The matter of handwritten text recognition is as yet a major challenge to mainstream researchers. A few ways deal with this challenge have been endeavored in the most recent years, for the most part concentrating on the English pre-printed or handwritten characters space. Consequently, the need to effort a research concerning to Arabic texts handwritten recognition. The Arabic handwriting presents unique technical difficulties because it is cursive, right to left in writing and the letters convert its shapes and structures when it is putted at initial, middle, isolation or at the end of words. In this study, the Arabic text recognition is developed and designed to recognize image of Arabic text/characters. The proposed model gets a single l

This study focuses on how tax administrations in Iraq use Artificial Intelligence (AI) techniques to monitor tax evasion for individuals and companies to achieve Tax Compliance (TC). AI was measured through four dimensions: Advanced Data Analytics Techniques (ADAT), Explainable AI (EAI), Machine learning (ML), and Robotic Process Automation (RPA). At the same time, TC was measured through registration, accounting, and tax payment stages. We relied on the questionnaire form to measure the variables. A sample of employees in the General Tax Authority in Iraq was selected, and a questionnaire was distributed to 132 people. The results indicated that the dimensions of AI affect achieving TC at all stages. This study provides evidence of using A

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Machine learning models have recently provided great promise in diagnosis of several ophthalmic disorders, including keratoconus (KCN). Keratoconus, a noninflammatory ectatic corneal disorder characterized by progressive cornea thinning, is challenging to detect as signs may be subtle. Several machine learning models have been proposed to detect KCN, however most of the models are supervised and thus require large well-annotated data. This paper proposes a new unsupervised model to detect KCN, based on adapted flower pollination algorithm (FPA) and the k-means algorithm. We will evaluate the proposed models using corneal data collected from 5430 eyes at different stages of KCN severity (1520 healthy, 331 KCN1, 1319 KCN2, 1699 KCN3 a

The Artificial Neural Network methodology is a very important & new subjects that build's the models for Analyzing, Data Evaluation, Forecasting & Controlling without depending on an old model or classic statistic method that describe the behavior of statistic phenomenon, the methodology works by simulating the data to reach a robust optimum model that represent the statistic phenomenon & we can use the model in any time & states, we used the Box-Jenkins (ARMAX) approach for comparing, in this paper depends on the received power to build a robust model for forecasting, analyzing & controlling in the sod power, the received power come from

        Artificial Neural networks (ANN) are powerful and effective tools in time-series applications. The first aim of this paper is to diagnose better and more efficient ANN models (Back Propagation, Radial Basis Function Neural networks (RBF), and Recurrent neural networks) in solving the linear and nonlinear time-series behavior. The second aim is dealing with finding accurate estimators as the convergence sometimes is stack in the local minima. It is one of the problems that can bias the test of the robustness of the ANN in time series forecasting. To determine the best or the optimal ANN models, forecast Skill (SS) employed to measure the efficiency of the performance of ANN models. The mean square error and

The most common cause of death is cardiovascular disease (CVD), with ischemic heart disease being the most notable type.  There is a propensity to raise the sensitivity of methods in contemporary laboratory for diagnosing of CVD, and assessing key as CVD bio-indicators. The urgent task is to seek for different indicators as a hopeful tool for early detection and monitoring of myocardial infarction in blood samples only. This study comprised 117 Volunteers, recorded with both genders in the age range of 32-64 years old. The volunteers were categorized into two groups: 67patients of myocardial infarction, other group embraced 50 healthy individuals. The samples of blood were collected and directed for biochemical analysis to evaluate estr

The aim of this research is to know danger of radioactive isotopes
that are found in samples of drugs traded in Iraqi markets. The
samples are Iraqi Amoxicillin, English Amoxicillin, UAE
Amoxicillin, Indian Amoxicillin, Iraqi Paracetamol, English
Paracetamol, UAE Paracetamol and Indian Paracetamol. By high
purity germanium the activity of the following isotopes 40K, 214Pb,
228Ac and 137Cs is measured and the specific activity was used to
calculate the annual effective dose. Then the calculated annual
effective dose values are compared with the allowable annual
effective dose values of each part of digestive channel. This research
concluded that the measured annual effective dose values are not
dangerous.<

Abstract Background Infections with helminths are associated with deficient sanitary facilities, unsafe human waste disposal, inadequate and lack of safe drinking water, and low socioeconomic status. Objective To determine the prevalence of some gastrointestinal helminths among Iraqi people, and association of infections with age, sex, and region in Iraq. Method This study is retrospective, including reported cases of infections using an available surveillance database from January 2013 to December 2013 of all provinces of Iraq by the Ministry of Health. Result The overall prevalence of gastrointestinal helminthiasis was 1.39%. Enterobius vermicularis found to be the predominant helminth parasite. No significant (P &lt; 0.05) rel