Application of Machine Learning and Optimization Methods in Engineering Mathematics
The articles published in this Special Issue collectively demonstrate the significant impact of mathematical modeling, machine learning, and optimization techniques in solving complex engineering problems. They cover a broad spectrum of applications, from manufacturing process control and electric v...
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| Päätekijät: | , |
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| Aineistotyyppi: | Online |
| Kieli: | englanti |
| Julkaistu: |
MDPI - Multidisciplinary Digital Publishing Institute
2026
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| Linkit: | https://directory.doabooks.org/handle/20.500.12854/170584 |
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| _version_ | 1869517366367354880 |
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| author | Kovačević, Miljan Bulajić, Borko Đ. |
| author_browse | Bulajić, Borko Đ. Kovačević, Miljan |
| author_facet | Kovačević, Miljan Bulajić, Borko Đ. |
| author_sort | Kovačević, Miljan |
| collection | Directory of Open Access Books |
| description | The articles published in this Special Issue collectively demonstrate the significant impact of mathematical modeling, machine learning, and optimization techniques in solving complex engineering problems. They cover a broad spectrum of applications, from manufacturing process control and electric vehicle motor temperature prediction to the financial optimization and structural analysis of dams. The integration of advanced algorithms, such as fuzzy control, deep learning, and stochastic modeling, with classical analytical methods highlights the evolving landscape of engineering mathematics. These studies not only improve predictive accuracy and operational efficiency but also contribute to sustainable and intelligent engineering solutions. Overall, this Special Issue showcases the critical role of interdisciplinary mathematical approaches in advancing engineering research and practice. |
| format | Online |
| id | doab-20.500.12854ir-170584 |
| 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-1705842026-01-02T16:13:19Z Application of Machine Learning and Optimization Methods in Engineering Mathematics Kovačević, Miljan Bulajić, Borko Đ. crumb rubber fly ash nano silica mechanical characteristics artificial neural network Pythagorean Fuzzy Analytic Hierarchy Process Interval Valued Pythagorean Fuzzy Analytic Hierarchy Process smartness buildings join operation data standardization spatial data distribution lagged cross-correlations time series data semantic data enrichment Open Data Barcelona Smart City potential theory BEM complex analysis estimations twin extreme learning machine within-class scatter fisher regularization capped L1-norm robustness Cox–Ingersoll–Ross model Heston model variance premium principle HARA utility PMSM drives remora optimization algorithm deep learning electric vehicles artificial intelligence optical filter big data mining fuzzy PID control neural network yield rate The articles published in this Special Issue collectively demonstrate the significant impact of mathematical modeling, machine learning, and optimization techniques in solving complex engineering problems. They cover a broad spectrum of applications, from manufacturing process control and electric vehicle motor temperature prediction to the financial optimization and structural analysis of dams. The integration of advanced algorithms, such as fuzzy control, deep learning, and stochastic modeling, with classical analytical methods highlights the evolving landscape of engineering mathematics. These studies not only improve predictive accuracy and operational efficiency but also contribute to sustainable and intelligent engineering solutions. Overall, this Special Issue showcases the critical role of interdisciplinary mathematical approaches in advancing engineering research and practice. 2026-01-02T16:13:15Z 2026-01-02T16:13:15Z 2025 book 978-3-7258-4745-7 https://directory.doabooks.org/handle/20.500.12854/170584 eng application/octet-stream Attribution 4.0 International https://mdpi.com/books https://mdpi.com/books/pdfview/book/11308 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-4746-4 10.3390/books978-3-7258-4746-4 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 978-3-7258-4745-7 196 CH open access |
| spellingShingle | crumb rubber fly ash nano silica mechanical characteristics artificial neural network Pythagorean Fuzzy Analytic Hierarchy Process Interval Valued Pythagorean Fuzzy Analytic Hierarchy Process smartness buildings join operation data standardization spatial data distribution lagged cross-correlations time series data semantic data enrichment Open Data Barcelona Smart City potential theory BEM complex analysis estimations twin extreme learning machine within-class scatter fisher regularization capped L1-norm robustness Cox–Ingersoll–Ross model Heston model variance premium principle HARA utility PMSM drives remora optimization algorithm deep learning electric vehicles artificial intelligence optical filter big data mining fuzzy PID control neural network yield rate Kovačević, Miljan Bulajić, Borko Đ. Application of Machine Learning and Optimization Methods in Engineering Mathematics |
| title | Application of Machine Learning and Optimization Methods in Engineering Mathematics |
| title_full | Application of Machine Learning and Optimization Methods in Engineering Mathematics |
| title_fullStr | Application of Machine Learning and Optimization Methods in Engineering Mathematics |
| title_full_unstemmed | Application of Machine Learning and Optimization Methods in Engineering Mathematics |
| title_short | Application of Machine Learning and Optimization Methods in Engineering Mathematics |
| title_sort | application of machine learning and optimization methods in engineering mathematics |
| topic | crumb rubber fly ash nano silica mechanical characteristics artificial neural network Pythagorean Fuzzy Analytic Hierarchy Process Interval Valued Pythagorean Fuzzy Analytic Hierarchy Process smartness buildings join operation data standardization spatial data distribution lagged cross-correlations time series data semantic data enrichment Open Data Barcelona Smart City potential theory BEM complex analysis estimations twin extreme learning machine within-class scatter fisher regularization capped L1-norm robustness Cox–Ingersoll–Ross model Heston model variance premium principle HARA utility PMSM drives remora optimization algorithm deep learning electric vehicles artificial intelligence optical filter big data mining fuzzy PID control neural network yield rate |
| topic_facet | crumb rubber fly ash nano silica mechanical characteristics artificial neural network Pythagorean Fuzzy Analytic Hierarchy Process Interval Valued Pythagorean Fuzzy Analytic Hierarchy Process smartness buildings join operation data standardization spatial data distribution lagged cross-correlations time series data semantic data enrichment Open Data Barcelona Smart City potential theory BEM complex analysis estimations twin extreme learning machine within-class scatter fisher regularization capped L1-norm robustness Cox–Ingersoll–Ross model Heston model variance premium principle HARA utility PMSM drives remora optimization algorithm deep learning electric vehicles artificial intelligence optical filter big data mining fuzzy PID control neural network yield rate |
| url | https://directory.doabooks.org/handle/20.500.12854/170584 |
| work_keys_str_mv | AT kovacevicmiljan applicationofmachinelearningandoptimizationmethodsinengineeringmathematics AT bulajicborkođ applicationofmachinelearningandoptimizationmethodsinengineeringmathematics |