Determining the face of wearing a mask from not wearing a mask from visual data such as video and still, images have been a fascinating research topic in recent decades due to the spread of the Corona pandemic, which has changed the features of the entire world and forced people to wear a mask as a way to prevent the pandemic that has calmed the entire world, and it has played an important role. Intelligent development based on artificial intelligence and computers has a very important role in the issue of safety from the pandemic, as the Topic of face recognition and identifying people who wear the mask or not in the introduction and deep education was the most prominent in this topic. Using deep learning techniques and the YOLO (”You on

     Agriculture improvement is a national economic issue that extremely depends on productivity. The explanation of disease detection in plants plays a significant role in the agriculture field. Accurate prediction of the plant disease can help treat the leaf as early as possible, which controls the economic loss. This paper aims to use the Image processing techniques with Convolutional Neural Network (CNN). It is one of the deep learning techniques to classify and detect plant leaf diseases. A publicly available Plant village dataset was used, which consists of 15 classes, including 12 diseases classes and 3 healthy classes.  The data augmentation techniques have been used. In addition to dropout and weight reg

The research aimed to statement, which impact that the development of Iraqi auditing standards in the fight against corruption and to fulfill the reform requirements by conducting a comparative study analysis with a framework proposal to amend the Iraqi Audit directory number statement (6) issued by the Accounting and Auditing Standards Board of the Republic of Iraq dated 08/24/2002 on audit planning and supervision on the basis of the latest versions of international auditing standards in this regard.

The researchers concluded that there is a need to update the standards (evidence) audit accredited in the Republic of Iraq in accordance with international auditing standards to meet the requirements of the report of the external a

Certainly there is a negative impact on development due to corruption in its corruption forms It is administrative and financial as well as direct impact on all sectors Including the health sector, which has seen in Iraq a marked setback in performance and services , The research aims at the possibility of demonstrating the negative effects of financial and administrative corruption on health institutions and Inability to meet health needs The extent to which Regulatory institutions and institutions manage to reduce financial and administrative corruption and ways of dealing with it The phenomenon of financial and administrative corruption is the most serious phenomenon because of its impact on economic growth, which in turn impedes the

     Coronavirus disease (COVID-19), which is caused by SARS-CoV-2, has been announced as a global pandemic by the World Health Organization (WHO), which results in the collapsing of the healthcare systems in several countries around the globe. Machine learning (ML) methods are one of the most utilized approaches in artificial intelligence (AI) to classify COVID-19 images. However, there are many machine-learning methods used to classify COVID-19. The question is: which machine learning method is best over multi-criteria evaluation? Therefore, this research presents benchmarking of COVID-19 machine learning methods, which is recognized as a multi-criteria decision-making (MCDM) problem. In the recent century, the trend of developing

Dust is a frequent contributor to health risks and changes in the climate, one of the most dangerous issues facing people today. Desertification, drought, agricultural practices, and sand and dust storms from neighboring regions bring on this issue. Deep learning (DL) long short-term memory (LSTM) based regression was a proposed solution to increase the forecasting accuracy of dust and monitoring. The proposed system has two parts to detect and monitor the dust; at the first step, the LSTM and dense layers are used to build a system using to detect the dust, while at the second step, the proposed Wireless Sensor Networks (WSN) and Internet of Things (IoT) model is used as a forecasting and monitoring model. The experiment DL system

Image compression plays an important role in reducing the size and storage of data while increasing the speed of its transmission through the Internet significantly. Image compression is an important research topic for several decades and recently, with the great successes achieved by deep learning in many areas of image processing, especially image compression, and its use is increasing Gradually in the field of image compression. The deep learning neural network has also achieved great success in the field of processing and compressing various images of different sizes. In this paper, we present a structure for image compression based on the use of a Convolutional AutoEncoder (CAE) for deep learning, inspired by the diversity of human eye

В статье рассматривается вопрос о связи флективных изменений с мыслительными процессами на материале русского и арабского языков, анализируются семантические, фонетические, морфологические и синтаксические основы фонограмматической когниции. Цель статьи выявление прямой связи между количественным звуковым изменением согласного состава слова и мыслительными процессами, с помощью которых человеческ