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    Near-Infrared Spectroscopy Machine-Learning Spectral Analysis Tool for Blueberries (<i>Vaccinium corymbosum</i>) Cultivar Discrimination by Pedro Ribeiro, Maria Inês Barbosa, Clara Sousa, Pedro Miguel Rodrigues

    Published 2025-04-01
    “…Machine-learning-based models, selected from a pool of 10 classifiers based on their discrimination power under a twofold stratified cross-validation process, were trained/tested with 1 to 20 components obtained by the application of data dimensionality reduction (DDR) techniques (dictionary learning, factor analysis, fast individual component analysis, and principal component analysis) to different near-infrared (NIR) spectra regions’ data, to either analyze a single spectral region and season or combine spectral regions and/or seasons for each side of the blueberry leaf. …”
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    Deep Learning for Opportunistic Rain Estimation via Satellite Microwave Links by Giovanni Scognamiglio, Andrea Rucci, Attilio Vaccaro, Elisa Adirosi, Fabiola Sapienza, Filippo Giannetti, Giacomo Bacci, Sabina Angeloni, Luca Baldini, Giacomo Roversi, Alberto Ortolani, Andrea Antonini, Samantha Melani

    Published 2024-10-01
    “…Compared to the state-of-the-art power-law-based models applied to similar datasets reported in the literature, our ML models achieve a <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>46</mn><mo>%</mo></mrow></semantics></math></inline-formula> reduction in the root mean squared error (RMSE) for event-based cumulative precipitation predictions.…”
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