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Application of Machine Learning and Optimization Methods in Engineering Mathematics
Miljan Kovačević and Borko Đ. Bulajić
2025
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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.
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Keywords
- artificial intelligence
- artificial neural network
- BEM
- big data mining
- Buildings
- capped L1-norm
- Complex analysis
- Cox–Ingersoll–Ross model
- crumb rubber
- data standardization
- deep learning
- Electric vehicles
- estimations
- fisher regularization
- fly ash
- fuzzy PID control
- HARA utility
- heston model
- Interval Valued Pythagorean Fuzzy Analytic Hierarchy Process
- join operation
- lagged cross-correlations
- mechanical characteristics
- nano silica
- Neural Network
- Open Data Barcelona
- optical filter
- PMSM drives
- potential theory
- Pythagorean Fuzzy Analytic Hierarchy Process
- remora optimization algorithm
- robustness
- semantic data enrichment
- Smart city
- smartness
- spatial data distribution
- time series data
- twin extreme learning machine
- variance premium principle
- within-class scatter
- yield rate
Links
DOI: 10.3390/books978-3-7258-4746-4Editions