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The paper proposes a Regime-Aware Multi-Modal Learning (RAML) method for Bitcoin price direction prediction that adaptively fuses social sentiment and technical features based on market volatility. Evaluated on hourly data from July 2024 to September 2025, RAML achieves moderate improvements over static fusion baselines.
This paper explores the use of HyperBand tuning to obtain irregular learning curves in Artificial Neural Network (ANN) models for price prediction.
This paper uses a BERT-based large language model for sentiment analysis of Decentraland's Discord community to enhance MANA token price prediction, demonstrating that a multi-modal LSTM incorporating sentiment, trading volume, and market capitalization outperforms a price-only baseline.
A domain name valuation AI model using neural networks with sentence transformers and domain tokenization, providing instant price estimates across auction, marketplace, and brokerage channels.