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As social networks grow, they become more and more complex, and analyzing them becomes complicated. One way to reduce this complexity is to divide the network into subnets, which are also called communities. Dividing social networks into desirable communities can help analysts and experts to understand the behavior and function of the networks. Community detection in networks is a challenging topic in network science and various methods have been proposed for that. Modularity maximization is one of the state-of-the-art methods suggested for community detection. Modularity maximization is an NP-hard problem meaning that no polynomial-time algorithm exists that could solve the problem optimally unless P=NP. A group of approaches that could solve such problems is approximate algorithms. Identifying the influential nodes has many important applications in social networks. This technique could also be used in community detection. To maximize modularity, in this paper, we propose approximate algorithms based on identifying the influential nodes and their influence domain. We used the concept of scale-free networks to prove the approximate factor. Experiments on real-world networks show that the proposed algorithm can compete with the state-of-the-art methods of community detection algorithms. [ABSTRACT FROM AUTHOR]

Most of the proposed methods for investigating the transient and short-term voltage (STV) stability have assumed that only one of the two phenomena exists, i.e. either transient instability or STV instability. While these two phenomena exist simultaneously with one of them being dominant, according to the operating point of the system and disturbance condition. A unified online prediction method is presented in this paper to distinguish between the stable and unstable conditions. In the proposed method, by employing the intelligent wide area measurement system (IWAMS), the frequency deviation and the active power imbalance are calculated by the generation buses, which are considered as the intelligent generation buses (IGB). By tracking the trajectories of the frequency deviation and the active power imbalance and checking some simple defined rules, early prediction of both transient and STV instability can be achieved. In order to evaluate the performance of the proposed algorithm, the method has been tested on single machine infinite bus (SMIB) system, single machine load bus (SMLB) system, IEEE 39-bus system and IEEE 118-bus system. According to the obtained results, the proposed algorithm has the overall accuracy of 99.5% under symmetrical and asymmetrical faults, PMU measurement error, different operating points and topological changes. [ABSTRACT FROM AUTHOR]

Introduction: Data mining techniques, including decision tree algorithms can be used for modeling and identifying those at risk of developing COVID-19. The main goal of this study is estimating the risk of death of patients due to COVID-19, using the classification and regression tree (CART) algorithm based on the observed effective factors. Methods: This paper is an analytical study and the data of all patients with COVID-19 registered on the Kaggle site through Johns Hopkins University, was extracted. There was a total of 26,031 records from various countries. Data analysis was performed using JMP statistical software version 13. In the modeling section, decision tree algorithm and CART model were used. Results: The results of the classification and regression tree showed that among quantitative variables, age, the interval between hospitalization and result, the interval between onset of symptoms and test result, and the interval between hospitalization and test result, and the qualitative variable of gender were the most important factors affecting the outcome of patient treatment, respectively. According to the analysis of words, fever, cough, sore throat, fatigue, weakness, headache, chills, and runny nose, respectively, were the most common symptoms among patients with this disease. Conclusion: The accuracy of the fitted model was shown to be 94.1% for experimental data and 91.1% for educational data using the area under the receiver operating characteristic (ROC) curve. [ABSTRACT FROM AUTHOR]