The Internet of Things (IoT) is enabled by the latest developments in smart sensors,
communication technologies, and Internet protocols with broad applications. Collecting data …

Missing data is one of the most common issues encountered in data cleaning process
especially when dealing with medical dataset. A real collected dataset is prone to be …

In environmental research, missing data are often a challenge for statistical modeling. This
paper addressed some advanced techniques to deal with missing values in a data set …

Electronic health records (EHR) are patient-level information, eg, laboratory tests and
questionnaires, stored in electronic format. Compared to physical records, the EHR …

Efforts to create age-friendly cities progressively intersect with goals for environmental
sustainability. The older people's beliefs, behaviours and financial aspects regarding …

This study explored methods for handling missing data in the development of machine
learning-based energy benchmarking models, assessing their training time, performance …

Over the last decade, collecting massive volumes of data has been made all the more
accessible, pushing the building sector to embrace data mining as a powerful tool for …

The presence of missing values in a time-series dataset is a very common and well-known
problem. Various statistical and machine learning methods have been developed to …

Real medical datasets usually consist of missing data with different patterns which decrease
the performance of classifiers used in intelligent healthcare and disease diagnosis systems …

Cardiovascular disease (CVD) is a class of diseases that involve the heart or blood vessels
and according to World Health Organization is the leading cause of death worldwide. EHR …