Hybrid Machine Learning and Blockchain Approaches for Secure and Transparent Stock Prediction in India
Published in January-December 2025 (Vol. 3, Issue 3, 2025)

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Published in:
January-December 2025 (Vol. 3, Issue 3, 2025)- 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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Blockchain-Based Intranet Banking Backup System for Emergency Cash Withdrawal During Network Outages
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
