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The COVID-19 pandemic has created massive issues around the world. To ensure that education continued during the crisis, educational institutions had to implement a variety of initiatives. This paper aims to examine the growth and country collaboration on social media (SM) research during the COVID-19 pandemic through a systematic review and investigate the impact of this body of work by citation and network analyses. The number of articles, keywords, and clusters of worldwide academic scholars working in the area was mapped using R studio and the VOS viewer tool. According to the study results, 519 articles have been retrieved from the Web of Science in the field of domain. The USA has produced the most publications, and Chen IH and Lin CY were the most prolific authors. Furthermore, the most studies on SM use in higher education were released in the International Journal of Environmental Research and Public Health. This research will help academic researchers, organizations, and policymakers to understand the ongoing research on SM during the last pandemic. It will help future academics analyze the evolution of social media technologies in higher education throughout the pandemic and identify areas for further study.

Clustering of multi-view data has got broad consideration of the researchers. Multi-view data is composed through different domain which shows the consistent and complementary behavior. The existing studies did not draw attention of over-fitting and sparsity among the diverse view, which is the considerable issue for getting the unique consensus knowledge from these complementary data. Herein article, a multi-view clustering approach is recommended to provide the consensus solution from the multiview data. To accomplish this task, we exploit non-negative matrix factorized method to generate a cost function. Further, manifold learning model is used to build the graph through the nearest neighbor strategy, which is effective to save the geometrical design for data and feature matrix. Furthermore, the over-fitting problem, sparsity is handled through adaption of frobenious norm, and L 1 -norm on basis and coefficient matrices. The whole formulation is done through the mathematical function, which is optimized through the iterative updating strategy to get the optimal solution. The computational experiment is carried on the available datasets to exhibits that the proposed strategy beats the current methodologies in terms of clustering execution.INDEX TERMS Non-negative matrix factorization, multi-view data, manifold structure, nearest neighbor.

There are many features which appear on the surface of the sun. One of these features that appear clearly are the dark threads in the Hydrogen alpha (Hα) spectrum solar images. These 'filaments' are found to have a definite correlation with Coronal Mass Ejections (CMEs). A CME is a large release of plasma into space. It can be hazardous to astronauts and the spacecraft if it is being ejected towards the Earth. Knowing the exact attributes of solar filaments may open the way towards predicting the occurrence of CMEs. In this paper, an efficient and fully automated algorithm for solar filament segmentation without compromising accuracy is proposed. The algorithm uses some statistical measures to design the thresholding equations and it is written in the C++ programming language. The square root of the range as a measure of variability of image intensity values is used to determine the size of the sliding window at run time. There are many previous studies in this area, but no single segmentation method that could precisely claim to be fully automatic exists. Samples were taken from several representative regions in low-contrast and high-contrast solar images to verify the viability and efficacy of the method.

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