This paper explores the employment of artificial intelligence and machine learning to
decipher strategic responses to incidences of climate change and to inform the management …

The global demand for energy has been steadily increasing due to population growth,
urbanization, and industrialization. Numerous researchers worldwide are striving to create …

This study employs a data-driven Fault Detection and Isolation (FDI) methodology in
Floating Offshore Wind Turbine (FOWT) farms. The main objective of the work lies in …

Small objects detection is a challenging task in computer vision due to the limited semantic
information that can be extracted and the susceptibility to background interference. In this …

Multi-fidelity surrogate (MFS) modeling technology, which efficiently constructs surrogate
models using low-fidelity (LF) and high-fidelity (HF) data, has been studied to enhance the …

Surface visibility (SV), a key indicator of atmospheric transparency, is used widely in the
fields of environmental monitoring, transportation, and aviation. However, the sparse …

Deep learning (DL) systems have been widely utilized across various domains. However,
the evolution of DL systems can result in regression faults. In addition to the evolution of DL …

Humans are distinguished by their walking patterns; many approaches, including using
various types of sensors, have been used to establish walking patterns as biometrics. By …

Idealised as universal approximators, learners such as neural networks can be viewed as"
variable functions" that may become one of a range of concrete functions after training. In the …

Learning the representation of sentences is fundamental work in the field of Natural
Language Processing. Although BERT-like transformers have achieved new SOTAs for …