Mathematical Foundations and Practical Applications of Data Mining and Deep Learning
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".
Deadline for manuscript submissions: 20 January 2026 | Viewed by 24
Special Issue Editor
Interests: data mining for industry applications; deep learning for smart manufacturing and automation; AI-driven decision support systems; industrial IoT and oredictive analytics; optimization and machine learning in engineering
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The rapid advancement of Data Mining and Deep Learning, grounded in mathematical modeling and computational techniques, is transforming industries by enabling intelligent automation, predictive analytics, and data-driven decision-making. Fundamental areas such as optimization, probabilistic modeling, matrix operations, and graph theory play crucial roles in enhancing the efficiency and interpretability of AI-driven solutions.
This Special Issue will explore both the mathematical foundations and practical applications of Data Mining and Deep Learning across various industrial domains, including manufacturing, healthcare, finance, energy, logistics, and cybersecurity.
We invite high-quality research and review articles that address the following topics:
- Mathematical Foundations in Smart Industry and Automation: Optimization algorithms and mathematical modeling for predictive maintenance, digital twins, and process optimization;
- Computational Methods in AI for Healthcare: Probabilistic modeling and statistical learning for medical imaging, disease prediction, and AI-assisted diagnostics;
- Mathematical Finance and Business Analytics: Risk assessment, fraud detection, and market analysis using optimization techniques and time-series modeling;
- Mathematical Modeling for Energy and Sustainability: Smart grid optimization and renewable energy forecasting using graph theory and statistical methods;
- Optimization and AI in Logistics and Supply Chain: Route optimization, demand forecasting, and autonomous transportation through reinforcement learning and mathematical programming;
- Mathematical Approaches to Cybersecurity and Privacy: AI-driven anomaly detection, cryptanalysis, and privacy-preserving Data Mining with advanced statistical frameworks.
This Special Issue will provide insights into the latest techniques, challenges, and opportunities related to applying AI-driven Data Mining and Deep Learning solutions in industry. We encourage researchers, engineers, and practitioners to contribute their expertise and findings.
Dr. Sekyoung Youm
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
- mathematical foundations of data mining and deep learning
- optimization and statistical learning for AI-driven decision support
- probabilistic models and computational methods in AI
- graph theory and matrix computations for smart systems
- mathematical modeling for automation and process optimization
- cryptanalysis, privacy-preserving AI, and secure machine learning
- industrial applications of AI in manufacturing, healthcare, and finance
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