Missing value imputation (MVI) has been studied for several decades being the basic
solution method for incomplete dataset problems, specifically those where some data …

Recently, numerous studies have been conducted on Missing Value Imputation (MVI),
intending the primary solution scheme for the datasets containing one or more missing …

Liver cancer is the sixth most frequently diagnosed cancer and, particularly, Hepatocellular
Carcinoma (HCC) represents more than 90% of primary liver cancers. Clinicians assess …

Background: Recurrence is an important cornerstone in breast cancer behavior, intrinsically
related to mortality. In spite of its relevance, it is rarely recorded in the majority of breast …

The presence of missing values in real-world data is not only a prevalent problem but also
an inevitable one. Therefore, missing values should be handled carefully before the mining …

Multi-temporal deep learning approaches can make full use of crop growth patterns and
phenological characteristics, resulting in excellent crop classification performance in large …

The performance evaluation of imputation algorithms often involves the generation of
missing values. Missing values can be inserted in only one feature (univariate configuration) …

The knowledge discovery process is supported by data files information gathered from
collected data sets, which often contain errors in the form of missing values. Data imputation …

The presence of missing values in incomplete datasets increases the difficulty of data
mining. In this paper, we use the autoencoder (AE) to model the incomplete data for …

In almost all countries, precautionary measures are less expensive than medical treatment.
The early detection of any disease gives a patient better chances of successful treatment …