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By integrating Capsule Networks (CapsNet) and transformer architectures with ensemble learning and data augmentation, the proposed approach optimizes the identification of wavelength propagation discrepancies in photonic structures. Experiments conducted on the ICCAD\u20102012 benchmark dataset demonstrate that the ensemble model achieves 90% test accuracy, outperforming standalone CapsNet and transformer models while reducing overfitting. The model also demonstrates strong classification consistency, with an F1\u2010score of 90% and a G\u2010mean of 90%, indicating robust performance across precision\u2013recall balance and class\u2010wise sensitivity\u2013specificity harmony. The framework\u2019s success in balancing performance and generalization highlights its potential to streamline photonic device design for applications in sensing, telecommunications, and energy harvesting. This work bridges advanced machine learning techniques with photonic engineering, offering a scalable and efficient solution for complex light\u2010matter interaction analysis.<\/jats:p>","DOI":"10.1155\/cplx\/8211411","type":"journal-article","created":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T04:51:31Z","timestamp":1760676691000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Robust Hotspot Detection in Photonic Crystals Using a Hybrid Capsule\u2013Transformer Deep Learning Model"],"prefix":"10.1155","volume":"2025","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-4287-7075","authenticated-orcid":false,"given":"Hassan","family":"Salarabadi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3825-7285","authenticated-orcid":false,"given":"Mohammad 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