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.
Network Traffic Monitoring and Analysis (NTMA) is the main element to network management, especially to correctly operate large-scale networks such as the Internet on which modern academic organizations heavily depend. Their traffic use increases significantly because students, staff members, and research labs use them to search information. It is necessary to analyze, measure, and classify this Internet traffic according to the need of different stakeholders such as Internet Service Providers and network administrators. Moreover, bandwidth congestions frequently occur, causing user dissatisfaction. This study tries to find different characterizations such as data over hosts, countries, cities, companies, top-level domains, and servers. In addition, this is a new study to find out different patterns and levels of analysis from the device to its international requests. Our findings show that the highest traffic use is on Mondays and Wednesdays. Web server and DNS server drop in response to fault tolerance. Social networks consume most of the bandwidth, such as 42% Facebook followed by 22% WhatsApp in peak hours. The second most accessed sites are search engines. Google is the most used one. About 59% of the host cities are outside Iraq, in particular USA and the UK. In Amara and Baghdad cities, the requested sites are 51% and 49% overseas. About 40% of the traffic is provided by EarthLink Ltd. Communication Internet services (Iraq), 14% EdgeCast. 12% level3, 9% Facebook, 7% Google, Akamai-as and Microsoft-corp-msn-as-block. This study gives guidelines for network administrators to improve their performance and bandwidth at the educational networks.
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