Pocaii–DNN: A Hybrid Population-Centric Adaptive Intelligence Deep Neural Network Framework for High-Precision THz Antenna Design Classification

Authors

  • Rahul Gupta, Sourabh Yadav, Deepika Bairagi, Sonia Wadhwa, Bhawana Yadav, Shantilata Barik

Keywords:

Deep Learning, Terahertz Antennas, Pocaii Optimization, Hyperparameter Tuning, Electromagnetic Design, Binary Classification, Ensemble Learning, ROC-AUC, SMOTE

Abstract

This study presents a hybrid optimization paradigm, termed Pocaii–DNN (Population-CentricAdaptive Intelligence–Deep Neural Network), developed for robust classification of Terahertz (THz) antenna designs using automated

References

Chen, Y., Yan, L., & Han, C. (2021). Hybrid Spherical- and Planar-Wave Modeling and DCNN-Powered Estimation of Terahertz Ultra-Massive MIMO Channels. IEEE Transactions on Communications, 69(10), 7063–7076. https://doi.org/10.1109/TCOMM.2021.3098696

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Published

2024-07-20

How to Cite

Rahul Gupta, Sourabh Yadav, Deepika Bairagi, Sonia Wadhwa, Bhawana Yadav, Shantilata Barik. (2024). Pocaii–DNN: A Hybrid Population-Centric Adaptive Intelligence Deep Neural Network Framework for High-Precision THz Antenna Design Classification . Journal of Computational Analysis and Applications (JoCAAA), 33(07), 3087–3112. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/4289

Issue

Vol. 33 No. 07 (2024): JOCAAA

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.