DEEP LEARNING AND BLOCKCHAIN-ENABLED FRAMEWORK FOR BITCOIN PRICE PREDICTION AND SECURE TRANSACTION INTELLIGENCE

Harish Barapatre; Om Pawar; Rupesh Thorat; Shreyas Rhatval

doi:10.29121/ijoest.v10.i2.2026.756

Authors

Dr. Harish Barapatre Associate Professor, Department of Computer Engineering, Yadavrao Tasgaonkar Institute of Engineering and Technology, Bhivpuri Road Karjat, Maharashtra, 410201, India
Om Pawar Student, Department of Computer Engineering, Yadavrao Tasgaonkar Institute of Engineering and Technology, Bhivpuri Road Karjat, Maharashtra, 410201, India
Rupesh Thorat Student, Department of Computer Engineering, Yadavrao Tasgaonkar Institute of Engineering and Technology, Bhivpuri Road Karjat, Maharashtra, 410201, India
Shreyas Rhatval Student, Department of Computer Engineering, Yadavrao Tasgaonkar Institute of Engineering and Technology, Bhivpuri Road Karjat, Maharashtra, 410201, India

DOI:

https://doi.org/10.29121/ijoest.v10.i2.2026.756

Keywords:

Bitcoin Prediction, Deep Learning, Blockchain Technology, Lstm, Cryptocurrency, Time Series Forecasting, Financial Analytics

Abstract

Bitcoin price prediction has become a critical research problem due to its extreme volatility and increasing adoption in financial systems. Traditional statistical and machine learning models often fail to capture the complex nonlinear dependencies and temporal dynamics present in cryptocurrency markets. In recent years, deep learning techniques such as Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) have demonstrated strong capability in modeling sequential financial data and extracting hidden temporal patterns Nakamoto (2008), LeCun et al. (2015). However, most existing approaches rely solely on historical price data and ignore the rich transactional and structural information available in blockchain networks.
This paper proposes a hybrid conceptual framework that integrates deep learning-based time-series prediction with blockchain-based transaction intelligence. The proposed system utilizes historical Bitcoin price data, trading volume, and blockchain-derived features such as transaction count, hash rate, and wallet activity to enhance prediction accuracy. Additionally, blockchain technology ensures data integrity, transparency, and resistance to tampering, thereby improving trustworthiness in financial prediction systems Hochreiter and Schmidhuber (1997), Cho et al. (2014).
The framework combines feature engineering, deep neural architectures, and secure blockchain data validation into a unified pipeline. This approach not only improves predictive capability but also introduces a secure and verifiable mechanism for financial data processing. The proposed model is expected to provide more robust and reliable Bitcoin price forecasts compared to conventional methods.

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References

Aggarwal, S., Kumar, N., and Tanwar, S. (2020). Blockchain-Envisioned UAV Communication using 6G Networks. IEEE Network, 34(6), 160–167.

Brownlee, J. (2018). Time Series Prediction with Deep Learning: A Review. Machine Learning Mastery.

Buterin, V. (2014). A Next-Generation Smart Contract and Decentralized Application Platform. Ethereum White Paper.

Chen, T., and Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (785–794).

Cho, K., van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y. (2014). Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation. arXiv preprint arXiv:1406.1078.

Christidis, K., and Devetsikiotis, M. (2016). Blockchains and Smart Contracts for the Internet of Things. IEEE Access, 4, 2292–2303.

Dean, J., and Ghemawat, S. (2004). Mapreduce: Simplified Data Processing on Large Clusters. In Proceedings of the 6th Symposium on Operating Systems Design and Implementation (OSDI) (137–150).

Glorot, X., and Bengio, Y. (2010). Understanding the Difficulty of Training Deep Feedforward Neural Networks. In Proceedings of the 13th International Conference on Artificial Intelligence and Statistics (AISTATS) (249–256).

Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep learning. MIT Press.

Hochreiter, S., and Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780.

Jiang, Z., and Liang, J. (2018). Cryptocurrency Portfolio Management with Deep Reinforcement Learning. IEEE Intelligent Systems, 33(3), 26–34.

Kingma, D. P., and Ba, J. (2015). Adam: A Method for Stochastic Optimization. In Proceedings of the 3rd International Conference on Learning Representations (ICLR).

Lahmiri, A., and Bekiros, S. (2019). Cryptocurrency Forecasting with Deep Learning Chaotic Neural Networks. Chaos, Solitons & Fractals, 118, 35–40.

LeCun, Y., Bengio, Y., and Hinton, G. (2015). Deep Learning. Nature, 521(7553), 436–444.

Livieris, I. E., Pintelas, E., and Pintelas, P. (2020). A CNN–LSTM Model for Cryptocurrency Price Prediction. Applied Sciences, 10(11), 1–13.

Mallqui, R., and Fernandes, R. (2019). Predicting the Direction, Maximum, Minimum and Closing Prices of Daily Bitcoin Exchange Rate Using Machine Learning Techniques. Applied Soft Computing, 75, 596–606.

McNally, A., Roche, S., and Caton, S. (2018). Predicting the Price of Bitcoin Using Machine Learning. In 2018 IEEE International Conference on Data Science and Advanced Analytics (DSAA) (551–556).

McNally, S., Roche, J., and Caton, S. (2018). Predicting Bitcoin Price Using LSTM. In 2018 IEEE International Conference on Big Data (1–9).

Mudassir, M., Bennbaia, S., Unal, D., and Hammoudeh, M. (2021). Time-Series Forecasting of Bitcoin Prices Using High-Dimensional Features. Neural Computing and Applications, 33, 287–304.

Nakamoto, S. (2008). Bitcoin: A Peer-To-Peer Electronic Cash System. https://bitcoin.org/bitcoin.pdf

Patel, H., Tanwar, S., Gupta, R., and Kumar, N. (2019). Blockchain-based smart contracts for secure financial transactions. IEEE Access, 7, 110721–110733.

Ron, D., and Shamir, A. (2013). Quantitative Analysis of the Full Bitcoin Transaction Graph. In Financial Cryptography and Data Security (6–24).

Valencia, F., Gómez, A., and Gómez, J. (2019). Bitcoin Price Prediction Using Machine Learning Techniques. IEEE Latin America Transactions, 17(11), 1866–1873.

Wood, G. (2014). Ethereum: A Secure Decentralised Generalised Transaction ledger. Ethereum Project Yellow Paper.