Data-driven Security Assessment Using High Content Database during the COVID -19 Pandemic

As the coronavirus disease (COVID-19) broke out in late 2019, the electricity sector was significantly impacted. Hence, the effects of the pandemic and restricting measures in power system operation are investigated during pandemic circumstances. The secure operation of the power system is a fundamental requirement. Appropriate procedures should be taken to mitigate these effects and ensure the power system's security. Accordingly, in this study, the authors determine that the COVID-19 pandemic can change the system's operating conditions in the first stage. Since data-driven security assessment methods require the training database to learn about Security constraints, this paper proposes an efficient database generation strategy respecting the consequences of the COVID-19 outbreak. The proposed strategy provides a training set with high information content compatible with the operating conditions. To this end, the method consists of a characteristics extraction approach and updating scheme. The characteristics should be extracted to represent the operating conditions of the system. Further, the similarity of intervals is compared using characteristics in updating scheme. The copula-based sampling approach is provided to generate the random samples. The proposed strategy generates a database for data-driven methods. Therefore, it can be utilized in various applications of security assessment. Real-world data is mapped to the IEEE 39-bus system to illustrate the framework efficiency. The outcomes indicate that a classification using the proposed strategy outperforms conventional methods in terms of evaluation metrics. © 2017 Elsevier Inc. All rights reserved.

A machine learning-driven spatio-temporal vulnerability appraisal based on socio-economic data for COVID-19 impact prevention in the U.S. counties.

A mature and hybrid machine-learning model is verified by mature empirical analysis to measure county-level COVID-19 vulnerability and track the impact of the imposition of pandemic control policies in the U.S. A total of 30 county-level social, economic, and medical variables and a timeline of the imposed policies constitutes a COVID-19 database. A hybrid feature-selection model composed of four machine-learning algorithms is developed to emphasize the regional impact of community features on the case fatality rate (CFR). A COVID-19 vulnerability index (COVULin) is proposed to measure the county's vulnerability, the effects of model's parameters on mortality, and the efficiency of control policies. The results showed that the dense counties in which minority groups represent more than 45% of the population and those with poverty rates greater than 24% were the most vulnerable counties during the first and the last pandemic peaks, respectively. Highly-correlated CFR and COVULin scores indicated a close agreement between the model outcomes and COVID-19 impacts. Counties with higher poverty and uninsured rates were the most resistant to government intervention. It is anticipated that the proposed model can play an essential role in identifying vulnerable communities and help reduce damages during long-term alike disasters.

A novel fusion-based deep learning model for sentiment analysis of COVID-19 tweets.

Undoubtedly, coronavirus (COVID-19) has caused one of the biggest challenges of all times. The ongoing COVID-19 pandemic has caused more than 150 million infected cases and one million deaths globally as of May 5, 2021. Understanding the sentiment of people expressed in their social media comments can help in monitoring, controlling, and ultimately eradicating the disease. This is a sensitive matter as the threat of infectious disease significantly affects the way people think and behave in various ways. In this study, we proposed a novel method based on the fusion of four deep learning and one classical supervised machine learning model for sentiment analysis of coronavirus-related tweets from eight countries. Also, we analyzed coronavirus-related searches using Google Trends to better understand the change in the sentiment pattern at different times and places. Our findings reveal that the coronavirus attracted the attention of people from different countries at different times in varying intensities. Also, the sentiment in their tweets is correlated to the news and events that occurred in their countries including the number of newly infected cases, number of recoveries and deaths. Moreover, common sentiment patterns can be observed in various countries during the spread of the virus. We believe that different social media platforms have great impact on raising people's awareness about the importance of this disease as well as promoting preventive measures among people in the community.