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Crescent Moon Visibility: A New Criterion using Deep learned Artificial Neural-Network
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     Many authors investigated the problem of the early visibility of the new crescent moon after the conjunction and proposed many criteria addressing this issue in the literature. This article presented a proposed criterion for early crescent moon sighting based on a deep-learned pattern recognizer artificial neural network (ANN) performance. Moon sight datasets were collected from various sources and used to learn the ANN. The new criterion relied on the crescent width and the arc of vision from the edge of the crescent bright limb. The result of that criterion was a control value indicating the moon's visibility condition, which separated the datasets into four regions: invisible, telescope only, probably visible, and certainly visible. This criterion was used on the dataset for ANN learning to compare its efficiency with the actual moon visibility events.

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Publication Date
Tue Jan 01 2013
Journal Name
International Journal Of Contemporary Mathematical Sciences
Equivalent between the Criterion and the Three Open Set's Conditions in Disk-Cyclicity
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A vector in a separable infinite dimensional Hilbert space is called disk-cyclic for bounded operator if the orbit α : 0, α∈;|∝| 1is dense in. The useful tool used to discover codisk-cyclic operation is called the disk-cyclic Criterion. In this paper we will show that some equivalent conditions of the

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Publication Date
Fri Mar 01 2019
Journal Name
Al-khwarizmi Engineering Journal
Improve Akaike’s Information Criterion Estimation Based on Denoising of Quadrature Mirror Filter Bank
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Akaike’s Information Criterion (AIC) is a popular method for estimation the number of sources impinging on an array of sensors, which is a problem of great interest in several applications. The performance of AIC degrades under low Signal-to-Noise Ratio (SNR). This paper is concerned with the development and application of quadrature mirror filters (QMF) for improving the performance of AIC. A new system is proposed to estimate the number of sources by applying AIC to the outputs of filter bank consisting quadrature mirror filters (QMF). The proposed system can estimate the number of sources under low signal-to-noise ratio (SNR).

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Publication Date
Thu Jul 09 2026
Journal Name
Journal Of Physical Education
The Effect of Special Exercises Using with Assisting Aids According to Differentiated Learning (Visual Learners) in Learning Crescent Kick in Fighters of Specialized Taekwondo schools
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Publication Date
Mon Dec 01 2008
Journal Name
Journal Of Economics And Administrative Sciences
Neural Networks as a Discriminant Purposes
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Discriminant between groups is one of the common procedures because of its ability to analyze many practical phenomena, and there are several methods can be used for this purpose, such as linear and quadratic discriminant functions. recently, neural networks is used as a tool to distinguish between groups.

In this paper the simulation is used to compare neural networks and classical method for classify observations to group that is belong to, in case of some variables that don’t follow the normal distribution. we use the proportion of number of misclassification observations to the all observations as a criterion of comparison.  

 

 

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Publication Date
Tue Feb 20 2024
Journal Name
Baghdad Science Journal
A New Strategy to Modify Hopfield by Using XOR Operation
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The Hopfield network is one of the easiest types, and its architecture is such that each neuron in the network connects to the other, thus called a fully connected neural network. In addition, this type is considered auto-associative memory, because the network returns the pattern immediately upon recognition, this network has many limitations, including memory capacity, discrepancy, orthogonally between patterns, weight symmetry, and local minimum. This paper proposes a new strategy for designing Hopfield based on XOR operation; A new strategy is proposed to solve these limitations by suggesting a new algorithm in the Hopfield network design, this strategy will increase the performance of Hopfield by modifying the architecture of t

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Publication Date
Sun Nov 02 2025
Journal Name
African Arguments
From Non‑Intervention to Non‑Indifference: What the African Union Has Really Learned about Crisis Management
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Two decades after replacing the OAU, the AU’s record is best measured not by communiqués but by how fast it converts rules into results on the ground. In March 2022, the African Union’s Peace and Security Council (PSC) authorised the transition from AMISOM to ATMIS in Somalia — a reminder that, two decades after the African Union (AU) replaced the Organisation of African Unity (OAU), the Union’s rules are judged by execution, not intent. This article argues that the AU’s legal and institutional redesign shortened the warning‑to‑decision cycle and raised the credibility of enforcement, but performance still hinges on finance, logistics and political will. Where mandates are matched with money, enabling capabilities and enforc

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Publication Date
Fri Apr 12 2019
Journal Name
Journal Of Economics And Administrative Sciences
Accounting Mining Data Using Neural Networks (Case study)
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Business organizations have faced many challenges in recent times, most important of which is information technology, because it is widely spread and easy to use. Its use has led to an increase in the amount of data that business organizations deal with an unprecedented manner. The amount of data available through the internet is a problem that many parties seek to find solutions for. Why is it available there in this huge amount randomly? Many expectations have revealed that in 2017, there will be devices connected to the internet estimated at three times the population of the Earth, and in 2015 more than one and a half billion gigabytes of data was transferred every minute globally. Thus, the so-called data mining emerged as a

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Publication Date
Thu Jan 29 2026
Journal Name
Journal Of Interdisciplinary Mathematics
Efficient design of neural network based on modified LM training algorithm for solving nonlinear 4th order 3D-PDEs 
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Authors in this work design efficient neural networks, which are based on the modified Levenberg - Marquardt (LM) training algorithms to solve non-linear fourth - order three -dimensional partial differential equations in the two kinds in the periodic and in the non-periodic - Periodic. Software reliability growth models are essential tools for monitoring and evaluating the evolution of software reliability. Software defect detection events that occur during testing and operation are often treated as counting processes in many current models. However, when working with large software systems, the error detection process should be viewed as a random process with a continuous state space, since the number of faults found during testin

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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
Tue Sep 01 2020
Journal Name
Journal Of Engineering
An Adaptive Digital Neural Network-Like-PID Control Law Design for Fuel Cell System Based on FPGA Technique
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This paper proposes an on-line adaptive digital Proportional Integral Derivative (PID) control algorithm based on Field Programmable Gate Array (FPGA) for Proton Exchange Membrane Fuel Cell (PEMFC) Model. This research aims to design and implement Neural Network like a digital PID using FPGA in order to generate the best value of the hydrogen partial pressure action (PH2) to control the stack terminal output voltage of the (PEMFC) model during a variable load current applied. The on-line Particle Swarm Optimization (PSO) algorithm is used for finding and tuning the optimal value of the digital PID-NN controller (kp, ki, and kd) parameters that improve the dynamic behavior of the closed-loop digital control fue

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