Astounding results from Transformer models on natural language tasks have intrigued the
vision community to study their application to computer vision problems. Among their salient …

Multimodal medical data fusion has emerged as a transformative approach in smart
healthcare, enabling a comprehensive understanding of patient health and personalized …

Data scarcity is a major challenge when training deep learning (DL) models. DL demands a
large amount of data to achieve exceptional performance. Unfortunately, many applications …

Abstract Human Activity Recognition (HAR) has become a crucial element for smart
healthcare applications due to the fast adoption of wearable sensors and mobile …

The advances of intelligent fault diagnosis in recent years show that deep learning has
strong capability of automatic feature extraction and accurate identification for fault signals …

The performance of deep learning models in NLP and other fields of machine learning has
led to a rise in their popularity, and so the need for explanations of these models becomes …

Intelligent fault diagnosis based on deep learning has yielded remarkable progress for its
strong feature representation capability in recent years. Nevertheless, in engineering …

Defects on the surface of yarn-dyed fabrics are one of the important factors affecting the
quality of fabrics. Defect detection is the core link of quality control. Due to the diversity of …

Remaining useful life (RUL) prediction has always been a core task of prognostics and
health management technology, which is crucial to the reliable and safe operation of …

What will the future be? We wonder! In this survey, we explore the gap between current
research in egocentric vision and the ever-anticipated future, where wearable computing …