In this paper, a handwritten digit classification system is proposed based on the Discrete Wavelet Transform and Spike Neural Network. The system consists of three stages. The first stage is for preprocessing the data and the second stage is for feature extraction, which is based on Discrete Wavelet Transform (DWT). The third stage is for classification and is based on a Spiking Neural Network (SNN). To evaluate the system, two standard databases are used: the MADBase database and the MNIST database. The proposed system achieved a high classification accuracy rate with 99.1% for the MADBase database and 99.9% for the MNIST database

This work implements an Electroencephalogram (EEG) signal classifier. The implemented method uses Orthogonal Polynomials (OP) to convert the EEG signal samples to moments. A Sparse Filter (SF) reduces the number of converted moments to increase the classification accuracy. A Support Vector Machine (SVM) is used to classify the reduced moments between two classes. The proposed method’s performance is tested and compared with two methods by using two datasets. The datasets are divided into 80% for training and 20% for testing, with 5 -fold used for cross-validation. The results show that this method overcomes the accuracy of other methods. The proposed method’s best accuracy is 95.6% and 99.5%, respectively. Finally, from the results, it

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

Evaluating a reservoir to looking for hydrocarbon bearing zones, by determining the petrophysical properties in two wells of the Yamama Formation in Siba field using Schlumberger Techlog software. Three porosity logs were used to identify lithology using MN and MID cross plots. Shale volume were calculated using gamma ray log in well Sb-6ST1 and corrected gamma ray in well Sb-5B. Sonic log was used to calculate porosity in bad hole intervals while from density log at in-gauge intervals. Moreover, water saturation was computed from the modified Simandoux equation and compared to the Archie equation. Finally, Permeability was estimated using a flow zone indicator. The results show that the Yamama Formation is found to be mainly limest

The Carbonate-clastic succession in this study is represented by the Shuaiba and Nahr Umr Formations deposited during the Albian - Aptian Sequence. The present study includes petrography, microfacies analyses, and studying reservoir characterizations for 5 boreholes within West Qurna oil field in the study area. According to the type of study succession (clastic – Carbonate) there are two types of facies analyses:-Carbonate facies analysis, which showed five major microfacies were recognized in the succession of the Shuaiba Formation, bioclastic mudstones to wackstone, Orbitolina wackestone to packstone, Miliolids wackestone, Peloidal wackestone to packstone and mudstone to wackestone identified as an open shelf toward the deep basin.

A polycrystalline PbxS1-x alloys with various Pb content ( 0.54 and 0.55) has been prepared successfully. The structure and composition of alloys are determined by X-ray diffraction (XRD), atomic absorption spectroscopy (AAS) and X-ray fluorescence (XRF) respectively. The X-ray diffraction results shows that the structure is polycrystalline with cubic structure, and there are strong peaks at the direction (200) and (111), the grain size varies between 20 and 82 nm. From AAS and XRF result, the concentrations of Pb content for these alloys were determined. The results show high accuracy and very close to the theoretical values. A photoconductive detector as a bulk has been fabricated by taking pieces of prepared alloys and polished chemic

This study was carried out to investigate the possibility of chickpea soaked water as a substitute for yeast in dough fermentation and its effects on sensory properties of the laboratory loaf bread. Chickpea was soaked for 24,48 and 72 hours at room temperature and used in proportion with or without yeast in dough fermentation . The results revealed that , as the percentage of soaked chickpea water substitution increased, the volume of the produced loaf bread decreased as compared with the control treatment (only yeast ).Best results were obtained by using soaked chickpea water for 24 hours in proportion of 1:1 soaked chickpea water : yeast regarding the sensory properties ,volume and leavening of the loaf bread.
Keywords: chickpea so

The effect of thermal treatment on optical constants of pure PMMA and with addition (15 and 35) ml of coumarin at different temperatures (100, 110 and 120) C⁰ for 1 hour were investigated. Cast method used to prepares films of pure PMMA and PMMA with (15 and 35) of coumarin. UV/VIS spectrometer technique used to measure the absorption spectra for these films. The optical constant (absorption spectra and absorption coefficient) don’t changes after applied temperatures in pure PMMA film but the optical constant (absorption spectra and absorption coefficient) in PMMA with (15 and 35) ml of coumarin increased with applied temperatures. The optical energy gap of pure PMMA and PMMA with (15 and 35) ml of coumarin sl