Cryptocurrencies are nowadays getting popular for investment due to its various benefits such as low transaction cost, blockchain secured platform, profit, etc. Bitcoin being top of the market capitalization currency, gained more popularity during covid-19 pandemic. This study focuses on bitcoin price prediction with covid-19 sentiment. Here Long Short Term Memory Deep learning model based on machine learning is used for price prediction. At the end both results i.e., with covid-19 sentiment and without it are compared which shows model performs better by adding sentiments.

Cryptocurrency, also known as crypto is a type of digital currency that operates as an exchange mechanism over a computer network and is not supported or maintained by any governing authority. Compared to other equities and bonds, the price of crypto-currencies is highly volatile in nature. Its fluctuations are highly influenced by supply and demand, sentiments of investors and media hype. This paper introduces quantum harmonic oscillator model to measure the value at risk for the fix amount of investment to identify the maximum risk behaviour of cryptocurrency.

Stock returns have a mixed distribution, which describes Gaussian and non-Gaussian characteristics of the stock return distribution, according to the solution of the Schrodinger equation for the quantum harmonic oscillator. As a model for the market force, A quantum harmonic oscillator which uses a stock return from short-run oscillations to long-run equilibrium will be suggested. We will calculate fitting errors and goodness of fit statistics by analysing the All-Share Index of the National Stock Exchange of India.

Cryptography is the technique of using mathematical algorithms to encrypt and decrypt the information. The process of converting plaintext to ciphertext is known as encryption, whereas the process of converting ciphertext to plaintext is known as decryption. Encryption and decryption methods based on Mellin Transform are unable to provide more security while transmitting the information. RSA algorithm is an Asymmetric key cryptography algorithm. The purpose of this study is to present a cryptographic method that uses the RSA algorithm and Mellin Transform to improve communication security.

Supervised Learning is an important type of Machine learning. It includes regression and classification problems. In Supervised learning, Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) can be used for classification and regression. Here, both algorithms are used for regression problem. The stock data is trained by SVR and KNN respectively to predict the stock price of the next day using python tool. Both algorithms are compared and it is observed that the price predicted by SVR is closer as compared to KNN.