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In this paper, we derive and prove the stability bounds of the momentum coefficient µ and the learning rate ? of the back propagation updating rule in Artificial Neural Networks .The theoretical upper bound of learning rate ? is derived and its practical approximation is obtained

Artificial Neural Networks (ANN) is one of the important statistical methods that are widely used in a range of applications in various fields, which simulates the work of the human brain in terms of receiving a signal, processing data in a human cell and sending to the next cell. It is a system consisting of a number of modules (layers) linked together (input, hidden, output). A comparison was made between three types of neural networks (Feed Forward Neural Network (FFNN), Back propagation network (BPL), Recurrent Neural Network (RNN). he study found that the lowest false prediction rate was for the recurrentt network architecture and using the Data on graduate students at the College of Administration and Economics, Univer

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This paper represents a study of the effect of the soil type, the drilling parameters and the drilling tool properties on the dynamic vibrational behavior of the drilling rig and its assessment in the drilling system. So first, an experimental drilling rig was designed and constructed to embrace the numerical work.

The experimental work included implementation of the drill-string in different types of soil with different properties according to the difference in the grains size, at different rotational speeds (RPM), and different weights on bit (WOB) (Thrust force), in a way that allows establishing the charts that correlate the vibration acceleration, the rate of penetration (ROP), and the power

Titanium dioxide (TiO2) nanotubes have gained particular interest as a material for gas sensors because of their vertical arrays, prepared by the anodization procedure. The presence of several oxygen vacancies in these nanotubes facilitates gas diffusion and provides additional active sites. This study examined the impact of voltages on the process of depositing iron nanoparticles onto arrays of TiO2 nanotubes (TNTs) for use as a gas sensor. The TNTs are manufactured using a straightforward and economical electrochemical anodization technique, specifically for gas sensor applications. By varying the deposition voltage (2-6 volts), ordered Fe-TNTs were efficiently manufactured using a simple two-step electrochemical process. It utili