Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.6
2.2
Journals
Mathematics
Special Issues
Emerging Mathematical Methods in Data Science: Theory, Algorithms, and Applications
share announcement

Emerging Mathematical Methods in Data Science: Theory, Algorithms, and Applications

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E: Applied Mathematics".

Deadline for manuscript submissions: 31 December 2026 | Viewed by 775

Special Issue Editor

Dr. Célio Fernandes

E-Mail Website
Guest Editor
Center for Studies of Transport Phenomena (CEFT), Department of Mechanical Engineering, Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal
Interests: polymer processing; viscoelasticity; particle-laden flows; thermo-fluids; computational rheology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, the mathematical foundations of data science have undergone a rapid evolution, driven by demands for scalable algorithms, rigorous uncertainty quantification, and robust inference in high-dimensional settings.

This Special Issue seeks original research and survey articles that advance the mathematical underpinnings of data science. We invite contributions on the following topics: 

- Algorithmic innovations in randomized algorithms for matrices and tensors and optimization methods for large-scale learning. 

- Uncertainty quantification frameworks for data-driven models, including Bayesian methods and robust statistics. 

- Topological and geometric data analysis, covering persistent homology, manifold learning, and graph-based techniques. 

- Applications that demonstrate the impact of these methods in science, engineering, and healthcare.

Dr. Célio Fernandes
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • randomized algorithms
  • statistical learning theory
  • optimization
  • topological data analysis
  • uncertainty quantification

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

20 pages, 6748 KB  
Article
Hybrid Dual Volume Learning for Iterative Fusion and Adaptive Depth Refinement for Shape from Focus
by Khurram Ashfaq and Muhammad Tariq Mahmood
Mathematics 2026, 14(10), 1619; https://doi.org/10.3390/math14101619 - 10 May 2026
Viewed by 252
Abstract
Shape from Focus (SFF) estimates scene depth by analyzing focus variations across a sequence of images captured at different focal settings. Traditional SFF methods rely on handcrafted focus operators that preserve local structural details, but they are often sensitive to noise and perform [...] Read more.
Shape from Focus (SFF) estimates scene depth by analyzing focus variations across a sequence of images captured at different focal settings. Traditional SFF methods rely on handcrafted focus operators that preserve local structural details, but they are often sensitive to noise and perform poorly in textureless regions. In contrast, deep learning-based methods are more robust and can exploit semantic and contextual cues, yet they may lose fine structural information due to feature abstraction and spatial downsampling. To address these complementary limitations, we propose a dual-branch SFF framework that integrates deep and traditional focus cues within a unified architecture. The first branch generates a deep focus volume using a multi-scale encoder-decoder network, while the second branch computes a traditional focus volume using a directional dilated Laplacian (DDL) operator to capture structural focus responses. These two volumes are progressively combined through an iterative gated fusion module, producing a more discriminative fused focus representation. From this fused volume, an initial depth map is estimated through a softmax-based slice aggregation strategy. To further improve spatial consistency and reduce residual artifacts, we introduce a lightweight depth refinement module guided by the mean RGB image of the focal stack. This refinement stage enhances boundary quality and improves the overall depth structure. Extensive experiments on synthetic and real-world datasets demonstrate that the proposed framework produces accurate and reliable depth maps. Full article
(This article belongs to the Special Issue Emerging Mathematical Methods in Data Science: Theory, Algorithms, and Applications)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop