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Deep learning in spectral analysis: Modeling and imaging
Mar 1, 2024 · In recent years, DL methods have been widely explored in spectral analysis. This review provides an overview of the advancements in DL techniques and highlights their recent applications in spectral analysis.
Machine learning in spectral domain - Nature Communications
Feb 26, 2021 · We here propose a radically new approach which anchors the learning process to reciprocal space.
Machine Learning Applied for Spectra Classification
Sep 10, 2021 · In this work, we apply three commonly used machine learning/deep learning architectures to time series spectral data classification. Our proposed baseline models are based on the same PCA preprocessing process.
A review on spectral data preprocessing techniques for machine learning ...
Spectroscopic techniques are indispensable for material characterization, yet their weak signals remain highly prone to interference from environmental noise, instrumental artifacts, sample impurities, scattering effects, and radiation-based distortions (e.g., fluorescence and cosmic rays).
MACHINE LEARNING METHODS FOR SPECTRAL ANALYSIS
Jul 27, 2021 · Measurement science has seen fast growth of data in both volume and complexity in recent years, new algorithms and methodologies have been developed to aid the decision making in measurement sciences, and this process is automated for the liberation of labor.
Machine learning enhanced spectroscopic analysis: towards …
Machine learning (ML) algorithms can enhance humans' ability to extract information from complex spectral data by learning the correlations between mixture compositions and absorption features.
Machine Learning-Based Multifunctional Optical Spectrum Analysis ...
We have investigated four widely used ML algorithms, including support vector machine (SVM), artificial neural network, k-nearest neighbors, and decision tree. First, the wavelengths, OSNRs, and bandwidths of optical signals are processed by four ML methods based on the spectral data.
Machine Learning Interpretation of Optical Spectroscopy Using …
Apr 15, 2025 · PSE-LR enables classification and interpretability by producing a peak-sensitive feature importance map, achieving an F1-score of 0.93 and a feature sensitivity of 1.0.
Spectral methods in machine learning and new strategies for very …
Jan 13, 2009 · First, we aim to demonstrate quantifiable performance-complexity trade-offs for spectral methods in machine learning, by exploiting the distinction between the amount of data to be analyzed and the amount of information those data represent relative to the kernel approximation task at hand.
[2502.09897] Artificial Intelligence in Spectroscopy: Advancing ...
Feb 14, 2025 · We trace the historical evolution of ML in spectroscopy, from early pattern recognition to the latest foundation models capable of advanced reasoning, and offer a taxonomy of representative neural architectures, including graph-based and transformer-based methods.