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

In this golden age of rapid development surgeons realized that AI could contribute to healthcare in all aspects, especially in surgery. The aim of the study will incorporate the use of Convolutional Neural Network and Constrained Local Models (CNN-CLM) which can make improvement for the assessment of Laparoscopic Cholecystectomy (LC) surgery not only bring opportunities for surgery but also bring challenges on the way forward by using the edge cutting technology. The problem with the current method of surgery is the lack of safety and specific complications and problems associated with safety in each laparoscopic cholecystectomy procedure. When CLM is utilize into CNN models, it is effective at predicting time series tasks like iden

The composites were manufactured and study the effect of addition of filler (nanoparticles SiO2 treated with silane) at different weight ratios (1, 2, 3, 4 and 5) %, on electrical, mechanical and thermal properties. Materials were mixed with each other using an ultrasound, and then pour the mixture into the molds to suit all measurements. The electrical characteristics were studied within a range of frequencies (50-1M) Hz at room temperature, where the best results were shown at the fill ratio (1%), and thermal properties at (X=3 %), the mechanical properties at the filler ratio (2%).

In the present work the Buildup factor for gamma rays were studied in shields from epoxy reinforced by lead powder and by aluminum powder, for NaI(Tl) scintillation detector size ( ×? ), using two radioactive sources (Co-60 and Cs-137). The shields which are used (epoxy reinforced by lead powder with concentration (10-60)% and epoxy reinforced by aluminum powder with concentration (10-50)% by thick (6mm) and epoxy reinforced by lead powder with concentration (50%) with thick (2,4,6,8,10)mm. The experimental results show that: The linear absorption factor and Buildup factor increase with increase the concentration for the powders which used in reinforcement and high for aluminum powder than the lead powder and decrease with inc

In this study, Laser Shock Peening (LSP) effect on the polymeric composite materials has been investigated experimentally. Polymeric composite materials are widely used because they are easy to fabricate and have many attractive features. Unsaturated polyester resin as a matrix was selected and Aluminum powder with micro particles as a reinforcement material was used with different volume fraction (2.5%, 5% and 7.5%). Hand lay-up process was used for preparation the composites. Fatigue test with constant amplitude with stress ratio (R =-1) was carried out before and after LSP process with two levels of energy (1Joule and 2Joule). The result showed an increase in the endurance strength of 25.448% at 7.5% volume fraction when peened is 1J

Background: The microhardness of a composite resin is a vital parameter that is used to determine its clinical behavior. Measuring the microhardness of a composite resin has been used as an indirect method to assess its degree of conversion and extent of polymerization. The purpose of this in vitro study was to evaluate the effect of three curing distances (0, 2, and 4 mm) on the microhardness of the top and bottom surfaces of three types of flowable bulk-fill composite resins (smart dentin replacement, Opus bulk fill flow, and Tetric N). Material and method: Sixty-three specimens from the three types of composite resins (n=21) were fabricated using Teflon mold with a 4mm depth and a 5 mm internal diameter and cured for 20 seconds. For e

The aim of this study is to understand the effect of addition carbon types on aluminum electrical conductivity which used three fillers of carbon reinforced aluminum at different weight fractions. The experimental results showed that electrical conductivity of aluminum was decreased by the addition all carbon types, also at low weight fraction of carbon black; it reached (4.53S/cm), whereas it was appeared highly increasing for each carbon fiber and synthetic graphite. At (45%) weight fraction the electrical conductivity was decreased to (4.36Scm) and (4.27Scm) for each carbon fiber and synthetic graphite, respectively. While it was reached to maximum value with carbon black. Hybrid composites were investigated also; the results exhibit tha

A particulate polymer composite material was prepared by reinforcing with the Aluminum Oxide (Al₂O₃) or Aluminum (Al) metallic particles with a particle size of (30) µm to an unsaturated Polyester Resin with a weight fraction of (5%, 10%, 15%, 20%).

Tensile test results showed the maximum value of elastic modulus reached (2400MPa.)  in the case of reinforcing with (Al) particles with weight fraction (20%) and (1500 MPa.)  in the case of reinforcing with (Al₂O₃) particles of the same weight fraction.

  When the impact and the flexural strength tests were done, the results showed that flexural strength (F.S), maximum shear stress (τ_max), impact strength

In this study, a traumatic spinal cord injury (TSCI) classification system is proposed using a convolutional neural network (CNN) technique with automatically learned features from electromyography (EMG) signals for a non-human primate (NHP) model. A comparison between the proposed classification system and a classical classification method (k-nearest neighbors, kNN) is also presented. Developing such an NHP model with a suitable assessment tool (i.e., classifier) is a crucial step in detecting the effect of TSCI using EMG, which is expected to be essential in the evaluation of the efficacy of new TSCI treatments. Intramuscular EMG data were collected from an agonist/antagonist tail muscle pair for the pre- and post-spinal cord lesi