Information Technology
AN ENHANCED EVOLUTIONARY ALGORITHM WITH LOCAL HEURISTIC APPROACH FOR DETECTING COMMUNITY IN COMPLEX NETWORKS
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Publication Date
Sat Feb 10 2024
Journal Name
Mustansiriyah Journal Of Sports Science
Training between reality and aspiration raising from the point of view of second lieutenant and supervisor of physical education lesson
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First: the definition searchEnsure the definition Introduction of research and its importance, as it was addressed to the importance of the importance of training in the development of people and institutions and the importance of diversifying the training vocabulary and a lot of relevant courses for its role to develop work and get away from the routine work, and was put up for the importance of research to identify the problems and obstacles facing the process of providing training as the best in order to access to the ambition sought by workers on training and even the trainees while defined research problem that was observed weak performance and cost-effective training through the study of some of the questionnaires distributed

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Publication Date
Thu Oct 01 2020
Journal Name
Journal Of Engineering Science And Technology (jestec)
Water Quality Assessment and Sodium Adsorption Ratio Prediction of Tigris River Using Artificial Neural Network
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Sodium adsorption ratio (SAR) is considered as a measure of the water suitability for irrigation usage. This study examines the effect of the physicochemical parameters on water quality and SAR, which included Calcium(Ca+2), Magnesium(Mg+2), Sodium (Na+), Potassium (K), Chloride (Cl-), Sulfate(SO4-2), Carbonate (CO3-2), Bicarbonate (HCO3-), Nitrate (NO3-), Total Hardness (TH), Total Dissolved Salts (TDS), Electrical Conductivity (EC), degree of reaction (DR), Boron (B) and the monthly and annually flow discharge (Q). The water samples were collected from three stations across the Tigris River in Iraq, which flows through Samarra city (upstream), Baghdad city (central) and the end of Kut city (downstream) for the periods of 2016-201

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Publication Date
Mon May 01 2023
Journal Name
Ain Shams Engineering Journal
Neural network modeling of rutting performance for sustainable asphalt mixtures modified by industrial waste alumina
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Publication Date
Tue May 01 2018
Journal Name
Journal Of Engineering
Prediction of Municipal Solid Waste Generation Models Using Artificial Neural Network in Baghdad city, Iraq
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The importance of Baghdad city as the capital of Iraq and the center of the attention of delegations because of its long history is essential to preserve its environment. This is achieved through the integrated management of municipal solid waste since this is only possible by knowing the quantities produced by the population on a daily basis. This study focused to predicate the amount of municipal solid waste generated in Karkh and Rusafa separately, in addition to the quantity produced in Baghdad, using IBM SPSS 23 software. Results that showed the average generation rates of domestic solid waste in Rusafa side was higher than that of Al-Karkh side because Rusafa side has higher population density than Al-Karkh side. T

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Publication Date
Thu Oct 01 2020
Journal Name
Journal Of Engineering Science And Technology
Water quality assessment and sodium adsorption ratio prediction of Tigris River using artificial neural network
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Publication Date
Thu Oct 13 2022
Journal Name
Computation
A Pattern-Recognizer Artificial Neural Network for the Prediction of New Crescent Visibility in Iraq
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Various theories have been proposed since in last century to predict the first sighting of a new crescent moon. None of them uses the concept of machine and deep learning to process, interpret and simulate patterns hidden in databases. Many of these theories use interpolation and extrapolation techniques to identify sighting regions through such data. In this study, a pattern recognizer artificial neural network was trained to distinguish between visibility regions. Essential parameters of crescent moon sighting were collected from moon sight datasets and used to build an intelligent system of pattern recognition to predict the crescent sight conditions. The proposed ANN learned the datasets with an accuracy of more than 72% in comp

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