Mathematical Methods in Computer Science

A special issue of Axioms (ISSN 2075-1680). This special issue belongs to the section "Mathematical Analysis".

Deadline for manuscript submissions: 30 September 2024 | Viewed by 1376

Special Issue Editors

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Guest Editor
Department of Computer Systems and Computation, Universitat Politècnica de València, Valencia, Spain
Interests: artificial intelligence; computer vision; fuzzy logic; perceptual comparison

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Guest Editor
Department of Statistics, Computer Science and Mathematics, Universidad Pública de Navarra, Pamplona, Spain
Interests: artificial intelligence; computer vision; fuzzy logic
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Special Issue Information

Dear Colleagues,

We are pleased to announce a forthcoming Special Issue of Axioms dedicated to the exploration of "Mathematical Methods in Computer Science". In the ever-evolving landscape of computer science, mathematical techniques serve as the bedrock upon which innovation and progress are built. This Special Issue aims to showcase the pivotal role of mathematical methods in advancing the field, emphasizing the crossroads between rigorous mathematical theory and practical computer science applications.

This Special Issue will encompass a wide spectrum of topics, offering a complete view of mathematical methods and their transformative impact on various aspects of computer science as well as the development and application of mathematical principles in real-world scenarios. From fuzzy set theory to perceptual comparison and machine learning (including deep learning), this Special Issue showcases a variety of mathematical tools and techniques that empower researchers and practitioners to navigate the intricate landscape of modern computer science.

This Special Issue underscores not only theoretical advancements but also their practical implications. With a focus on interdisciplinary approaches, it encourages researchers to explore uncharted territories, tackling multifaceted challenges in fields such as computational biology, natural language processing, network analysis, image processing, robotics, federated learning,  and bioinformatics, among others.

Researchers are invited to submit their original research, theoretical developments, and practical applications in the intersection of mathematics and computer science. By fostering a multidisciplinary dialogue, this Special Issue aims to deepen our understanding of how mathematical methods continue to drive innovation and shape the future of computer science.

We look forward to receiving your insightful contributions.

Dr. Cedric Marco-Detchart
Dr. Carlos Lopez-Molina
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at 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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Axioms is an international peer-reviewed open access monthly 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 2400 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.


  • algebra
  • fuzzy set theory
  • applications
  • machine learning
  • data science
  • perceptual comparison
  • decision-making
  • aggregation theory

Published Papers (1 paper)

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16 pages, 1329 KiB  
Compact Data Learning for Machine Learning Classifications
by Song-Kyoo (Amang) Kim
Axioms 2024, 13(3), 137; - 21 Feb 2024
Cited by 1 | Viewed by 986
This paper targets the area of optimizing machine learning (ML) training data by constructing compact data. The methods of optimizing ML training have improved and become a part of artificial intelligence (AI) system development. Compact data learning (CDL) is an alternative practical framework [...] Read more.
This paper targets the area of optimizing machine learning (ML) training data by constructing compact data. The methods of optimizing ML training have improved and become a part of artificial intelligence (AI) system development. Compact data learning (CDL) is an alternative practical framework to optimize a classification system by reducing the size of the training dataset. CDL originated from compact data design, which provides the best assets without handling complex big data. CDL is a dedicated framework for improving the speed of the machine learning training phase without affecting the accuracy of the system. The performance of an ML-based arrhythmia detection system and its variants with CDL maintained the same statistical accuracy. ML training with CDL could be maximized by applying an 85% reduced input dataset, which indicated that a trained ML system could have the same statistical accuracy by only using 15% of the original training dataset. Full article
(This article belongs to the Special Issue Mathematical Methods in Computer Science)
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