With the widespread deployment of sensors and the Internet-of-Things, multi-view data has
become more common and publicly available. Compared to traditional data that describes …

Feature selection is an important task in data mining and machine learning to reduce the
dimensionality of the data and increase the performance of an algorithm, such as a …

The advent of machine learning (ML) and Artificial intelligence (AI) has brought about a
significant transformation across multiple industries, as it has facilitated the automation of …

Gene expression data have become increasingly important in machine learning and
computational biology over the past few years. In the field of gene expression analysis …

One of the most critical challenges in managing complex diseases like COVID-19 is to
establish an intelligent triage system that can optimize the clinical decision-making at the …

In this work, we conducted a visualized bibliometric analysis to map the research trends of
machine learning in engineering (MLE) based on articles indexed in the Web of Science …

Subspace distance is an invaluable tool exploited in a wide range of feature selection
methods. The power of subspace distance is that it can identify a representative subspace …

In this paper, we propose a new optimization-based framework to reduce the dimensionality
of hyperspectral images. One of the most problems in hyperspectral image classification is …

With the rapid development of multimedia technology, massive unlabelled data with high
dimensionality need to be processed. As a means of dimensionality reduction, unsupervised …

This study reviews and analyses the research landscape for intrusion detection systems
(IDSs) based on deep learning (DL) techniques into a coherent taxonomy and identifies the …