Deep learning convolution neural network has been widely used to recognize or classify voice. Various techniques have been used together with convolution neural network to prepare voice data before the training process in developing the classification model. However, not all model can produce good classification accuracy as there are many types of voice or speech. Classification of Arabic alphabet pronunciation is a one of the types of voice and accurate pronunciation is required in the learning of the Qur’an reading. Thus, the technique to process the pronunciation and training of the processed data requires specific approach. To overcome this issue, a method based on padding and deep learning convolution neural network is proposed to

The present work is to investigate the feasibility of removal vanadium (V) and nickel (Ni) from Iraqi heavy gas oil using activated bentonite. Different operating parameters such as the degree of bentonite activation, activated bentonite loading, and operating time was investigated on the effect of heavy metal removal efficiency. Experimental results of adsorption test show that Langmuir isotherm predicts well the experimental data and the maximum bentonite uptake of vanadium was 30 mg/g. The bentonite activated with 50 wt% H₂SO₄ shows a (75%) removal for both Ni and V. Results indicated that within approximately 5 hrs, the vanadium removal efficiencies were 33, 45, and 60% at vanadium loadings of 1

The paper present design of a control structure that enables integration of a Kinematic neural controller for trajectory tracking of a nonholonomic differential two wheeled mobile robot, then  proposes a Kinematic neural controller to direct a National Instrument mobile robot (NI Mobile Robot). The controller is to make the actual velocity of the wheeled mobile robot close the required velocity by guarantees that the trajectory tracking mean squire error converges at minimum tracking error. The proposed tracking control system consists of two layers; The first layer is a multi-layer perceptron neural network system that controls the mobile robot to track the required path , The second layer is an optimization layer ,which is impleme

This work was conducted to study the oxidation of phenol in aqueous solution using copper based catalyst with zinc as promoter and different carrier, i.e. γ-Alumina and silica. These catalysts were prepared by impregnation method.
The effect of catalyst composition, pH (5.6-9), phenol to catalyst concentration ratio (2-0.5), air feed rate (30-50) ml/s, stirring speed (400-800) rpm, and temperature (80-100) °C were examined in order to find the best conditions for phenol conversion.
The best operating conditions which lead to maximum phenol conversion (73.1%) are : 7.5 pH, 4/6 phenol to catalyst concentration, 40 ml/s air feed rate, 600 rpm stirring speed, and 100 °C reaction temperature. The reaction involved an induction period

Toxic dyes are commonly discharged into waste waters and dyes are extensively used in the textile industry so it is necessary to find out efficient and eco-friendly method for treating waste waters resulting from industrial effluences. To achieve this aim the fungus Trichoderma sp. is employed into two lines: first line was self – immobilized fungal pellets in (Czapek – Dox medium) to adsorbs two dyes crystal violet, congo red by concentrations 0.01, 0.02, 0.03, 0.04, 0.05, 0.06 mg/L to both dyes, PH 2, room temperature with shaker in ( hrs.²,hrs.⁴,hrs.²⁴) , by Uv- Visible spectrum . the removal efficiency of 0.05 mg/L crystal violet by Trichoderma sp was 96%. but there was no remova

6. coli K12 and B. subtilis 168 were investigated for their cadmium and mercury tolerance abilities. They were developed by UV mutagenesis technique to increase their tolerances either to cadmium or mercury, and their names then were designated depend on the name and concentration of metals. E. coli K12 Cd3^R exhibited bioremediation amount of 6.5 mg Cd/g dry biomass cell. At the same time, its wild-type (E. coli K12 Cd3) was able to remove 5.2 mg Cd/g dry biomass cell in treatment of 17 mg Cd /L within 72 hours of incubation at 37 °C (pH=7) in vitro assays. The results show that E.coli K12 Hg 20 was able to remove 0.050 µg Hg/g dry biomass cell

Face Recognition Systems (FRS) are increasingly targeted by morphing attacks, where facial features of multiple individuals are blended into a synthetic image to deceive biometric verification. This paper proposes an enhanced Siamese Neural Network (SNN)-based system for robust morph detection. The methodology involves four stages. First, a dataset of real and morphed images is generated using StyleGAN, producing high-quality facial images. Second, facial regions are extracted using Faster Region-based Convolutional Neural Networks (R-CNN) to isolate relevant features and eliminate background noise. Third, a Local Binary Pattern-Convolutional Neural Network (LBP-CNN) is used to build a baseline FRS and assess its susceptibility to d

It is well known that petroleum refineries are considered the largest generator of oily sludge which may cause serious threats to the environment if disposed of without treatment. Throughout the present research, it can be said that a hybrid process including ultrasonic treatment coupled with froth floatation has been shown as a green efficient treatment of oily sludge waste from the bottom of crude oil tanks in Al-Daura refinery and able to get high yield of base oil recovery which is 65% at the optimum operating conditions (treatment time = 30 min, ultrasonic wave amplitude = 60 micron, and (solvent: oily sludge) ratio = 4). Experimental results showed that 83% of the solvent used was recovered meanwhile the main water