Artificial intelligence (AI) models are increasingly finding applications in the field of
medicine. Concerns have been raised about the explainability of the decisions that are …

In this article, we propose a unified framework to solve the following two challenging
problems in incomplete multi-view representation learning: i) how to learn a consistent …

With the advent of 5G commercialization, the need for more reliable, faster, and intelligent
telecommunication systems is envisaged for the next generation beyond 5G (B5G) radio …

Multiview clustering (MVC) aims to reveal the underlying structure of multiview data by
categorizing data samples into clusters. Deep learning-based methods exhibit strong feature …

Feature selection aims to remove irrelevant or redundant features and thereby remain
relevant or informative features so that it is often preferred for alleviating the dimensionality …

Commercial e-learning platforms have to overcome the challenge of resource overload and
find the most suitable material for educators using a recommendation system (RS) when an …

Artificial Intelligence (AI) has become an important component of many software
applications. It has reached a point where it can provide complex and critical decisions in …

Although explainable artificial intelligence (XAI) promises considerable progress in
glassboxing deep learning models, there are challenges in applying XAI to geospatial …

Multiple kernel clustering (MKC) is committed to achieving optimal information fusion from a
set of base kernels. Constructing precise and local kernel matrices is proven to be of vital …

This paper proposes to perform online clustering by conducting twin contrastive learning
(TCL) at the instance and cluster level. Specifically, we find that when the data is projected …