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

   A comparison of double informative and non- informative priors assumed for the parameter of Rayleigh distribution is considered. Three different sets of double priors are included, for a single unknown parameter of Rayleigh distribution. We have assumed three double priors: the square root inverted gamma (SRIG) - the natural conjugate family of priors distribution, the square root inverted gamma – the non-informative distribution, and the natural conjugate family of priors - the non-informative distribution as double priors .The data is generating form three cases from Rayleigh distribution for different samples sizes (small, medium, and large). And Bayes estimators for the parameter is derived under a squared erro

The study of surface hardness, wear resistance, adhesion strength, electrochemical corrosion resistance and thermal conductivity of coatings composed from sodium silicate was prepared using graphite micro-size particles and carbon nano particles as fillers respectively of concentration of (1-5%), for the purpose of covering and protecting the oil distillation towers. The results showed that the sodium silicate coating reinforced with carbon nano-powder has higher resistance to stitches, mechanical wear, adhesive and thermal conductivity than graphite/sodium silicate composite especially when the ratio 5% and 1%, the electrochemical corrosion test confirmed that the coating process of stainless steel 304 lead to increasin

The ground charge density distributions (CDD), elastic charge form factors and proton, charge, neutron, and matter root mean square (rms) radii for stable 40Ca and 48Ca have been calculated using single-particle radial wave functions of Woods-Saxon (WS) and harmonic-oscillator (HO) potentials. Different central potential depths are used for each subshell which is adjusted so as to reproduce the experimental single-nucleon binding energies. An excellent agreement between the calculated rms charge radii and experimental data are found for both nuclei using WS and HO potentials. The calculated proton rms radii for 40Ca are found to be in good agreement with experiment data using both WS and HO potentials while the results for 48Ca showed an ov

The aim of the present research is concerned with study the effect of UV radiation on the optical properties at wavelengths 254, 365 nm of pure PC and anthracene doping PC films prepared using the cast method for different doping ratio 10-60 mL. Films of pure PC and anthracene doping PC were aged under UV radiation for periods of up to 360 h. It found that the effect of UV radiation at wavelength 254 nm on the optical properties is great than the effect of UV radiation at wavelength 365 nm. Also, it found that the optical energy gap of pure PC and anthracene doping PC films is stable against radiation.

The nonlinear optical properties response of nematic liquid crystal (6CHBT) and the impact of doping with two kinds of nanoparticles; Fe₃O₄ magnetic nanoparticles and SbSI ferroelectric nanoparticles have been studied using the non-linear dynamic method through z-scan measurement technique. This was achieved utilizing CW He-Ne laser. The pure LC and magnetic LC nanoparticle composite samples had a maximum absorption while the ferroelectric LC nanoparticle composite had a minimum absorption of the incident light. The nonlinear refractive index was positive for the pure LC and the rod-like ferronematic LC composite samples, while it was negative for the ferroelectric LC composite. The studying of the nonlinear optical

This research aims to study the effect of microwave furnace heat on the mechanical properties and fatigue life of aluminum alloy (AA 2024-T3). Four conditions were used inside microwave furnace (specimens subjected to heat as dry for 30 and 60min. and specimens subjected to heat as wet (water) for 30 and 60 min.), and compared all results with original alloy (AA 2024-T3). Tensile, fatigue, hardness and surface roughness tests were used in this investigation. It is found that hardness of dry conditions is higher than wet conditions and it increases with increasing of time duration inside microwave furnace for dry and wet conditions. Also, tensile strength has the same behavior of hardness, but it increases with decreasing

Vitrifications process one of the important methods to immobilize nuclear waste.  In this research nuclear waste (Strontium Oxides) with molecular weight (5%) was immobilized by vitrification methods in two types of borosilicate glass (c-type) which are glass and glass-ceramics. To investigate the physical, chemical and mechanical properties of glass and glass-ceramic after immobilize nuclear waste these samples irradiated by gamma ray radiation. Co-60 was used as gamma a irradiation with dose rate 0.38 kGy/hr for different period of time. It’s found that gamma radiation affected the glass and glass-ceramic properties. From phase analysis by the x-ray diffraction for glass-ceramic samples proved that at doses 343kGy change the cry

A hand gesture recognition system provides a robust and innovative solution to nonverbal communication through human–computer interaction. Deep learning models have excellent potential for usage in recognition applications. To overcome related issues, most previous studies have proposed new model architectures or have fine-tuned pre-trained models. Furthermore, these studies relied on one standard dataset for both training and testing. Thus, the accuracy of these studies is reasonable. Unlike these works, the current study investigates two deep learning models with intermediate layers to recognize static hand gesture images. Both models were tested on different datasets, adjusted to suit the dataset, and then trained under different m

The COVID-19 pandemic has had a huge influence on human lives all around the world. The virus spread quickly and impacted millions of individuals, resulting in a large number of hospitalizations and fatalities. The pandemic has also impacted economics, education, and social connections, among other aspects of life. Coronavirus-generated Computed Tomography (CT) scans have Regions of Interest (ROIs). The use of a modified U-Net model structure to categorize the region of interest at the pixel level is a promising strategy that may increase the accuracy of detecting COVID-19-associated anomalies in CT images. The suggested method seeks to detect and isolate ROIs in CT scans that show the existence of ground-glass opacity, which is fre