Missing data imputation is a technique to deal with incomplete datasets. Since many models
and algorithms cannot be applied to data containing missing values, a pre-processing step …

In real-world intelligent transportation systems, the spatiotemporal traffic data collected from
sensors often exhibit missing or corrupted data, significantly hindering the development of …

Carbon emissions are a significant factor contributing to global climate change, and their
characterization and prediction are of great significance for regional sustainable …

Processing time series with missing segments is a fundamental challenge that puts
obstacles to advanced analysis in various disciplines such as engineering, medicine, and …

Spatiotemporal traffic data is increasingly important in transportation services with the
development of intelligent transportation system (ITS). However, due to various …

Water level data obtained from telemetry stations typically contains large number of outliers.
Anomaly detection and a data imputation are necessary steps in a data monitoring system …

Generative adversarial networks (GANs) have been widely utilized in time series analysis
and modeling, wherein generators and discriminators interact to generate realistic data …

Medical time series have been widely employed for disease prediction. Missing data hinders
accurate prediction. While existing imputation methods partially solve the problem, there are …

Long term visual localization has to conquer the problem of matching images with dramatic
photometric changes caused by different seasons, natural and man-made illumination …

This research investigates the impact of artificial intelligence platforms on the engineering
design industry by analyzing the perceptions of color and views on artificial intelligence …