Small data are often used in scientific and engineering research due to the presence of
various constraints, such as time, cost, ethics, privacy, security, and technical limitations in …

Multimodal machine learning is a vibrant multi-disciplinary research field that aims to design
computer agents with intelligent capabilities such as understanding, reasoning, and learning …

This paper presents a systematic review of studies related to artificial intelligence (AI)
applications in supply chain management (SCM). Our systematic search of the related …

Multimodal machine learning is a vibrant multi-disciplinary research field that aims to design
computer agents with intelligent capabilities such as understanding, reasoning, and learning …

View missing and label missing are two challenging problems in the applications of multi-
view multi-label classification scenery. In the past years, many efforts have been made to …

Machine learning develops rapidly, which has made many theoretical breakthroughs and is
widely applied in various fields. Optimization, as an important part of machine learning, has …

Multimodal deep learning systems that employ multiple modalities like text, image, audio,
video, etc., are showing better performance than individual modalities (ie, unimodal) …

Semi-supervised learning is becoming increasingly important because it can combine data
carefully labeled by humans with abundant unlabeled data to train deep neural networks …

Cloud computing has rapidly emerged as a model for delivering Internet-based utility
computing services. Infrastructure as a Service (IaaS) is one of the most important and …

In recent years, multi-view multi-label learning has aroused extensive research enthusiasm.
However, multi-view multi-label data in the real world is commonly incomplete due to the …