Recent Advances in Machine Learning and Industrial Big Data Analysis
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Industrial Technologies".
Deadline for manuscript submissions: closed (30 August 2023) | Viewed by 4103
Special Issue Editor
Interests: evolutionary algorithms; multi-objective optimization; evolutionary learning; evolutionary computing; computational intelligence optimization; engineering optimization technologies
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In recent years, with the rapid development of the Industrial Internet, many industrial enterprises have realized real-time sensing and integration of operation data, management data, production data, and process data, thus providing a more solid database and potential application scenarios for the industrial big data analysis methods, and also promoting the rapid development and industrial application of various machine learning methods such as ensemble learning and deep learning. In particular, automatic machine learning methods based on evolutionary optimization, such as neural architecture search (NAS) based on evolutionary computation, have received more attention. Therefore, this Special Issue will focus on the latest advances in theoretical methods and applications of machine learning and industrial big data analysis in recent years. The contents covered in this Special Issue include but are not limited to:
- SVM, random forests, ensemble learning methods and applications;
- Deep learning methods and applications;
- Evolutionary machine learning methods and applications;
- Neural architecture search and applications;
- Industrial image understanding and applications;
- Industrial big data analysis and applications;
- Data-driven production management and optimization;
- Data-driven operation management and optimization;
- Data-driven product quality prediction;
- Data-driven production process modeling and optimization;
- Data-driven energy management and optimization;
- Data-driven production planning and scheduling.
Prof. Dr. Xianpeng Wang
Guest Editor
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