FastSS: Fast and smooth segmentation of JPEG compressed printed text documents using DC and AC signal analysis.

With the surge of COVID-19 pandemic, the world is moving towards digitization and automation more than it was presumed. The Internet is becoming one of the popular mediums for communication, and multimedia (image, audio, and video) combined with data compression techniques play a pivotal role in handling a huge volume of data that is being generated on a daily basis. Developing novel algorithms for automatic analysis of compressed data without decompression is the need of the present hour. JPEG is a popular compression algorithm supported in the digital electronics world that achieves compression by dividing the whole image into non-overlapping blocks of 8 × 8 pixels, and subsequently transforming each block using Discrete Cosine Transform (DCT). This research paper proposes to carry out Fast and Smooth Segmentation (FastSS) directly in JPEG compressed printed text document images at text-line and word-level using DC and AC signals. From each 8 × 8 block, DC and AC signals are analyzed for accomplishing Fast and Smooth segmentation, and subsequently, two Faster segmentation (MFastSS) algorithms are also devised using low resolution-images generated by mapping the DC signal (DC Reduced Image) and encoded DCT (ECM Image) coefficients separately. Proposed models are tested on various JPEG compressed printed text document images created with varied space and fonts. The experimental results have demonstrated that the direct analysis of compressed streams is computationally efficient, and has achieved speed gain more than 90% when compared to uncompressed domains.

Target specific mining of COVID-19 scholarly articles using one-class approach.

The novel coronavirus disease 2019 (COVID-19) began as an outbreak from epicentre Wuhan, People's Republic of China in late December 2019, and till June 27, 2020 it caused 9,904,906 infections and 496,866 deaths worldwide. The world health organization (WHO) already declared this disease a pandemic. Researchers from various domains are putting their efforts to curb the spread of coronavirus via means of medical treatment and data analytics. In recent years, several research articles have been published in the field of coronavirus 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 coronavirus related research articles using machine learning approaches to help the research community for future exploration concerning COVID-19 prevention and treatment techniques. 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). These defined tasks describes the behavior of clusters to accomplish target-class guided mining. 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.