Recent advancements in multi-modal artificial intelligence (AI) have revolutionized the fields of stock market forecasting and heart rate monitoring. Utilizing diverse data sources can substantially improve prediction accuracy. Nonetheless, additional data may not always align with the original dataset. Interpolation methods are commonly utilized for handling missing values in modal data, though they may exhibit limitations in the context of sparse information. Addressing this challenge, we propose a Modality Completion Deep Belief Network-Based Model (MC-DBN). This approach utilizes implicit features of complete data to compensate for gaps between itself and additional incomplete data. It ensures that the enhanced multi-modal data closely aligns with the dynamic nature of the real world to enhance the effectiveness of the model. We conduct evaluations of the MC-DBN model in two datasets from the stock market forecasting and heart rate monitoring domains. Comprehensive experiments showcase the model's capacity to bridge the semantic divide present in multi-modal data, subsequently enhancing its performance. The source code is available at: https://github.com/logan-0623/DBN-generate

翻译：近年来，多模态人工智能（AI）的进展彻底革新了股市预测和心率监测领域。利用多样化的数据源可显著提升预测精度。然而，额外数据可能与原始数据集不完全对齐。插值方法常用于处理模态数据中的缺失值，但在稀疏信息背景下可能存在局限性。针对这一挑战，我们提出了一种基于深度信念网络的模态补全模型（MC-DBN）。该方法利用完整数据的隐式特征来补偿其与额外不完整数据之间的差距，确保增强后的多模态数据紧密贴合真实世界的动态特性，从而提升模型效能。我们在股市预测和心率监测领域的两个数据集上对MC-DBN模型进行了评估。综合实验展示了该模型弥合多模态数据中语义鸿沟的能力，进而提升了其性能。源代码地址：https://github.com/logan-0623/DBN-generate