In the era of big data, there has been a surge in the availability of data containing rich spatial
and temporal information, offering valuable insights into dynamic systems and processes for …

This article proposes a knowledge-and data-driven graph neural network-based
collaboration learning model for reliable aircraft recognition in a heterogeneous radar …

Recently, many deep-learning techniques have been applied to various weather-related
prediction tasks, including precipitation nowcasting (ie, predicting precipitation levels and …

Mobile sensors are increasingly used to monitor air quality to accurately quantify human
exposure to air pollution. These sensors are subject to various issues (misuse, malfunctions …

The problem of processing very long time-series data (eg, a length of more than 10,000) is a
long-standing research problem in machine learning. Recently, one breakthrough, called …

Mobile sensors are being widely used to monitor air quality to quantify human exposure to
air pollution. These sensors are prone to malfunctions, resulting in many data quality issues …

Recently, precipitation nowcasting has gained significant attention. For instance, the
demand for precise precipitation nowcasting is significantly increasing in South Korea since …

While regulatory compliance data has been governed in the financial services industry for a
long time to identify, assess, remediate and prevent risks, improving data governance (“DG”) …

This paper explores the potential applicability of a previously proposed data quality model
and metrics applied to the context of dronebased waste detection. The proposed framework …