Autoencoders have become a hot researched topic in unsupervised learning due to their
ability to learn data features and act as a dimensionality reduction method. With rapid …

The convergence of ensemble deep learning and machine learning has become a critical
strategy for tackling intricate challenges across diverse fields such as healthcare, finance …

The integration of multisensory-based public subjective perception into planning,
management, and policymaking is of great significance for the sustainable development and …

Background Maintaining machines effectively continues to be a challenge for industrial
organisations, which frequently employ reactive or premeditated methods. Recent research …

Deep learning (DL) models hold great promise in enhancing the decision-making abilities of
electricity market participants and system operators in the short term, as they excel at …

Early identification of respiratory irregularities is critical for improving lung health and
reducing global mortality rates. The analysis of respiratory sounds plays a significant role in …

Many noncoding RNAs (ncRNAs) have been identified, and many of them play vital roles in
various biological processes, including gene expression regulation, epigenetic regulation …

With the development of industrial information technology, deep learning (DL) has been
successfully applied in chemical process fault detection. However, the features of incipient …

In this paper, we propose long short term memory speech enhancement network (LSTMSE-
Net), an audio-visual speech enhancement (AVSE) method. This innovative method …

In the field of audiology, achieving accurate discrimination of auditory impairments remains
a formidable challenge. Conditions such as deafness and tinnitus exert a substantial impact …