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  • Publication
    AI-powered MMI fiber sensors for wide-range refractive index detection using neural networks algorithm
    (Elsevier, 2025-03)
    Nurul Farah Adilla Zaidi
    ;
    Muhammad Yusof Mohd Noor
    ;
    Nur Najahatul Huda Saris
    ;
    Mohd Rashidi Salim
    ;
    Sumiaty Ambran
    ;
    Azizul Azizan
    ;
    Raja Kamarulzaman Raja Ibrahim
    ;
    Fauzan Ahmad
    ;
    Nurul Ashikin Daud
    ;
    Norazida Ali
    ;
    ;
    Ian Yulianti
    ;
    Gang-Ding Peng
    This research presents an artificial intelligence (AI)-driven machine learning (ML) approach for accurately measuring refractive index (RI) values across both lower and higher regimes than the fiber material's RI, using a simple single multimode interference (MMI) fiber sensor. The sensor configuration consists of a no-core fiber (NCF) segment between two single-mode fiber (SMF) sections. A Bilayer Neural Network (BNN) regression model is employed to predict both low refractive index (LRI) and high refractive index (HRI) regimes, achieving a broad dynamic measurement range from 1.3000 RIU to 1.3900 RIU for LRI regime and from 1.4600 RIU to 1.5500 RIU for HRI regime. The model demonstrates 99.7% accuracy and a low root mean square error (RMSE) of 0.0044, ensuring that predicted RI values closely match actual measurements without any RI ambiguity. Furthermore, the all-silica NCF structure is inherently resistant to temperature fluctuations, enabling its deployment in environments with varying temperatures without requiring additional temperature compensation mechanisms.
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