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Journal of Emerging Trends in Blockchain Technology (JETBT)

Published

Hybrid Machine Learning and Blockchain Approaches for Secure and Transparent Stock Prediction in India

Published in January-December 2025 (Vol. 3, Issue 3, 2025)

Hybrid Machine Learning and Blockchain Approaches for Secure and Transparent Stock Prediction in India - Issue cover

Abstract

Indian stock market, predictive analytics, blockchain technology, machine learning, LSTM, XGBoost, Random Forest, stock price forecasting, data integrity, data security, transparency, smart contracts, IPFS, decentralized storage, cryptographic hashing, hybrid model, financial technology, FinTech, regulatory compliance, data transparency, auditability, forecasting accuracy

Authors (2)

Dr. Kaushal Jani
ORCID

Indus University

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Dr. Nisarg Patel
ORCID

Coreway Technologies

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Article Information

Article ID:
JETBT330002
Paper ID:
JETBT-01-000016
Published Date:
2026-01-05

How to Cite

Kaushal, D., & Nisarg, D. (2026). Hybrid Machine Learning and Blockchain Approaches for Secure and Transparent Stock Prediction in India. Journal of Emerging Trends in Blockchain Technology (JETBT), 3(3), xx-xx. https://jetbt.org/articles/11

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Modern banking systems are heavily dependent on internet connectivity, making them vulnerable during network outages caused by cyberattacks, natural disasters, or warfare. This paper proposes a novel intranet-based backup system that enables customers to withdraw cash from their home branch ATMs during internet disruptions. The system utilizes local area network (LAN) infrastructure combined with blockchain technology to ensure secure, transparent, and tamper-proof transaction processing. Our proposed architecture implements a hybrid Byzantine Fault Tolerance (BFT) consensus algorithm to validate offline transactions and synchronize data once connectivity is restored. The system restricts withdrawals to predetermined limits at the customer's registered home branch, ensuring liquidity management and fraud prevention. Performance analysis demonstrates that the proposed system achieves 99.7% transaction success rate with an average processing time of 2.3 seconds while maintaining data integrity through cryptographic hashing. This research contributes to financial system resilience by providing a practical solution for maintaining critical banking services during emergency situations. Index Terms—Banking backup system, blockchain, Byzantine Fault Tolerance, disaster recovery, intranet, offline ATM, network resilience, emergency banking

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