The early diagnosis and personalised treatment of diseases are facilitated by machine
learning. The quality of data has an impact on diagnosis because medical data are usually …

This study aimed to review the existing research focalizing on the missing data imputation
techniques for the systems enabling actual evapotranspiration calculation (such as eddy …

Classification of missing data based on estimation is still challenging since existing methods
relying on one imputation strategy fail to consider the diversity of different attribute …

Over the past decade, the Internet of Thing (IoT) devices have been deployed in wide-scale
several applications to collect vast amount of data from different locations in a time-series …

An emergency information response system (EIRS) is a system that utilizes various
intelligence technologies to effectively handle various emergencies and provide decision …

For incomplete data classification, missing attribute values are often estimated by imputation
methods before building classifiers. The estimated attribute values are not actual attribute …

Addressing missing values is a persistent challenge in the field of data mining. The
presence of incomplete data can significantly compromise the overall data quality …

Over the past decade, the Internet of Thing (IoT) devices have been deployed in wide-scale
several applications to collect vast amount of data from different locations in a time-series …

Quality of data is the primary factor that affects the level of insights that can be fetched from
data. One of the elements that significantly affect data quality is missing values. In some …

In the clinical treatment of skin diseases and cancer, cryotherapy and immunotherapy offer
effective and minimally invasive alternatives. However, the complexity of patient response …