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Experimental evaluations on benchmark forensic datasets demonstrate that the proposed framework achieves competitive detection performance and robustness across most datasets, attaining an AUC of up to 0.998 on NIST16, while highlighting specific failure modes on challenging cases such as COVERAGE.
A comprehensive analysis of ML-driven methodologies for denoising, spectral decomposition, feature extraction, and high-accuracy classification in CQD fluorescence systems shows how ML enables ultra-low-level analyte detection, interpretable photophysical modeling, and real-time intelligent sensing across chemical and...
The results demonstrate that SA-LDP-DW supports responsible data sharing, data governance, and privacy-aware data analytics, enabling privacy protection, ownership verification, and reliable analytical utility for real-world data-driven applications.
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