  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
