Machine Learning Methods and Mathematical Modeling with Applications
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Engineering Mathematics".
Deadline for manuscript submissions: 31 March 2025 | Viewed by 74
Special Issue Editors
Interests: machine learning methods with applications to operations management; energy forecasting; financial risk assessment and other fields; forecasting theories and methods; nonlinear optimization; data mining and artificial intelligence
Interests: machine learning; optimization theory; healthcare
Special Issue Information
Dear Colleagues,
Machine learning methods (including support vector machine, deep learning and ensemble learning) and mathematical modeling have attracted much attention in recent years. In particular, many machine learning models are formulated as nonlinear optimization models, and mathematical modeling methods have employed machine learning to gain outstanding results. For handling large-scaled real-world data, it is also necessary to develop optimization algorithms for implementing well-known and emerging machine learning methods. Moreover, machine learning methods and mathematical modeling exhibit impressive performances in various real-world applications, including demand and price forecasting, electric load forecasting, scheduling optimization for emergency materials, etc. To this end, this Special Issue focuses on the application of current advances in machine learning and optimization methods for real-world problems, especially for industrial engineering and management science. This Special Issue will provide a platform for researchers from academia and industry to present their novel and unpublished work in the domain of machine learning and mathematical modeling, allowing us to foster future interesting research in related emerging fields.
Prof. Dr. Jian Luo
Dr. Zheming Gao
Dr. Xin Yan
Guest Editors
Manuscript Submission Information
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Keywords
- machine learning
- mathematical modeling
- industrial engineering
- forecasting methods
- management science
