The 2019-2020 coronavirus pandemic is an emerging infectious disease that has been referred to as the "COVID-19", which results from the coronavirus "sars-cov-2" that started in Wuhan, China, in Dec. 2019 and then spread worldwide. In this paper, an attempt for compiling and analyzing the information of the epidemiological outbreaks on "COVID‐19" based upon datasets on "2019‐nCoV" has been presented. An empirical data analysis with the visualizations was conducted for understanding the numbers of the variety of the cases that have been reported (i.e. confirmed, deaths, and recoveries) in and outside of Iraq and carried out a dynamic map visualization of the "Covid-19" expansion in a global manner through the date wise and in Iraq. We an investigation has been carried out as well, which characterized the pandemic effects Iraq and the entire world, with the use of machine learning. A k nearest neighbors' (KNN) model and a linear regression (LR) model have been proposed.This paper included the precise analysis of the confirmed cases, as well as the recovered cases, deaths, predicting the pandemic viral attacks and how far it is expanding in Iraq and the world, the LR model got the highest results, reaching 100 percent.

Breast cancer is becoming a global epidemic, affecting predominantly women. As a result, the number of people diagnosed with breast cancer is increasing every day. As a result, it is critical to have certain early detection methods in place that can assist patients in recognizing this condition at an early stage. Therefore, they might begin taking their medication to prevent the sickness from killing them. Different prediction approaches for early diagnosis of such diseases have been created in the machine learning fields. Those algorithms employ a variety of computational classifiers and claim to achieve satisfactory results in a few areas. However, no research was reached to determine which computationally sophisticated approach is more effective in detecting breast cancer. As a result, it is necessary to select the most effective strategy from the available options. This paper makes a contribution to the performance evaluation of 12 alternative classification strategies on datasets of breast cancer. The right explanations for the classifiers' dominance were investigated.

<span lang="EN-US">The tremendous growth in the availability of enormous text data from a variety of sources creates a slew of concerns and obstacles to discovering meaningful information. This advancement of technology in the digital realm has resulted in the dispersion of texts over millions of web sites. Unstructured texts are densely packed with textual information. The discovery of valuable and intriguing relationships in unstructured texts demands more computer processing. So, text mining has developed into an attractive area of study for obtaining organized and useful data. One of the purposes of this research is to discuss text pre-processing of automobile marketing domains in order to create a structured database. Regular expressions were used to extract data from unstructured vehicle advertisements, resulting in a well-organized database. We manually develop unique rule-based ways of extracting structured data from unstructured web pages. As a result of the information retrieved from these advertisements, a systematic search for certain noteworthy qualities is performed. There are numerous approaches for query recommendation, and it is vital to understand which one should be employed. Additionally, this research attempts to determine the optimal value similarity for query suggestions based on user-supplied parameters by comparing MySQL pattern matching and Jaccard similarity.</span>