Missing data is a problem often found in real-world datasets and it can degrade the
performance of most machine learning models. Several deep learning techniques have …

Heterogeneous tabular data are the most commonly used form of data and are essential for
numerous critical and computationally demanding applications. On homogeneous datasets …

Desde la llegada de internet a España en los años 90, las tecnologías digitales han
demostrado su multitud y diversidad de aplicaciones en diversos campos, entre ellos en …

Humanity's quest for safe, resilient, and liveable cities has prompted research into the
application of computational tools in the design and development of sustainable smart cities …

Studies on missing data have increased in the past few decades. It is an uncontrollable
phenomenon and could occur during the data collection in practically any research field …

Tooth segmentation, as one of the key techniques of medical image segmentation, can be
widely applied to various medical applications, eg, orthodontic treatment, corpse …

Missing data is a major problem in real-world datasets, which hinders the performance of
data analytics. Conventional data imputation schemes such as univariate single imputation …

To train a neural network with an incomplete dataset containing missing values, the dataset
is required to be completed in advance. The conventional approach applies missing value …

Data imputation aims to solve missing values problem which is common in nowadays
applications. Many techniques have been proposed to solve this problem from statistical …

Medical questionnaires are a valuable source of information but are often difficult to analyse
due to both their size and the high possibility of them having missing values. This is a …