The occurrence of missing values in time series is a common phenomenon attributed to
equipment malfunction during data acquisition and transmission errors. However, most …

Real-world datasets often have missing values, which hinders the use of a large number of
machine learning (ML) estimators. To overcome this limitation in a data analysis pipeline …

Image inpainting is one of the important tasks in computer vision which focuses on the
reconstruction of missing regions in an image. The aim of this paper is to introduce an image …

In order to solve low-quality problems such as data anomalies and missing data in the
condition monitoring data of hydropower units, this paper proposes a monitoring data quality …

For effective human-robot teaming, it is important for the robots to be able to share their
visual perception with the human operators. In a harsh remote collaboration setting, data …

Modern scientific research and applications very often encounter 'fragmentary data'which
brings big challenges to imputation and prediction. By leveraging the structure of response …

Imputation for missing values is a key operation in building data analysis models. In this
paper, we target numerical and categorical values in tabular data. While previous studies …

In the contemporary realm of solar energy research, accurately predicting Solar Irradiance
(SI) is critical for optimizing photovoltaic (PV) installations. This research delves into the …

Transmission scheduling is a fundamental energy-saving problem in many wireless sensor
networks (WSN), especially for mHealth systems with multiple distributed sensing …

Many studies have attempted to solve the problem of missing data using various
approaches. Among them, Generative Adversarial Imputation Nets (GAIN) was first used to …