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https://doi.org/10.5772/intech...
Part of book or chapter of book . 2026 . Peer-reviewed
License: CC BY
Data sources: Crossref
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Integration of AI and ML in Cryptoeconomics

Authors: Basetty Mallikarjuna; Puspalatha Chittem Setty and Sam Goundar;

Integration of AI and ML in Cryptoeconomics

Abstract

The integration of three different domains: artificial intelligence (AI) and machine learning (ML) and advanced deep learning (DL) algorithms with blockchain and cryptocurrency ecosystems is revolutionizing financial technologies. The “Cryptoeconomics” word born from the intersection of cryptography, economics, and decentralized computation. It has its own importance existing in AI and ML to enhance decision-making, optimize consensus mechanisms, detect fraud, and forecast market trends. This chapter provides the AI and blockchain, highlighting innovations in algorithmic trading, predictive analytics, and security management. This chapter also provides the research gaps, AI and ML applications in cryptoeconomics, technical challenges, and future prospects. And overall, this chapter gives the applications and implementation of ethical concerns, and potential research directions for establishing the intelligent and sustainable cryptoeconomic systems.

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    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    0
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
hybrid