  In recent years, several research articles have been published in the field
of corona-virus caused diseases like severe acute respiratory syndrome (SARS),
middle east respiratory syndrome (MERS) and COVID-19. In the presence of
numerous research articles, extracting best-suited articles is time-consuming
and manually impractical. The objective of this paper is to extract the
activity and trends of corona-virus related research articles using machine
learning approaches. The COVID-19 open research dataset (CORD-19) is used for
experiments, whereas several target-tasks along with explanations are defined
for classification, based on domain knowledge. Clustering techniques are used
to create the different clusters of available articles, and later the task
assignment is performed using parallel one-class support vector machines
(OCSVMs). Experiments with original and reduced features validate the
performance of the approach. It is evident that the k-means clustering
algorithm, followed by parallel OCSVMs, outperforms other methods for both
original and reduced feature space.
