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@article{181383,
        author = {Shefali Ajay Gaddam and Mahesh Mathpati},
        title = {Enhancing Antenna Design Through Stacked Machine Learning Models for S11 Prediction},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {4691-4696},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=181383},
        abstract = {The optimization of return loss (S11) is crucial in antenna design, as it significantly impacts the efficiency and performance of antenna systems. This research explores the application of stacked machine learning models to predict S11 parameters based on various design features, including frequency, patch dimensions, and slot characteristics. Utilizing a comprehensive dataset comprising diverse antenna configurations, we employed base models, specifically Random Forest and XGBoost, to capture complex relationships between design parameters and return loss. A stacking ensemble approach was implemented to combine the predictions of these models, enhancing the accuracy of S11 forecasts.
Hyperparameter tuning through Grid Search was conducted to optimize the performance of the stacked model. The results demonstrated a significant improvement in predictive capability, achieving a Mean Squared Error of 0.1968 and an R² score of 0.9775. Additionally, feature importance analysis was performed to identify key design parameters influencing S11, providing valuable insights for antenna designers. The findings of this study not only underscore the effectiveness of machine learning techniques in antenna optimization but also contribute to the advancement of data-driven approaches in the field of electromagnetics.},
        keywords = {S11, Antenna Design Patch Antenna, Random Forest, XGBoost},
        month = {June},
        }