Data-Driven Modelling And Computational Intelligence For Predictive Engineering And Smart Systems
DOI:
https://doi.org/10.29284/e7szbg33Keywords:
Data-Driven Modelling, Computational Intelligence, Hybrid Machine Learning, Predictive Engineering, Smart Systems, Predictive Maintenance, Statistical Validation.Abstract
The fast development of smart systems of engineering has augmented pressure on precise and robust predictive modelling structures that have the capacity to manage intricate and information-intensive settings. This paper suggests a hybrid data-driven computational intelligence framework that combines ensemble learning with optimization-based parameter optimization to improve the predictive performance. The framework was tested in comparison to the traditional machine learning models, i.e., Artificial Neural Networks, Support Vector Machines, and Random Forest, with the statistical validation measures, i.e. Accuracy, RMSE, Precision, Recall, and F1-Score. The results of the empirical study indicate that the hybrid model proposed has better accuracy, convergence, and robustness in the presence of noise. The reliability and generalization possibility of the approach are verified with the help of cross-validation and significance testing. Moreover, the framework is extended to the intelligent engineering fields like predictive maintenance and energy forecasting, which denotes its usefulness in practice. The results prove the hybrid model of computational intelligence to be a scalable and effective solution to the next-generation predictive engineering and intelligent decision-support systems.
References
1. Alnaqbi, A., Al-Khateeb, G. G., & Zeiada, W. (2026). Innovative hybrid model for spalling prediction in concrete pavement: SVR enhanced by particle swarm optimization. Neural Computing and Applications, 38(1), 9.
2. Alsolami, E. (2026). Investigation of Performance Analysis for Data‐Driven Intelligent Model With Internet of Things in Higher Education Teaching System. European Journal of Education, 61(1), e70409.
3. Apata, O., Munda, J. L., & Migabo, E. M. (2026). Artificial Intelligence for Predictive Maintenance and Performance Optimization in Renewable Energy Systems: A Comprehensive Review. Energies, 19(2), 536.
4. Bakare, M. S., & Abubaker, K. (2026). IoT-based indoor environmental monitoring system using multi-parameter sensing and ESP32-WROOM integration. Discover Electronics, 3(1), 6.
5. Bashir, U., Singh, K., Mansotra, V., Khanday, A. M. U. D., & Neshat, M. (2026). EvoBagNet: a decomposition-driven bagging ensemble with evolutionary hyperparameter optimization for robust stock price prediction. Neural Computing and Applications, 38(3), 31.
6. Bris-Peñalver, F. J., Verdecia-Peña, R., & Alonso, J. I. (2026). A Survey of AI-Enabled Predictive Maintenance for Railway Infrastructure: Models, Data Sources, and Research Challenges. Sensors, 26(3), 906.
7. Daneshvar, A., Golroo, A., Moghadas Nejad, F., Karbalaei, A., Hosseini, P., & Rasti, M. (2026). Cognitive Digital Twins for Pavement Management: A Comprehensive Review. Archives of Computational Methods in Engineering, 1-20.
8. Desai, M., & Chatterjee, K. (2026). Application of Machine Learning Techniques for Prediction of Soil Water Characteristics Curve: A State of the Art Review.
9. Dudek, G., & Blachnik, M. (2026). Artificial Intelligence and Machine Learning Models for Forecasting, Optimization, and Control in Smart Energy Systems. In (Vol. 19, pp. 768): MDPI.
10. Etri, T., Seyed-Djawadi, M. H., Nikoo, M. R., Nazari, R., & Al-Rawas, G. (2026). A causal diagnostic framework for artificial intelligence-driven risk assessment and decision support in complex marine environments. Engineering Applications of Artificial Intelligence, 163, 113066.
11. Girija, R., Vedhapriyavadhana, R., Jayalakshmi, S., & Ahmed, H. F. T. (2026). Smart AI: adapting to sensor data in practical applications. In Adaptive AI in Sensor Informatics (pp. 203-227). Elsevier.
12. Hussein, A. A., Gergely, B., & Hasnain, A. A. (2026). A Survey of Six Classical Classifiers, Including Algorithms, Methodological Characteristics, Foundational Variants, and Recent Advances. Algorithms, 19(1), 37.
13. Jia, H., & Qin, W. (2026). Integrating interpretability, using hybrid data-driven ensemble learning, and optimization framework for concrete strength prediction. Multiscale and Multidisciplinary Modeling, Experiments and Design, 9(1), 59.
14. Kantesaria, R., & Panda, H. S. (2026). A Review on AI-Based Data-Driven Models for Optimization of Nanocarriers as Drug Delivery Systems. ACS Biomaterials Science & Engineering.
15. Karshiboev, A., Al-Samad, K., Tarafdar, M. T. R., Rimi, N. N., Islam, M. S., & Papel, M. S. I. (2026). Artificial Intelligence For Risk And Decision Assessment In Agile IT Projects: A Thematic Analysis And Dynamic Structuration Framework Approach. INTERNATIONAL JOURNAL OF ADVANCES IN SIGNAL AND IMAGE SCIENCES, 387-410.
16. Katragadda, S. R. (2026). Deep Learning-Enabled Demand Intelligence: Transforming Forecast Accuracy in high velocity transaction systems. Journal of Computer Science and Technology Studies, 8(3), 39-45.
17. Kaur, R., Roul, R. K., & Batra, S. (2026). Towards smarter mobility: a Grey Wolf Optimization-based incremental extreme learning machine framework for explainable bike-sharing demand forecasting. Evolving Systems, 17(1), 9.
18. Keshireddy, S. R., Jamithireddy, N. H., Jamithireddy, N. S., Sappa, A., Vandrangi, S. K., Emani, S., & Velidi, G. (2026). Combustion performance prediction in oil and gas plants using integrated neural network models and SAP S4HANA sensor analytics. Scientific Reports.
19. Khursheed, Z., Jawaid, M. A., Mustafa, M., Lalji, S. M., Ali, S. I., & Ayubi, M. (2026). Comparative Analysis of Machine Learning and Neural Network Models for Petrophysical Property Prediction in Heterogeneous Reservoirs. Next Research, 101388.
20. Kumar, D., Addula, S. R., Lind, M., Brown, S., & Odion, S. (2026). AI-Driven Hybrid Deep Learning and Swarm Intelligence for Predictive Maintenance of Smart Manufacturing Robots in Industry 4.0. Electronics, 15(3), 715.
21. Lastra, J. M. P. d. l., Sobrino, I., Rodríguez Borges, V. M., & de la Fuente, J. (2026). Future Perspectives on the Application of Systems Biology and Generative Artificial Intelligence in the Design of Immunogenic Peptides for Vaccines. Vaccines, 14(2), 177.
22. Li, J.-P., Polovina, N., & Konur, S. (2026). A Review of AI-Driven Engineering Modelling and Optimization: Methodologies, Applications and Future Directions. Algorithms, 19(2).
23. Li, Y., & Zhou, H. (2026). Prediction of seismic topographic effects from DEM data via U-Net deep learning model. The European Physical Journal Plus, 141(1), 13.
24. M’harzi Alaoui, F. E., Taki, Y., Aalil, I., Dadah, O., & Ben Abbou, S. (2026). From lab to algorithm: a review of AI-driven predictive modeling in soil stabilization for road construction. Journal of Umm Al-Qura University for Engineering and Architecture, 1-31.
25. Mohammed, B. (2026). The sentient infrastructure paradigm: a review of novel sensors, digital twins, and AI for civil structural health monitoring. Sensor Review, 1-19.
26. Ni, Y., Zhu, Y., Xiao, B., & Liu, Q. (2026). WAWL-net: A hybrid model combining weighted aggregation and wavelet convolutions for tropical cyclone intensity estimation. Atmospheric Research, 108797.
27. Panda, G., & Tripathy, B. (2026). Hybrid computational intelligence techniques for environmental hazard prediction and risk management. In Data-Driven Environmental Intelligence (pp. 28-65). CRC Press.
28. Paria, A., & Das, R. (2026). Integrating Artificial Intelligence in Climate-Aware Yield Forecasting for Sustainable Precision Agriculture. In Harnessing AI to Reshape the Future of Agriculture (pp. 437-455). Springer.
29. ShravanKumar, S., Karthick, A., Priya, A., Mohanavel, V., & Muthusamy, S. (2026). Remote Sensing and Artificial Intelligence in Flood Prediction: Progress, Challenges, and Prospects. Archives of Computational Methods in Engineering, 1-32.
30. Singh, S., Sharma, D., & Gandhi, A. (2026). When machine learning meets credit assessments: benchmarking ML models for forecasting NBFC stability in pre-crisis times. Journal of Modelling in Management, 1-28.
31. Verwoerd, J. C. (2026). Machine Learning Applications in Sensor-Based Infrastructure Monitoring.
32. Villegas-Ch, W., Gutierrez, R., Jaramillo-Alcazar, A., & Maldonado-Navarro, A. (2026). Integrating artificial intelligence and data envelopment analysis for sustainable efficiency assessment in higher education. Computers and Education: Artificial Intelligence, 100557.
33. Wang, R. (2026). A Data-Driven Predictive Maintenance Framework for Smart Buildings: Integrating Digital Twin and Machine Learning in HVAC Systems. Journal of Building Engineering, 115416.
34. Wang, Z., Tang, R., Chen, G., Li, H., Deng, Y., Shen, J., & Li, D. (2026). A Review of Artificial Intelligence-Driven Smart Treatment of Aquaculture Effluent: Technical Framework, Application Scenarios, and Development Outlook. Water, 18(4), 470.
35. Wu, J., Lu, Y., Zhang, Y., Zhang, S., Wang, J., & Liu, S. (2026). Swelling-shrinkage of an unsaturated clay upon freezing: Experimental investigation and data-driven modelling. Cold Regions Science and Technology, 104856.
36. Xing, Y., Wang, J., Cui, S., Liu, X., & Song, M. (2026). Interpretable Bayesian-optimized Autoencoder for fault detection and diagnosis with application in nuclear power plants. Progress in Nuclear Energy, 190, 105982.
37. Yang, W., Feng, X.-Q., & Gao, H. (2026). Outstanding issues and emerging frontiers in fracture mechanics. International Journal of Fracture, 250(1), 10.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
