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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]

This paper presents an algorithm for the segmentation of Blood Vessels from x-ray angiography of coronary artery images using Matched filters and Otsu thresholding. Identifying these arteries is a difficult process because of the vessel overlapping, and superimposition with various anatomical structures such as the ribs, the spine and the heart chambers. Firstly, the 3D Fourier and 2D Wavelet transforms are first used to eliminate the background and reduce noise in the images. Afterwards, a set of matched filters was applied to enhance the coronary arteries in the images. Finally with Otsu thresholding and 8-connected neighborhood pixels the segmented vessel was obtained. [ABSTRACT FROM AUTHOR]

One of the most important stages in Character Recognition Systems is "Segmentation", because any mistake will affect to all other tasks, especially to character recognition. This operation is more complex in Persian/Arabic writing than other Latin writing like English, and there has been an ongoing research on it. Other algorithms, that has been used as base as proposed algorithm, show 85% accuracy. In this paper, a new improved method has been presented by analyzing the visual features of the Persian/Arabic language. The proposed algorithm is able to segment existing fonts up to 98.5% accuracy or even 100% on some cases. The remaining error could be refined by applying a good character recognition technique and a precise vocabulary. [ABSTRACT FROM AUTHOR]

This paper present a simple watermarking approach based on the rotation of low frequency components of image blocks. The rotation process is performed with less distortion by projection of the samples on specific lines according to message bit. To have optimal detection Maximum Likelihood criteria has been used. Thus by computing the distribution of rotated noisy samples the optimum decoder is presented and its performance is analytically investigated. The privilege of this proposed algorithm is its inherent robustness against gain attack as well as its simplicity. Experimental results confirm the validity of the analytical derivations and also high robustness against common attacks. [ABSTRACT FROM AUTHOR]