Small data are often used in scientific and engineering research due to the presence of
various constraints, such as time, cost, ethics, privacy, security, and technical limitations in …

Feature selection has become an indispensable machine learning process for data
preprocessing due to the ever-increasing sizes in actual data. There have been many …

Abstract Machine learning has been the corner stone in analysing and extracting information
from data and often a problem of missing values is encountered. Missing values occur …

The main objective of this study is to evaluate and compare the performance of different
machine learning (ML) algorithms, namely, Artificial Neural Network (ANN), Extreme …

The Internet of Things (IoT) will be ripe for the deployment of novel machine learning
algorithm for both network and application management. However, given the presence of …

Digital twin can be defined as a virtual representation of a physical asset enabled through
data and simulators for real-time prediction, optimization, monitoring, controlling, and …

The demand for artificial intelligence has grown significantly over the past decade, and this
growth has been fueled by advances in machine learning techniques and the ability to …

Feature Selection and classification have previously been widely applied in various areas
like business, medical and media fields. High dimensionality in datasets is one of the main …

Data fusion is a prevalent way to deal with imperfect raw data for capturing reliable, valuable
and accurate information. Comparing with a range of classical probabilistic data fusion …

In recent years, Earth system sciences are urgently calling for innovation on improving
accuracy, enhancing model intelligence level, scaling up operation, and reducing costs in …