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VLSI is the relating to more than one branch of science of utilizing the advanced semiconductor technology to develop various functional units of computational System. The most complex function carried out by ALU is multiplication. Digital multiplication is most extensively used operation particularly in signal processing, System designer has to sacrifice silicon die area in order to make the multiplication as soon as possible. In this paper, various multiplier architectures are simulated and compared with high speed and low power 8x8 Vedic multiplier (Urdhva Triyagbhayam) which uses modified carry select adder for partial product reduction. The design is implementation and simulation has been done by Microwind tool with 90 nm technology. The simulation result shows 58% of power reduction, 65% of delay reduction and 44% of area reduction has been attained.

Long term diseases require continuous monitoring, sometimes periodic monitoring to verify if any serious concern requires an attention. In recent years, it is noticed that the COVID-19 pandemic has triggered serious concern towards the long-term diseased individuals. As the mortality rate of the COVID-19 clearly indicates that the highest percentage of deaths reflect in the individuals suffering from long term diseases such as diabetes, pneumonia, cardiovascular and acute renal failure. Though they are tested for COVID negative through conventional apparatus, it doesn’t confer that they are completely out of post consequences. Hence a periodic, if necessary continuous monitoring needs to be aided, which in current scenario is a challenging task. Hence, our current article reviews the use of machine learning algorithms to detect and diagnose pre and post COVID-19 effects on long term diseased patients.