Current - Issue
Year 2026 · Volume 6 · Issue 4
Original Article
Regime-Aware Model Selection for Internal Combustion Engine Performance Prediction Using Machine Learning
Ashish Kumar Routray1
Soumyajeet chakra2
Anuprita Chakra3
Thambe Sai Pavan4
Abhibhav Mohanty5
1 Department of Statistics, Ravenshaw University, Cuttack, Odisha. 2 4 5 Department of Data Science, Lakshya Institute of Technology, Bhubaneswar, Odisha. 3 Assistance professor in Agriculture department, GIFT autonomous, Bhubaneswar, Odisha.
Published Online: July-August 2026
Pages: 364-373
Cite this article
↗ https://www.doi.org/10.59256/ijrtmr.20260604040
References
1. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.
2. Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International
Conference on Knowledge Discovery and Data Mining, 785–794.
3. Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232.
4. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
5. Grinsztajn, L., Oyallon, E., & Varoquaux, G. (2022). Why do tree-based models still outperform deep learning on typical tabular data?
Advances in Neural Information Processing Systems, 35.
6. Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning (2nd ed.). Springer.
7. Hornik, K., Stinchcombe, M., & White, H. (1989). Multilayer feedforward networks are universal approximators. Neural Networks, 2(5),
359–366.
8. Kingma, D. P., & Ba, J. (2015). Adam: A method for stochastic optimization. International Conference on Learning Representations.
9. Kuhn, M., & Johnson, K. (2013). Applied Predictive Modeling. Springer.
10. Shwartz-Ziv, R., & Armon, A. (2022). Tabular data: Deep learning is not all you need. Information Fusion, 81, 84–90.
11. Heywood, J. B. (2018). Internal Combustion Engine Fundamentals (2nd ed.). McGraw-Hill.
12. Wolpert, D. H. (1996). The lack of a priori distinctions between learning algorithms. Neural Computation, 8(7), 1341–1390.
13. Dehury, S., Sahoo, S. S., Routray, A. K., Kumar, M., & Sahu, S. K. (2026). A Pseudo-Convex Fuzzy EOQ Model for Deteriorating Items
with Time Varying Demand. Asian Research Journal of Mathematics, 22(7), 219–238. https://doi.org/10.9734/arjom/2026/v22i71127
14. Routray, A. K., Dehury, S., & Sahu, S. K. (2026). Three-Warehouse Inventory Model for Non-Instantaneously Deteriorating Items with
Ramp-Type Demand, Carbon Cap and Trade, Exponential Bac and Two-Part Trade Credit under Inflation. Asian Journal of Probability
and Statistics, 28(6), 43–64. https://doi.org/10.9734/ajpas/2026/v28i6905
15. Routray, A. K., Sahoo, S. S., Dehury, S., & Sahu, S. K. (2026). Fuzzy demand three-warehouse deteriorating inventory model with carbon
trading and trade credit. International Journal of Statistics and Applied Mathematics, 11(7), 237–249.
https://doi.org/10.22271/maths.2026.v11.i7c.2643
16. Routray, A. K., Dehury, S., & Sahu, S. K. (2026). A three-warehouse inventory model with time-dependent deterioration, non-
instantaneous decay, and quadratic demand under partial backlogging. Journal of Research in Business and Management, 14(5), 82–97.
https://doi.org/10.35629/3002-14058297
17. Routray, A. K., Dehury, S., Kumar, M., & Sahu, S. K. (2026). Deep learning driven agricultural remote sensing: A scoping review of crop
mapping, soil moisture estimation, and multi-sensor fusion. Journal of Emerging Technologies and Innovative Research (JETIR), 13(5),
JETIR2605037.
2. Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International
Conference on Knowledge Discovery and Data Mining, 785–794.
3. Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232.
4. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
5. Grinsztajn, L., Oyallon, E., & Varoquaux, G. (2022). Why do tree-based models still outperform deep learning on typical tabular data?
Advances in Neural Information Processing Systems, 35.
6. Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning (2nd ed.). Springer.
7. Hornik, K., Stinchcombe, M., & White, H. (1989). Multilayer feedforward networks are universal approximators. Neural Networks, 2(5),
359–366.
8. Kingma, D. P., & Ba, J. (2015). Adam: A method for stochastic optimization. International Conference on Learning Representations.
9. Kuhn, M., & Johnson, K. (2013). Applied Predictive Modeling. Springer.
10. Shwartz-Ziv, R., & Armon, A. (2022). Tabular data: Deep learning is not all you need. Information Fusion, 81, 84–90.
11. Heywood, J. B. (2018). Internal Combustion Engine Fundamentals (2nd ed.). McGraw-Hill.
12. Wolpert, D. H. (1996). The lack of a priori distinctions between learning algorithms. Neural Computation, 8(7), 1341–1390.
13. Dehury, S., Sahoo, S. S., Routray, A. K., Kumar, M., & Sahu, S. K. (2026). A Pseudo-Convex Fuzzy EOQ Model for Deteriorating Items
with Time Varying Demand. Asian Research Journal of Mathematics, 22(7), 219–238. https://doi.org/10.9734/arjom/2026/v22i71127
14. Routray, A. K., Dehury, S., & Sahu, S. K. (2026). Three-Warehouse Inventory Model for Non-Instantaneously Deteriorating Items with
Ramp-Type Demand, Carbon Cap and Trade, Exponential Bac and Two-Part Trade Credit under Inflation. Asian Journal of Probability
and Statistics, 28(6), 43–64. https://doi.org/10.9734/ajpas/2026/v28i6905
15. Routray, A. K., Sahoo, S. S., Dehury, S., & Sahu, S. K. (2026). Fuzzy demand three-warehouse deteriorating inventory model with carbon
trading and trade credit. International Journal of Statistics and Applied Mathematics, 11(7), 237–249.
https://doi.org/10.22271/maths.2026.v11.i7c.2643
16. Routray, A. K., Dehury, S., & Sahu, S. K. (2026). A three-warehouse inventory model with time-dependent deterioration, non-
instantaneous decay, and quadratic demand under partial backlogging. Journal of Research in Business and Management, 14(5), 82–97.
https://doi.org/10.35629/3002-14058297
17. Routray, A. K., Dehury, S., Kumar, M., & Sahu, S. K. (2026). Deep learning driven agricultural remote sensing: A scoping review of crop
mapping, soil moisture estimation, and multi-sensor fusion. Journal of Emerging Technologies and Innovative Research (JETIR), 13(5),
JETIR2605037.
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