Special Issues
Interest-related subjects include, but are not limited to:
Optimization in machine learning algorithms;
Inverse problems;
Parameter and Hyperparameter estimation;
Low-rank methods for data analysis;
Image reconstruction;
Image processing;
Regularization and penalization methods and their applications;
Algorithms for feature extraction and selection from real data.
Journal of Computational Mathematics and Data Science (ISSN: 2772-4158)
Special Issue: "Learning from data: from Optimization to Inverse problems"
Over the past decade, machine learning has emerged as a major driving force in the growth of innovation. With the increasing amount of data generated, the research community in this field has expanded rapidly and achieved significant progress in processing and analyzing data. This development has enabled the creation of interpretative models for learning from data that are precise and reliable as well as more effective. The rapid development of learning approaches naturally flows in the requirement for a mathematical formalization and computational analysis of the related issues. These cover several fields of mathematics, from optimization to inverse problems, encountering several open questions in many real applications. Regarding learning approaches, the special issue aims to examine the most recent developments in mathematical and computational techniques for practical applications. It also examines the most recent and cutting-edge algorithms for finding patterns, relationships, and correlations in sizable real-world datasets. The articles in this special issue will then get straight to the main problems with data preprocessing, analysis, and interpretation.
Guest Editors: Dr. Esposito Flavia, Dr. Laura Esposito
More information on web site.
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematical Biology".
Deadline for manuscript submissions: 31 October 2022
In recent decades, the rapid development of high-performance technologies has produced an explosive growth of digitized medical data, including radiology images, omics data, laboratory test results, and medical and personal statistics. These data need to be processed and studied to extract information which is useful to better understand mechanisms of pathogenesis of complex diseases and to potentially improve care and outcomes for patients based on predictive analytics. The analysis of biomedical data often requires the construction of unified frameworks using various machine learning, statistical techniques, mathematical and computational methods to provide insights into the biological task under study. The main aim of this Special Issue is to introduce and discuss major problems for the preprocessing, analysis, and interpretation of biomedical data, to review the state-of-the-art of mathematical and computational approaches for biomedical applications, and to explore current and emerging algorithms and techniques able to unravel patterns, associations and correlations in large amounts of biomedical datasets.
Keywords Topics of interest include but are not limited to:
Low-rank methods for biomedical data;
Feature extraction and selection algorithms in biomedical data;
Anomaly detection methods for biomedical applications;
Clustering algorithms applied to omics and biomedical data;
Bioimaging for omics data;
Optimization and machine learning algorithms for biomedical data.
More information on web site.