Lion Swarm Optimization with Deep Learning Driven Predictive Model on Blockchain Financial Product Return Rates

Abstract

Recently, financial globalization is an extremely improved in distinct manners for enhancing service quality with advanced resources. An effectual application of bitcoin Blockchain (BC) approaches allows the shareholders to be concern regarding the risk and return of financial product. The shareholders mainly concentrate on the predictive of risk and return rates of financial product. Thus, an automated return rate bitcoin predictive method develops vital for BC financial product (FP). A newly planned machine learning (ML) and deep learning (DL) techniques offers a way for the return rate predictor systems. This work designs a Lion Swarm Optimization with Deep Learning Driven Predictive Model on Blockchain Financial Product Return Rates (LSODL-BFPRR) technique. The projected LSODL-BFPRR technique lies in the effectual forecasting of return rates in the BC financial sector. In the presented LSODL-BFPRR technique, stacked bidirectional gated recurrent unit (SBiGRU) approach was exploited for return rate classification. To modify the hyperparameters based on the SBiGRU approach, the LSO algorithm is used. The LSODL- BFPRR technique exploits Ethereum (ETH) return rate as the target. The experimental outcomes of the LSODL-BFPRR technique are tested using a series of simulations and the results demonstrate the effectual predicting results of the LSODL-BFPRR technique over other ones.