Deep Learning and Ensemble Models for Multi-Country Cryptocurrency Prediction and Automated Smart-Contract Trading

Author(s)	Harsha Neema, Arohi Patel, Hiral Patel
Country	India
Abstract	This study presents a unified framework that integrates deep learning architectures and ensemble machine learning models to enhance cryptocurrency price prediction across multiple countries and regulatory environments. Leveraging LSTM, GRU, hybrid recurrent networks, Random Forest, XGBoost, PCA, and Z-score anomaly detection, the system captures nonlinear market dynamics and cross-jurisdictional policy impacts. SHAP-based explainability improves transparency, while smart-contract automation enables real-time, trust-free trading execution via oracle-driven price updates. Regulatory ablation experiments reveal significant country-specific sensitivities, highlighting the importance of tailored policy design. The proposed approach demonstrates strong predictive accuracy and practical feasibility, offering a robust foundation for next-generation crypto-market intelligence.
Keywords	Cryptocurrency Forecasting, Deep Learning, Ensemble Models, Smart-Contract Trading, Explainable AI
Field	Engineering
Published In	Volume 6, Issue 11, November 2025
Published On	2025-11-26
Cite This	Deep Learning and Ensemble Models for Multi-Country Cryptocurrency Prediction and Automated Smart-Contract Trading - Harsha Neema, Arohi Patel, Hiral Patel - IJLRP Volume 6, Issue 11, November 2025.

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