Multi-Modal Deep Learning Architectures for Integrating Text, Image, and Sensor Data in Intelligent Systems

Abstract

The fast pace of artificial intelligence and deep learning has also increased the rate at which intelligent systems whose ability to process heterogeneous data across various modalities are developed. Traditional learning strategies that focus on single mode tend to have less contextual knowledge and predictive validity because they cannot take advantage of complementary information in a variety of data sources. This research paper suggests a multi-modal deep learning model to join text, image and sensor information into intelligent systems. The suggested framework integrates the transformer-based learning of textual representations, convolutional neural networks-driven visual features with long short-term memory-based analysis of temporal sensors in a coherent fusion framework. A hybrid fusion mechanism with attention is presented to enhance learning cross-modal representations to enable the greater use of contexts when making decisions. Benchmark multimodal datasets of textual descriptions, samples of images and real-time sensor measurements were used in experimental evaluation. Its proposed architecture was able to outperform other unimodal and traditional multimodal methods, with an accuracy of 96.8, a precision of 96.2, a recall of 95.9 and a F1-score of 96.0. The generalization capability and strength in repeated experiments were statistically verified by the use of 10-fold cross-validation. Moreover, the framework had lower inference latency and reasonable computational performance to run in real time. The suggested system has a lot of potential in the health care monitoring, intelligent surveillance, industrial automation, autonomous systems, and human, machine interaction setting where there is need to have solid heterogeneous data assemblages.