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.

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.

Mathematical approach to understand the kinetics of alpha-synuclein aggregation: relevance to Parkinson's disease.

alpha-Synuclein aggregation is a hallmark pathological feature in Parkinson's disease (PD). The conversion of alpha-synuclein from soluble monomer to insoluble aggregates through the toxic oligomeric intermediates underlie the neurodegeneration associated with PD. Redox active metal ions such as iron (Fe) and copper (Cu) are known to enhance alpha-synuclein fibrillogenesis.

An algorithmic approach to understand trace elemental homeostasis in serum samples of Parkinson disease.

A classical problem in neurological disorders is to understand the progression of disorder and define the trace elements (metals) which play a role in deviating a sample from normal to an abnormal state, which implies the need to create a reference knowledge base (KB) employing the control samples drawn from normal/healthy set in the context of the said neurological disorder, and in sequel to analytically understand the deviations in the cases of disorders/abnormalities/unhealthy samples. Hence building up a computational model involves mining the healthy control samples to create a suitable reference KB and designing an algorithm for estimating the deviation in case of unhealthy samples. This leads to realizing an algorithmic cognition-recognition model, where the cognition stage establishes a reference model of a normal/healthy class and the recognition stage involves discriminating whether a given test sample belongs to a normal class or not.