Deep learning has become a predominant method for solving data analysis problems in
virtually all fields of science and engineering. The increasing complexity and the large …

Transformer has been widely applied in the research of rotating machinery fault diagnosis
due to its ability to explore the internal correlation of vibration signals. However, challenges …

To enable researchers to fully trust the decisions made by deep diagnostic models,
interpretable rotating machinery fault diagnosis (RMFD) research has emerged. Existing …

Action anticipation aims to infer the action in the unobserved segment (future segment) with
the observed segment (past segment). Existing methods focus on learning key past …

Recently, artificial intelligence and data science have witnessed dramatic progress and
rapid growth, especially Automatic Speech Recognition (ASR) technology based on Hidden …

This study presents a novel deep-learning framework designed to efficiently generate high-
fidelity three-dimensional (3D) point clouds of rail fasteners. The proposed method …

In this paper, we present an integrated framework leveraging natural language processing
(NLP) techniques and machine learning (ML) algorithms to detect malware at its early stage …

In this pioneering research endeavor, we delved into the intricate realm of speech
recognition technology, aiming to surmount the formidable challenges posed by acoustically …

Despite demonstrating impressive capabilities, Large Language Models (LLMs) still often
struggle to accurately express the factual knowledge they possess, especially in cases …

Although deep learning-based methods have made remarkable achievements in
polarimetric synthetic aperture radar (PolSAR) image classification, these methods require a …