Clustering has primarily been used as an analytical technique to group unlabeled data for
extracting meaningful information. The fact that no clustering algorithm can solve all …

Multimodal visual information fusion aims to integrate the multi-sensor data into a single
image which contains more complementary information and less redundant features …

The importance of identifying plant diseases has risen recently due to the adverse effect they
have on agricultutal production. Plant diseases have been a big concern in agriculture, as …

Deep model fusion/merging is an emerging technique that merges the parameters or
predictions of multiple deep learning models into a single one. It combines the abilities of …

As cities continue to burgeon, Urban Computing emerges as a pivotal discipline for
sustainable development by harnessing the power of cross-domain data fusion from diverse …

Semi-supervised multi-modal learning has attracted much attention due to the expense and
scarcity of data labels, especially in disease diagnosis field. Most existing methods follow …

Background and objective COVID-19 outbreak has become one of the most challenging
problems for human being. It is a communicable disease caused by a new coronavirus …

Currently, most of the existing fusion methods ignore the rich multi-source information of
different types of sensor nodes in the underwater unknown environment, which makes it …

To tackle the challenge of vehicle re-identification (Re-ID) in complex lighting environments
and diverse scenes, multi-spectral sources like visible and infrared information are taken …

Social media allows the spread of vital information regarding crises and emergencies. Thus,
emergency management systems can benefit from social media because they can be used …