Advanced Methods in Machine Learning and Statistics for Big Data in a Sustainable Society
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E: Applied Mathematics".
Deadline for manuscript submissions: 30 November 2025 | Viewed by 56
Special Issue Editor
Interests: machine learning; high dimensional regression analysis; penalized methods; censored data
Special Issue Information
Dear Colleagues,
The rapid expansion of big data in diverse domains, including healthcare, finance, engineering, and environmental sciences, has created an urgent demand for advanced machine learning and statistical techniques that can extract meaningful insights from complex, high-dimensional datasets. This Special Issue aims to bridge the gap between mathematical theory, statistical modeling, and cutting-edge machine learning algorithms to address key challenges in big data analytics.
We welcome original research and review articles focusing on innovative methodologies, theoretical advancements, and real-world applications of machine learning, deep learning, Bayesian inference, optimization techniques, and statistical modeling. Topics of interest include, but are not limited to, the following:
- Mathematical foundations of machine learning.
- Scalable learning algorithms for high-dimensional data.
- Bayesian and probabilistic approaches in machine learning.
- Optimization techniques for large-scale machine learning.
- Interpretable and explainable AI in big data.
- Applications in healthcare, economics, agriculture, and more.
This Special Issue seeks to highlight interdisciplinary approaches that integrate applied mathematics, statistics, and machine learning to solve complex real-world problems. We invite researchers, academicians, and industry experts to contribute their latest findings and insights.
Dr. Reza Arabi Belaghi
Guest Editor
Manuscript Submission Information
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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
- machine learning
- big data analytics
- statistical modeling
- deep learning
- bayesian inference
- optimization algorithms
- explainable AI
- high-dimensional data
- computational mathematics for machine learning
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