Estimating an individual's age from a photograph of their face is critical in many applications, including intelligence and defense, border security and human-machine interaction, as well as soft biometric recognition. There has been recent progress in this discipline that focuses on the idea of deep learning. These solutions need the creation and training of deep neural networks for the sole purpose of resolving this issue. In addition, pre-trained deep neural networks are utilized in the research process for the purpose of facial recognition and fine-tuning for accurate outcomes. The purpose of this study was to offer a method for estimating human ages from the frontal view of the face in a manner that is as accurate as possible and takes

In this paper, the memorization capability of a multilayer interpolative neural network is exploited to estimate a mobile position based on three angles of arrival. The neural network is trained with ideal angles-position patterns distributed uniformly throughout the region. This approach is compared with two other analytical methods, the average-position method which relies on finding the average position of the vertices of the uncertainty triangular region and the optimal position method which relies on finding the nearest ideal angles-position pattern to the measured angles. Simulation results based on estimations of the mobile position of particles moving along a nonlinear path show that the interpolative neural network approach outperf

Wellbore instability and sand production onset modeling are very affected by Sonic Shear Wave Time (SSW). In any field, SSW is not available for all wells due to the high cost of measuring. Many authors developed empirical correlations using information from selected worldwide fields for SSW prediction. Recently, researchers have used different Artificial Intelligence methods for estimating SSW. Three existing empirical correlations of Carroll, Freund, and Brocher are used to estimate SSW in this paper, while a fourth new empirical correlation is established. For comparing with the empirical correlation results, another study's Artificial Neural Network (ANN) was used. The same data t

The aim of this work is to study reverse osmosis characteristics for copper sulfate hexahydrate (CuSO4.6H2O), nickel sulfate hexahydrate (NiSO4.6H2O) and zinc sulfate hexahydrate (ZnSO4.6H2O) removal from aqueous solution which discharge from some Iraqi factories such as Alnasser Company for mechanical industries. The mode of operation of reverse osmosis was permeate is removed and the concentrate of metals solution is recycled back to the feed vessel. Spiral-wound membrane is thin film composite membrane (TFC) was used to conduct this study on reverse osmosis. The variables studied are metals concentrations (50 – 150 ppm) and time (15 – 90 min). It was found that increasing the time results in an increase in concentration of metal in p

  Soil is a crucial component of environment. Total soil analysis may give information about possible enrichment of the soil with heavy metals. Heavy metals, potentially contaminate soils, may have been dumped on the ground. The concentrations of soil heavy metals (Cd, As, Pb, Cr, Ni, Zn and Cu) were measured in three zones thought to be deeply contaminated at different depths (5, 25, 50 cm) at Ibn Al-Haitham College. The highest concentration of heavy metals Pb (63.3ppm), Cr (90.7ppm), Ni (124ppm) and Cu (75.7ppm) were found in zone (A) location-1, where the highest concentration of Zn (111.7ppm) was found in zone (C). Cd and As were detected in small amounts in all zones.     PH value, organic matters, carbonat

The aim of the present work, was measuring of uranium concentrations in 25 soil samples from five locations of Al-Kut city. The samples taken from different depths ranged from soil surface to 60cm step 15 cm, for this measurement of uranium concentrations .The most widely used technique SSNTDs was chosen to be the measurement technique. Results showed that the higher concentrations were in Hai Al- Kafaat which recorded 1.49 ± 0.054 ppm . The uranium content in soil samples were less than permissible limit of UNSCEAR(11.7ppm).