Abstract Machine learning (ML) models have been quite successful in predicting outcomes
in many applications. However, in some cases, domain experts might have a judgment …

Current instance transfer learning (ITL) methodologies use domain adaptation and sub-
space transformation to achieve successful transfer learning. However, these …

The increasing scale of the network and the demand for data privacy‐preserving have
brought several challenges for existing intrusion detection schemes, which presents three …

Recent years, domain adaptation has attracted much attention in the community of machine
learning. In this paper, we mainly focus on the tasks of Joint Domain Matching and …

Soybean is important for protein and oil worldwide, requiring investments in research and
production technology, mainly breeding programs, to meet the increasing demand. Seed …

The objective of this study is to evaluate whether the number of requisite training samples
can be reduced with the use of various transfer learning models for predicting, for example …

In the presented study, the problem of interactive feature extraction, ie, supported by
interaction with users, is discussed, and several innovative approaches to automating …

The objective is to improve the learning of a regression model of the hydrocracking process
using a reduced number of observations. When a new catalyst is used for the hydrocracking …

This article presents the development and preliminary evaluation of an empathy controlled
robot. Such a robot is one step forward towards industry 5.0, as it provides a theoretical …

In a variety of real-world scenarios, techniques such as machine learning and data mining
are applied. Traditional machine learning frameworks suppose that training data and testing …