This paper describes theoretical modeling of electrostatic mirror based on two cylindrical electrodes, A computational investigation has been carried out on the design and properties of the electrostatic  mirror. we suggest a mathematical expression to represent the axial potential of an electrostatic mirror. The  beam path  by using the Bimurzaev technique have been investigated as a mirror trajectory with the aid of Runge – Kutta  method. the electrode shape of mirror two electrode has been determined by using package SIMION computer program . The spherical and chromatic aberrations coefficients of mirror has been computed and normalized in terms of the focal length. The choice of the mirror depends on the op

FG Mohammed, HM Al-Dabbas, Iraqi journal of science, 2018 - Cited by 6

The aim of the present study was to distinguish between healthy children and those with epilepsy by electroencephalography (EEG). Two biomarkers including Hurst exponents (H) and Tsallis entropy (TE) were used to investigate the background activity of EEG of 10 healthy children and 10 with epilepsy. EEG artifacts were removed using Savitzky-Golay (SG) filter. As it hypothesize, there was a significant changes in irregularity and complexity in epileptic EEG in comparison with healthy control subjects using t-test (p< 0.05). The increasing in complexity changes were observed in H and TE results of epileptic subjects make them suggested EEG biomarker associated with epilepsy and a reliable tool for detection and identification of this di

Recently, the phenomenon of the spread of fake news or misinformation in most fields has taken on a wide resonance in societies. Combating this phenomenon and detecting misleading information manually is rather boring, takes a long time, and impractical. It is therefore necessary to rely on the fields of artificial intelligence to solve this problem. As such, this study aims to use deep learning techniques to detect Arabic fake news based on Arabic dataset called the AraNews dataset. This dataset contains news articles covering multiple fields such as politics, economy, culture, sports and others. A Hybrid Deep Neural Network has been proposed to improve accuracy. This network focuses on the properties of both the Text-Convolution Neural

The aim of this research work is to study the effect of stabilizing gypseous soil, which covers vast areas in the middle, west and south parts of Iraq, using liquid asphalt on its strength properties to be used as a base course layer replacing the traditional materials of coarse aggregate and broken stones which are scarce at economical prices and hauling distances. Gypseous soil brought from Al-Ramadi City, west of Iraq, with gypsum content of 66.65%, medium curing cutback asphalt (MC-30), and hydrated lime are used in this study. The conducted tests on untreated and treated gypseous soil with different percentages of medium curing cutback asphalt (MC-30), water, and lime were: unconfined compression strength, and one dimensional confine

A new de-blurring technique was proposed in order to reduced or remove the blur in the images. The proposed filter was designed from the Lagrange interpolation calculation with adjusted by fuzzy rules and supported by wavelet decomposing technique. The proposed Wavelet Lagrange Fuzzy filter gives good results for fully and partially blurring region in images.
 

In this paper, a new approach was suggested to the method of Gauss Seidel through the controlling of equations installation before the beginning of the method in the traditional way. New structure of equations occur after the diagnosis of the variable that causes the fluctuation and the slow extract of the results, then eradicating this variable. This procedure leads to a higher accuracy and less number of steps than the old method. By using the this proposed method, there will be a possibility of solving many of divergent values equations which cannot be solved by the old style.

The recent emergence of sophisticated Large Language Models (LLMs) such as GPT-4, Bard, and Bing has revolutionized the domain of scientific inquiry, particularly in the realm of large pre-trained vision-language models. This pivotal transformation is driving new frontiers in various fields, including image processing and digital media verification. In the heart of this evolution, our research focuses on the rapidly growing area of image authenticity verification, a field gaining immense relevance in the digital era. The study is specifically geared towards addressing the emerging challenge of distinguishing between authentic images and deep fakes – a task that has become critically important in a world increasingly reliant on digital med

In recent years, the world witnessed a rapid growth in attacks on the internet which resulted in deficiencies in networks performances. The growth was in both quantity and versatility of the attacks. To cope with this, new detection techniques are required especially the ones that use Artificial Intelligence techniques such as machine learning based intrusion detection and prevention systems. Many machine learning models are used to deal with intrusion detection and each has its own pros and cons and this is where this paper falls in, performance analysis of different Machine Learning Models for Intrusion Detection Systems based on supervised machine learning algorithms. Using Python Scikit-Learn library KNN, Support Ve