Deep Learning Algorithms and Architectures for Multimodal Data Analysis
Anwar Ali Sathio, Muhammad Malook Rind, Abdullah Raza Lakhan · 2023
This chapter comprehensively overviews algorithms and deep learning architectures for multimodal data. The multimodal data comprises various sources, text, images, audio, etc. Deep learning is an effective technique that can be used to analyze multimodal data for data classification and associative pattern recognition. It also automatically learns complex data for accuracy and prediction. This chapter begins with an introduction to deep learning and its applications in multimodal data analysis. We then cover various model architectures, CNNs, RNNs, and related variants architectures that have been discussed. Deep learning is particularly useful for multimodal data analysis, where various models can be applied to extract the features from different data modalities. The popular deep learning models, such as autoencoder, BRNN, DBN, GRU, LSTM, MLP, ResNet, RBM, and VGG(16/19), explained the understanding of the deep learning scope in the multimodal data analysis. Moreover, the chapter further presents the comparative analysis of deep learning research gaps for deep neural networks. We also discuss the pre-processing procedures in the multimodal data analysis and explain the PCA and ICA role in the deep learning analysis. We explained the training processing procedure, i.e., data pre-processing, selection of models, model architecture design, training, model evaluation, fine-tuning, and deployments, adopted in deep learning. We also added training strategies (joint, parallel, and serial), regularization techniques (dropouts, batch normalization), and hyperparameter tuning. We include the importance of emerging blockchain technology&s;s importance and its influential role in transactional multimodal data for security and analysis. The newly emerging field, cross-border blockchain technology frameworks, and their importance are explored with deep learning interoperability with different applications (e-commerce, supply chain, healthcare, finance, real estate, etc.). At the end of the chapter, we discuss the hands-on practice of the various deep learning model in Python to better understand the functionalities of the deep model with multimodal data, challenges, and future directions for deep learning in multimodal data analysis.