Applying machine learning models on blockchain platform selection

Chhaya Dubey (), Dharmendra Kumar (), Ashutosh Kumar Singh () and Vijay Kumar Dwivedi ()
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Chhaya Dubey: United College of Engineering and Research
Dharmendra Kumar: United College of Engineering and Research
Ashutosh Kumar Singh: United College of Engineering and Research
Vijay Kumar Dwivedi: United College of Engineering and Research

International Journal of System Assurance Engineering and Management, 2024, vol. 15, issue 8, No 8, 3643-3656

Abstract: Abstract Recently, technology like Blockchain is gaining attention all over the world today, because it provides a secure, decentralized framework for all types of commercial interactions. When choosing the optimal blockchain platform, one needs to consider its usefulness, adaptability, and compatibility with existing software. Because novice software engineers and developers are not experts in every discipline, they should seek advice from outside experts or educate themselves. As the number of decision-makers, choices, and criteria grows, the decision-making process becomes increasingly complicated. The success of Bitcoin has spiked the demand for blockchain-based solutions in different domains in the sector such as health, education, energy, etc. Organizations, researchers, government bodies, etc. are moving towards more secure and accountable technology to build trust and reliability. In this paper, we introduce a model for the prediction of blockchain development platforms (Hyperledger, Ethereum, Corda, Stellar, Bitcoin, etc.). The proposed work utilizes multiple data sets based on blockchain development platforms and applies various traditional Machine Learning classification techniques. The obtained results show that models like Decision Tree and Random Forest have outperformed other traditional classification models concerning multiple data sets with 100% accuracy.

Keywords: Hyper-ledger; Ethereum; Blockchain; Platforms (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s13198-024-02363-2

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