Multimodal Learning Analysis via Machine Learning and Deep Learning Methodologies

Abstract

The world we live in is purely multimodal in nature. We can see objects, hear sounds, smell different sorts of scents, feel things, and taste various flavors. The word ‘Modality’ refers to something that occurs or something that can be experienced. An experience is presented as multimodal only when it includes some of the modalities found in the world. In order to gain recognition in understanding our general surroundings, artificial intelligence should be able to understand such multimodal perspectives. On the other hand, multimodal machine learning refers to the development of models that can interact and correlate data from various modalities. It’s a dynamic and multi-disciplinary area of expanding significance with exceptional perspective. Rather than focusing on some limited multimodal applications, we, in this paper review the new technological developments in multimodal machine learning and present them in a typical scientific categorization. We move past the usual classifications and discuss more extensive opportunities and challenges presented by multimodal machine learning. Most of the studies conducted on multimodal learning methodologies utilize polls and surveys as their primary source of collecting quantitative data. This paper discusses the results of precise literature of observational studies on the skills of multimodal data (MMD) for human learning with the help of artificial intelligence-empowered methodologies i.e. Machine Learning and Deep Learning. This paper also gives an outline of what and in what manner MMD has been leveraged to strengthen learning and in what environmental settings. The discussion of this paper portrays the abilities of multimodal learning and the continuous advances and methods that rise out of the work of multimodal learning to enhance and further elevate the learning process. At last, we conclude that future researchers should thoroughly consider developing a system that would empower multimodal features to be lined up with the ongoing research and learning plan. These features could likewise be used on enabling theory and practice activities to further elevate the multimodal learning process. This paper sets a clear pattern to enable the adoption of multimodal data inside future learning technologies and development endeavors.