Large Language Models (LLMs) went from non-existent to ubiquitous in the machine
learning discourse within a few years. Due to the fast pace of the field, it is difficult to identify …

Infinite numbers of real-world applications use Machine Learning (ML) techniques to
develop potentially the best data available for the users. Transfer learning (TL), one of the …

Abstract Machine-learning models for medical tasks can match or surpass the performance
of clinical experts. However, in settings differing from those of the training dataset, the …

The traditional deep learning-based bearing fault diagnosis approaches assume that the
training and test data follow the same distribution. This assumption, however, is not always …

Smart buildings play a crucial role toward decarbonizing society, as globally buildings emit
about one-third of greenhouse gases. In the last few years, machine learning has achieved …

Transfer learning (TL) has been successfully applied to many real-world problems that
traditional machine learning (ML) cannot handle, such as image processing, speech …

Accurate remaining useful life (RUL) prediction for rolling bearings encounters many
challenges such as complex degradation processes, varying working conditions, and …

Transfer learning aims at improving the performance of target learners on target domains by
transferring the knowledge contained in different but related source domains. In this way, the …

Multi-Task Learning has emerged as a methodology in which multiple tasks are jointly
learned by a shared learning algorithm, such as a DNN. MTL is based on the assumption …

Smart cities attempt to reach net-zero emissions goals by reducing wasted energy while
improving grid stability and meeting service demand. This is possible by adopting next …