Description: Multimodal data processing refers to the integration and analysis of data from multiple sources or modalities, such as text, images, audio, and video. This approach allows for a richer and more comprehensive understanding of information, as each modality provides different perspectives and details that can be crucial for analysis. For instance, in the field of artificial intelligence, multimodal data processing is used to enhance the accuracy of machine learning models by combining visual and textual data. Key features of this type of processing include the ability to merge heterogeneous data, the need for advanced preprocessing techniques to handle variability in data formats, and the implementation of algorithms that can learn from multiple types of information simultaneously. The relevance of multimodal data processing lies in its application across various fields, such as healthcare, where diverse data types can be combined for more accurate insights, or in sentiment analysis, where text and voice data are integrated to better understand user emotions. In summary, multimodal data processing is a powerful tool that enables researchers and professionals to extract value from the diversity of data available in today’s world.
