Applied Machine Learning and Soft Computing
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".
Deadline for manuscript submissions: 31 May 2026 | Viewed by 197
Special Issue Editor
Special Issue Information
Dear Colleagues,
Nowadays, machine learning (ML) has been an integral part of scientific and technological progress. Various ML methods have the potential to offer tools for learning, knowledge discovery, and decision-making that can outperform human abilities and can be used in a large number of application domains. Especially, applied machine learning (AML) focuses on implementing machine learning algorithms to solve practical engineering challenges whose key characteristics include domain-specific applications, engineering integration, and trend evolution. Soft computing (SC), closely related to ML, represents computational methods that tolerate uncertainty and imprecision to achieve robust, low-cost solutions. Then, integration of AML and SC drives cutting-edge research such as enhanced robustness, explainable AI, and efficiency optimization. Novel machine learning methods integrated with intelligent optimization and various soft computing techniques, hybrid and deep learning methods, and ensemble techniques are quickly emerging and deliver models with higher accuracy.
As a response to the recent advancements, the objective of this Special Issue is to present a collection of notable methods and applications of recent theoretical and computational studies on AML and SC. We invite scientists from all around the world to contribute to developing a comprehensive collection of papers on the progressive and high impact of AML and SC.
Prof. Dr. Yeming Dai
Guest Editor
Manuscript Submission Information
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Keywords
- machine learning
- predictive models and analytics
- prescriptive analytics
- deep learning
- deep reinforcement learning
- hybrid models
- ensemble models
- soft computing
- machine learning for big data
- business intelligence
- intelligent optimization
- data-driven models
- feature selection
- preprocessing methods
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