Efficient binary grasshopper optimization based neural network algorithm for bitcoin value prediction

【Author】 Kumar, A. Saran; Priyanka, S.; Dhanashree, K.; Praveen, V; Rekha, R.

【Source】INTERNATIONAL JOURNAL OF NONLINEAR ANALYSIS AND APPLICATIONS

【影响因子】0.000

【Abstract】Digital currency plays a vital role in the process of trading as it helps the sellers and buyers to earn more profit. In today's world, many categories of cryptocurrencies exist and each one of them employs its own security algorithms. Bitcoin price prediction is a complex problem that needs advanced algorithms to solve exactly. In this paper, swarm based intelligence algorithms are applied in order to solve the bitcoin value prediction problem. In particular, Ant Colony Optimization and Binary Grasshopper Optimization algorithms are combined as a hybrid framework to select the most critical features in the dataset for bitcoin value prediction. The extracted features from the hybrid model are given as input to the convolutional neural network to predict the price of the bitcoins. As per the experimental results, the proposed hybrid algorithm produces better results when compared with the stand-alone version of grasshopper and neural network algorithms.

【Keywords】Bitcoin; Value Prediction; Optimization Algorithm; Binary Grasshopper Algorithm; CNN Algorithm

【发表时间】2022

【收录时间】2022-04-25

【文献类型】实证性文章

【主题类别】

区块链治理-市场治理-