Fusion Of Computational Mathematics And Artificial Intelligence For Intelligent Decision-Making In Engineering And Technology

Dr. Vijaya Kumar H R; Biswajit Basak; Dr. Smarti Gosani; Dr. N.B. Mahesh Kumar; T. Hemalatha; Anupama Shetter

doi:10.29284/s6eze156

Authors

Dr. Vijaya Kumar H R Associate Professor, Department of Electronics and Communication Engineering, Harsha Institute of Technology, Bengaluru, Karnataka
Biswajit Basak Assistant Professor, Department: Electronics & Communication Engineering, Hooghly Engineering & Technology College, Hooghly, West Bengal
Dr. Smarti Gosani Associate Professor, Department of Mathematics (School of Basic and Applied Sciences), Lingaya's Vidyapeeth, Faridabad, Haryana, Delhi NCR
Dr. N.B. Mahesh Kumar Associate Professor, Department of Computer Science and Engineering, Hindusthan Institute of Technology, Coimbatore, Tamilnadu
T. Hemalatha Assistant Professor, Computer Science And Engineering, Koneru Lakshmaiah Education Foundation, Greenfields, Vaddeswaram, Guntur, Andhra Pradesh
Anupama Shetter Assistant Professor, Electronics and Communication Engineering, ATME College of Engineering, Mysuru, Karnataka

DOI:

https://doi.org/10.29284/s6eze156

Keywords:

Computational Mathematics, Artificial intelligence, Hybrid optimization, Intelligent decision-making, Machine learning, Engineering systems, Convergence analysis, predictive modeling.

Abstract

The growing complexity of the contemporary engineering systems requires intelligent decision-making structures that can deal with nonlinear dynamics, high-dimensional data, and operational uncertainty. Current paper suggests a hybrid computing system, which combines computational mathematics and artificial intelligence to facilitate optimization, prediction and stability of the system in engineering processes. The model integrates mathematical modelling and constraint-based optimisation with machine learning methods in order to provide both theoretical rigour and adaptive learning. To confirm the performance improvements, descriptive and inferential statistical analysis was performed showing statistically significant improvements when compared to standalone optimization and machine learning-based models. Improved predictive reliability and the stability of convergence are verified by the performance evaluation metrics, such as accuracy, RMSE, precision, recall, and F1-score. The practicality and scalability of the proposed framework are also emphasized in the case studies of smart manufacturing optimization, energy load forecasting and predictive maintenance. The hybrid nature(mathematically constrained) guarantees feasibility besides utilizing AI in nonlinear pattern identification and nonlinear adaptability in real-time. In general, the integration offers a powerful, scalable, and computationally efficient solution with intelligent engineering decision systems in complex and data-intensive application environments.

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