A bstract We deploy an advanced Machine Learning (ML) environment, leveraging a multi-
scale cross-attention encoder for event classification, towards the identification of the gg→ …

Object detection in remotely sensed satellite pictures is fundamental in many fields such as
biophysical and environmental monitoring. While deep learning algorithms are constantly …

Advances in artificial intelligence, specifically convolutional neural networks (CNNs), have
significantly enhanced the ability to analyze and interpret consumer behaviors to digital …

There has been growing scientific interest in the research field of deep learning techniques
applied to skin cancer diagnosis in the last decade. Though encouraging data have been …

High-mountain water bodies represent critical components of their ecosystems, serving as
vital freshwater reservoirs, environmental regulators, and sentinels of climate change. To …

The majority of research on the Aspect-Based Sentiment Analysis (ABSA) tends to split this
task into two subtasks: one for extracting aspects, Aspect Term Extraction (ATE), and another …

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 …

Land cover mapping is an essential procedure that yields extremely helpful data for many
enterprises, including land supply, spatial planning, disaster assistance, and agricultural …

The prevalence of fatty liver disease is on the rise, posing a significant global health
concern. If left untreated, it can progress into more serious liver diseases. Therefore …

The precise monitoring of chemical reactions in plasma-based processes is crucial for
advanced semiconductor manufacturing. This study integrates three diagnostic techniques …