The Optical Character Recognition (OCR) is software for text recognition that takes an image containing text, to transform it into strings, then save them into a format that make it able to use in text editing programs. The OCR plays a significant role in the transformation of printed materials into digital text files. These digital files can be very useful for children and adults who have awkward reading. This is because a digital text can be used with computer programs that allow people to read them in different ways. In this paper, we developed system for Turkish character recognition under visual studio (C#) development environment, where machine learning is used to accurately predict optical characters, the reason why it has a high precision and high recognition speed through deep learning, which is one of the machine learning methods for OCR when drawing letters by mouse on the screen, then recognize by using back propagation algorithm.

The coronavirus is a family of viruses that cause different dangerous diseases that lead to death. Two types of this virus have been previously found: SARS-CoV, which causes a severe respiratory syndrome, and MERS-CoV, which causes a respiratory syndrome in the Middle East. The latest coronavirus, originated in the Chinese city of Wuhan, is known as the COVID-19 pandemic. It is a new kind of coronavirus that can harm people and was first discovered in Dec. 2019. According to the statistics of the World Health Organization (WHO), the number of people infected with this serious disease has reached more than seven million people from all over the world. In Iraq, the number of people infected has reached more than twenty-two thousand people until April 2020. In this article, we have applied convolutional neural networks (ConvNets) for the detection of the accuracy of computed tomography (CT) coronavirus images that assist medical staffs in hospitals on categorization chest CT-coronavirus images at an early stage. The ConvNets are able to automatically learn and extract features from the medical image dataset. The objective of this study is to train the GoogleNet ConvNet architecture, using the COVID-CT dataset, to classify 425 CT-coronavirus images. The experimental results show that the validation accuracy of GoogleNet in training the dataset is 82.14% with an elapsed time of 74 minutes and 37 seconds.

Authorship attribution of a style of writing is a method depend on analyzing texts in text mining, e.g., historical books and novels that famous authors wrote, attempted to measure the author's style, by choosing some attributes that show the author manner of writing. Assuming that these writers have a different way of writing that no other writer have; thus, authorship attribution is the essential of identifying the author of a given text [1].

The importance of risk management has been increasing for a lot of construction projects in different industries, and thus risk management department must be established to monitor the risks. The construction industry and its managers are exposed to a high degree of risk that leads to increasing the cost or delay in the projects. Therefore, there must be techniques used to control the risk and determine the best method to respond to it. Artificial intelligence and its techniques will be described includes the principle of and its advantages, types and the techniques that used for the classification that includes, decision tree and K-star, neural network and support vector machine and simulation techniques like system dynamic and also using optimization techniques, Particle swarm, Gravitational Search Algorithm as follows: Classification (decision tree, K-star, neural network, support vector. Machine and).

The purpose of this work is to investigate the problem of detecting transportable borrowings and text reuse. The article proposes a monolingual solution to this problem: translating the suspicious material into language collections for additional monolingual analysis. One of the major requirements for the suggested technique is robustness against machine learning ambiguities. The next step in the document analysis is split into two parts. The authors begin by retrieving documents-candidates that are similarity to other types of text recurrence. The paper proposes retrieving texts utilizing word clusters formed using distributional semantic for robustness. In the second stage, the authors use deep learning neural networks to compare the suspected document to candidates utilizing phrase embedding. The experimentation is carried out for the language pair “English-Arabic” on both articles and synthetic data.