Deep learning has emerged as a potential tool for crop yield prediction, allowing the model
to automatically extract features and learn from the datasets. Meanwhile, smart farming …

Advancements in structural health monitoring (SHM) techniques have spiked in the past few
decades due to the rapid evolution of novel sensing and data transfer technologies. This …

With the wide deployments of heterogeneous networks, huge amounts of data with
characteristics of high volume, high variety, high velocity, and high veracity are generated …

Artificial Intelligence techniques powered by deep neural nets have achieved much success
in several application domains, most significantly and notably in the Computer Vision …

Multi-modal fusion is a basic task of autonomous driving system perception, which has
attracted many scholars' attention in recent years. The current multi-modal fusion methods …

The large number of spectral bands in hyperspectral images provides abundant information
to distinguish different land covers. However, these spectral bands have much redundancy …

In this paper, we present an abstract model of visualization and inference processes, and
describe an information-theoretic measure for optimizing such processes. In order to obtain …

Multi‐modal data of the complex human anatomy contain a wealth of information. To
visualize and explore such data, techniques for emphasizing important structures and …

In multimodal land cover classification (MLCC), a common challenge is the redundancy in
data distribution, where task-irrelevant information from multiple modalities can hinder the …

The multimodal emotion recognition in conversation task aims to predict the emotion label
for a given utterance with its context and multiple modalities. Existing approaches achieve …