A Comprehensive Survey on Covid-19 Prediction Models

The world witnessed the outbreak of the novel coronavirus (COVID-19) in late December 2019 and since then the cases have been reported in various countries across continents. The rising number of cases posed an alarming health crisis situation in the whole world. It was important for countries to understand in advance when peaks can be reported and thus accordingly prepare their systems to save lives. Forecasting the number of cases and understanding the underlying trends associated with rise and fall of infection spread became paramount for researchers. Numerous studies have been conducted and a plethora of models have been proposed to aid in forecasting the number of COVID-19 cases using deep learning and other techniques. In this paper, we have presented a review of the studies conducted in past to predict the number of COVID-19 cases. Under this study, a total of 64 papers obtained through the interfaces of Google Scholar and Carnegie Mellon University (CMU) were included and analysed. The contributions in this review:
1. Provides useful insights into the work that has been done so far in the field of COVID-19 prediction modeling.
2. Highlights the various techniques used and their respective performance.
3. Reveals the future directions.

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Ethereum Analytics: Exploring the Ethereum Blockchain

With over $385 billion in market capitalization (as of 31st August, 2021), Ethereum is the largest public blockchain that supports smart contracts. It is also the second most valuable cryptocurrency, just after Bitcoin.

In this work, we have done exploratory data analysis of the Ethereum blockchain. We started by giving an overview of the Digital Ledger Technology and then explained several consensus mechanisms. Further, we described blockchain technology and gave an explanation of how it works. Then we stated the different types of blockchains being used in the industry today. A detailed description of the Ethereum blockchain is then given by deeply talking about the Ethereum model and the Ethereum Roadmap. After that, the data analysis setup is reported and the dataset schema is presented along with a dataset relational diagram. Further, we move on to the analytics and provide a generic analytics of the Ethereum blockchain related to transactions, gas, addresses, smart contracts and ETH price. Finally, we present a focused analytics of transactions, volume, accounts and smart contracts.