MATHEMATICAL MODELING OF NIR SPECTROSCOPY DATA AND DEVELOPMENT OF A DIAGNOSTIC MODEL BASED ON ARTIFICIAL INTELLIGENCE
DOI:
https://doi.org/10.65164/b3182r59Kalit so‘zlar:
artificial intelligence, NIR spectroscopy, near-infrared radiation, mathematical modeling, diagnostic model, machine learning, spectral analysis, biological tissues, optical properties, early diagnosis.Abstrak
The paper presents the development of a diagnostic model based on near- infrared
(NIR) spectroscopy data using artificial intelligence methods. Spectral signals are preprocessed and
mathematically modeled. The modeling is based on the Beer–Lambert law, and the J criterion is used
to evaluate signal quality. The obtained results demonstrate the effectiveness of the proposed model.
The maximum J value reached 6.57, while for the simple method it was
4.75. The overall accuracy of the model ranged from 85% to 92%. In some cases, a decrease in
accuracy was observed due to noise and external factors. The proposed approach is effective for
detecting optical changes in biological tissues and for early disease diagnosis.
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