Mathematical Foundations and Advances in Machine Learning and Data Mining
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".
Deadline for manuscript submissions: 31 March 2025 | Viewed by 89
Special Issue Editor
Special Issue Information
Dear Colleagues,
We are pleased to invite contributions to the Special Issue "Mathematical Foundations and Advances in Machine Learning and Data Mining" in Mathematics. Machine learning and data mining are critical areas in modern computational science, offering powerful tools for extracting knowledge from large and complex datasets. These techniques have transformed various domains, including healthcare, finance, and social sciences, by enabling predictive modeling, anomaly detection, and decision-making automation. The integration of mathematical theories with practical applications in these fields highlights their importance and the need for continued research and innovation.
This Special Issue aims to bring together recent advances and applications of mathematical foundations in machine learning and data mining. It aligns with the journal's scope by focusing on the development and analysis of mathematical models and algorithms that underpin these technologies. We seek to explore both theoretical advancements and practical implementations, fostering a comprehensive understanding of how mathematical principles drive progress in this dynamic field.
In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following: mathematical foundations of machine learning; algorithm development and optimization; statistical methods in data mining; big data analytics; advances in explainable AI; applications of machine learning in various domains (e.g., healthcare, finance, and engineering); neural networks and deep learning; pattern recognition; data preprocessing and feature selection; clustering and classification techniques; and predictive modeling and analytics.
I look forward to receiving valuable contributions.
Prof. Dr. Kan Li
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 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. 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
- deep learning
- machine learning
- data mining
- explainable AI
- pattern recognition
- big data
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