<span>Novel coronavirus (COVID-19) is a newly discovered infectious disease that has received much attention in the literature because of its rapid spread and daily global deaths attributable to such disease. The White House, together with a coalition of leading research groups, has published the freely available COVID-19 Open Research Dataset to help the global research community apply the recent advances in natural language processing and other AI techniques in generating novel insights that can support the ongoing fight against this disease. In this paper, the hierarchical and k-means clustering techniques are used to create a tool for identifying similar articles on COVID-19 and filtering them based on their titles. These articles are classified by applying three data mining techniques, namely, random forest (RF), decision tree (DT) and bagging. By using this tool, specialists can limit the number of articles they need to study and pre-process these articles via data framing, tokenisation, normalisation and term frequency-inverse document frequency. Given its 2D nature, the dimensionality of this dataset is reduced by applying t-SNE. The aforementioned data mining techniques are then cross validated to test the accuracy, precision and recall performance of the proposed tool. Results show that the proposed tool effectively extracts the keywords for each cluster, with RF, DT and bagging achieving optimal accuracies of 98.267%, 97.633% and 97.833%, respectively.</span>

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.

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In this paper, a new technique to monitor and control bidirectional DC-DC converter was designed and implemented precisely. A prototype of a complete system is verified with efficient communication capabilities. This system is realized by integrating the internet of things (IoT) operating system and the bidirectional DC-DC converter. The IoT communication facilities further develop and extend the platform for this system. The DC-DC converter with the soft switching technique will then convert the battery voltage to a high voltage of 380V inverter bus in emergencies via boost converter mode. High-frequency toroidal transformer has been used for power level shifting and isolation between the primary and secondary sides of the transformer. The closed-loop control scheme is implemented in software by using a high-performance 32-bit STM32 micro controller. IoT technique is used to find current, voltage and perform the communication smoothly through Wi-Fi sensors to complete the design of the system. The results of the proposed system prove the effectiveness of the proposed system with high-performance specifications.

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