Data-Driven Intelligent Modeling and Optimization Algorithms for Industrial Processes
The aim of this Special Issue is to explore the multifaceted aspects of data-driven intelligent modeling and optimization algorithms for industrial processes. The main goals are to harness the power of data to improve control, decision making, and parameter optimization, and to drive industrial syst...
में बचाया:
| स्वरूप: | Online |
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| भाषा: | अंग्रेज़ी |
| प्रकाशित: |
MDPI - Multidisciplinary Digital Publishing Institute
2026
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| विषय: | |
| ऑनलाइन पहुंच: | https://directory.doabooks.org/handle/20.500.12854/170648 |
| टैग: |
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| _version_ | 1869528648588984320 |
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| collection | Directory of Open Access Books |
| description | The aim of this Special Issue is to explore the multifaceted aspects of data-driven intelligent modeling and optimization algorithms for industrial processes. The main goals are to harness the power of data to improve control, decision making, and parameter optimization, and to drive industrial systems to unprecedented levels of efficiency, reliability, and adaptability. Research areas in this Special Issue include digital twin technology, multimodal data recognition, sensor data ingestion and real-time processing, multi-objective path-planning, conditional generative adversarial network, generating job recommendations, comprehensive risk assessment, large language models, self-supervised key-point learning, trustworthy article ranking, engine optimization model, and bioinspired generative design. These powerful and intelligent algorithms use data for control, decision making, and parameter optimization, driving industrial systems to unprecedented levels of efficiency, reliability, and adaptability. By sharing their practice and insights in the development and application of these new technologies, the authors of the articles in this reprint have demonstrated the value of data-driven intelligent modeling and optimization algorithms for industrial processes, providing readers with valuable ideological inspiration in the field. |
| format | Online |
| id | doab-20.500.12854ir-170648 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2026 |
| publishDateRange | 2026 |
| publishDateSort | 2026 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1706482026-01-02T16:20:57Z Data-Driven Intelligent Modeling and Optimization Algorithms for Industrial Processes Huang, Zixin Du, Sheng Jin, Li Wan, Xiongbo data-driven modeling industrial processes machine learning algorithms optimization algorithms The aim of this Special Issue is to explore the multifaceted aspects of data-driven intelligent modeling and optimization algorithms for industrial processes. The main goals are to harness the power of data to improve control, decision making, and parameter optimization, and to drive industrial systems to unprecedented levels of efficiency, reliability, and adaptability. Research areas in this Special Issue include digital twin technology, multimodal data recognition, sensor data ingestion and real-time processing, multi-objective path-planning, conditional generative adversarial network, generating job recommendations, comprehensive risk assessment, large language models, self-supervised key-point learning, trustworthy article ranking, engine optimization model, and bioinspired generative design. These powerful and intelligent algorithms use data for control, decision making, and parameter optimization, driving industrial systems to unprecedented levels of efficiency, reliability, and adaptability. By sharing their practice and insights in the development and application of these new technologies, the authors of the articles in this reprint have demonstrated the value of data-driven intelligent modeling and optimization algorithms for industrial processes, providing readers with valuable ideological inspiration in the field. 2026-01-02T16:20:55Z 2026-01-02T16:20:55Z 2025 book 978-3-7258-4911-6 https://directory.doabooks.org/handle/20.500.12854/170648 eng application/octet-stream Attribution 4.0 International https://mdpi.com/books https://mdpi.com/books/pdfview/book/11376 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-4912-3 10.3390/books978-3-7258-4912-3 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 978-3-7258-4911-6 284 CH open access |
| spellingShingle | data-driven modeling industrial processes machine learning algorithms optimization algorithms Data-Driven Intelligent Modeling and Optimization Algorithms for Industrial Processes |
| title | Data-Driven Intelligent Modeling and Optimization Algorithms for Industrial Processes |
| title_full | Data-Driven Intelligent Modeling and Optimization Algorithms for Industrial Processes |
| title_fullStr | Data-Driven Intelligent Modeling and Optimization Algorithms for Industrial Processes |
| title_full_unstemmed | Data-Driven Intelligent Modeling and Optimization Algorithms for Industrial Processes |
| title_short | Data-Driven Intelligent Modeling and Optimization Algorithms for Industrial Processes |
| title_sort | data driven intelligent modeling and optimization algorithms for industrial processes |
| topic | data-driven modeling industrial processes machine learning algorithms optimization algorithms |
| topic_facet | data-driven modeling industrial processes machine learning algorithms optimization algorithms |
| url | https://directory.doabooks.org/handle/20.500.12854/170648 |