Cryptocurrencies are peer-to-peer-based transaction systems where the data exchanges are secured using the secure hash algorithm (SHA)-256 and message digest (MD)-5 algorithms. The prices of cryptocurrencies are highly volatile and follow stochastic moments and have reached their unpredictable limits. They are commonly used for investment and have become a substitute for other types of investment like metals, estates, and the stock market. Their importance in the market raises the strict requirement for a sturdy forecasting model. However, cryptocurrency price prediction is quite challenging due to its dependency on other cryptocurrencies. Many researchers have used machine learning and deep learning models, and other market sentiment-based models to predict the price of cryptocurrencies. As all the cryptocurrencies belong to a specific class, we can infer that the increase in the price of one cryptocurrency can lead to a price change for other cryptocurrencies. Researchers had also utilized the sentiments from tweets and other social media platforms to increase the performance of their proposed system. Motivated by these, in this paper, we propose a hybrid and robust framework, DL-Gues, for cryptocurrency price prediction, that considers its interdependency on other cryptocurrencies and also on market sentiments. We have considered price prediction of Dash carried out using price history and tweets of Dash, Litecoin, and Bitcoin for various loss functions for validation. Further, to check the usability of DL-GuesS on other cryptocurrencies, we have also inferred results for price prediction of Bitcoin-Cash with the price history and tweets of Bitcoin-Cash, Litecoin, and Bitcoin.

Cryptographic forms of money are distributed peer-to-peer (P2P) computerized exchange mediums, where the exchanges or records are secured through a protected hash set of secure hash algorithm-256 (SHA-256) and message digest 5 (MD5) calculations. Since their initiation, the prices seem highly volatile and came to their amazing cutoff points during the COVID-19 pandemic. This factor makes them a popular choice for investors with an aim to get higher returns over a short span of time. The colossal high points and low points in digital forms of money costs have drawn in analysts from the scholarly community as well as ventures to foresee their costs. A few machines and deep learning algorithms like gated recurrent unit (GRU), long short-term memory (LSTM), autoregressive integrated moving average with explanatory variable (ARIMAX), and a lot more have been utilized to exactly predict and investigate the elements influencing cryptocurrency prices. The current literature is totally centered around the forecast of digital money costs disregarding its reliance on other cryptographic forms of money. However, Dash coin is an individual cryptocurrency, but it is derived from Bitcoin and Litecoin. The change in Bitcoin and Litecoin prices affects the Dash coin price. Motivated from these, we present a cryptocurrency price prediction framework in this paper. It acknowledges different cryptographic forms of money (which are subject to one another) as information and yields higher accuracy. To illustrate this concept, we have considered a price prediction of Dash coin through the past days' prices of Dash, Litecoin, and Bitcoin as they have hierarchical dependency among them at the protocol level. We can portray the outcomes that the proposed scheme predicts the prices with low misfortune and high precision. The model can be applied to different digital money cost expectations.INDEX TERMS Cryptocurrency, price analysis, volume analysis, deep learning, machine learning, survey, ensemble model, fusion in metrics.

In the present work, we investigate the dynamics of a bubble, rising inside a vertical sinusoidal wavy channel. We carry out a detailed numerical investigation using a dual grid level set method coupled with a finite volume based discretization of the Navier–Stokes equation. A detailed parametric investigation is carried out to identify the fate of the bubble as a function of Reynolds number, Bond number, and the amplitude of the channel wall and represented as a regime map. At a lower Reynolds number (high viscous force), we find negligible wobbling (path instability) in the dynamics of the bubble rise accompanied only with a change in shape of the bubble. However, at a higher Reynolds number, we observe an increase in the wobbling of the bubble due to the lowered viscous effects. Conversely, at a lower Bond number, we predict a stable rise of the bubble due to higher surface tension force. However, with a gradual increase in the Bond number, we predict a periodic oscillation which further tends to instigate the instability in the dynamics. With a further increase in the Bond number, a significant reduction in instability is found unlike a higher Reynolds number with only change in the shape of the bubble. At lower values of Reynolds numbers, Bond numbers, and channel wall amplitudes, the instability is discernible; however, with an increase in the channel wall amplitude, the bubble retains integrity due to higher surface tension force. At a higher Bond number and channel wall amplitude, a multiple breakup in the form of secondary bubbles is observed. We propose a correlation which manifests the average bubble rise velocity and the fluctuating velocity (due to channel waviness) as a function of Reynolds number, Bond number, and channel wall amplitude. Finally, we conclude that the bubble dynamics pertinent to the offset channels with varying amplitudes does not remain the same as that of the symmetric channel.

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