ChatGPT is a large language model developed by OpenAI. It is trained on a dataset of conversational text and can be used to generate human-like responses to prompts in a variety of languages and formats. It can be used for tasks such as chatbots, language translation, and text completion. The role of ChatGPT is to generate human-like text based on a given prompt or context. It can be used in a variety of applications such as chatbots, language translation, text completion, and question answering. Additionally, it can be fine-tuned for specific tasks such as generating product descriptions or summarizing articles. It can also be used to generate creative writing such as poetry and stories. It can be integrated into a wide range of industries from customer service to entertainment, to research.

<span lang="EN-US">Artificial intelligence is a science that is growing at a tremendous speed every day and has become an essential part of many domains, including the medical domain. Therefore, countless artificial intelligence applications can be seen in the medical domain at various levels, which are employed to enhance early diagnosis and prediction and reduce the risks associated with many diseases, including heart diseases. In this article, machine learning techniques (logistic regression, random forest, artificial neural network, support vector machines, and k-nearest neighbors) are utilized to diagnose heart disease from the Cleveland Clinic dataset got from the University of California Irvine machine learning (UCL) repository and Kaggle platform then create a comparison between the performance of these techniques. In addition, some literature related to machine learning and deep learning techniques that aim to provide reasonable solutions in monitoring, detecting, diagnosing, and predicting heart disease and how these technologies assist in making health decisions are reviewed. Ten studies are selected and summarized by the authors published between 2017 and 2022 are illustrated. After executing a series of tests, it is seen that the most profitable performance in diagnosing heart disease is the support vector machines, with a diagnostic accuracy of 96%. This article has concluded that these techniques play a significant and influential role in assisting physicians and health care workers in analyzing heart patients' data, making health decisions, and saving patients' lives.</span>

The term cybersecurity refers to an environment capable of protecting digital devices, networks and information from unauthorized access and preventing data theft or alteration. It is composed of a collection of carefully crafted techniques, processes, and practices to protect sensitive information and deterring cyber-attacks. In the recent period, the domain of cybersecurity has undergone rapid growth in response to the increasing cyber threats. Cybersecurity includes important tactics that help protect the digital environment, which are firewalls, encryption, secure passwords, and threat detection and response systems. Employees must be trained on these tactics. This article will discuss the five most pressing challenges facing the cybersecurity industry today that must be taken into account by businesses, organizations, and individuals in order to secure their confidential data from cybercrime. The conclusion of the article highlighted the significance of growing awareness about cybersecurity risks in order to effectively handle digital environments and protect them from any electronic threats.

Abstract-This paper presents an analysis of the factors which could have possible affect over the adoption of cloud computing via the Iraqi e-government. A conceptual framework model for cloud computing within Iraqi e-government is proposed, analyzed, evaluated and discussed.

Heart diseases are diverse, common, and dangerous diseases that affect the heart's function. They appear as a result of genetic factors or unhealthy practices. Furthermore, they are the leading cause of mortalities in the world. Cardiovascular diseases seriously concern the health and activity of the heart by narrowing the arteries and reducing the amount of blood received by the heart, which leads to high blood pressure and high cholesterol. In addition, healthcare workers and physicians need intelligent technologies that help them analyze and predict based on patients’ data for early detection of heart diseases to find the appropriate treatment for them because these diseases appear on the patient without pain or noticeable symptoms, which leads to severe concerns such as heart failure and stroke and kidney failure. In this regard, the authors highlight an amount of literature considered the most practical in utilizing machine learning techniques in predicting heart disease. Twenty articles were chosen out of fifty articles gathered and summarised in a table form. The main goal is to make this article a reference that can be utilized in the future to assist healthcare workers in studying these techniques with ease and saving time and effort on them. This article has concluded that machine learning techniques have a significant and influential role in analyzing disease data, predicting heart disease, and assisting decision-making. In addition, these techniques can analyze data that reaches millions of cohorts.