A. Saran Kumar - Department of CSE, Bannari Amman Institute of Technology, Erode, Tamilnadu, India
S. Priyanka - Department of CSE, Bannari Amman Institute of Technology, Erode, Tamilnadu, India
K. Dhanashree - Department of CSE, Sri Ramakrishna Engineering College, Coimbatore, Tamilnadu, India
V. Praveen - Department of CSE, Bannari Amman Institute of Technology, Erode, Tamilnadu, India
R. Rekha - Department of IT, PSG College of Technology, Coimbatore, Tamilnadu, India

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.

Bitcoin, Value Prediction, Optimization Algorithm, Binary Grasshopper Algorithm, CNN Algorithm