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MBL Life science memory-based learning (mbl) model
Memory Based Learning (Mbl) Model, supplied by MBL Life science, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/memory-based+learning+model/memory+based+learning/pm37647733-64-10-9
Average 90 stars, based on 1 article reviews
memory-based learning (mbl) model - by Bioz Stars, 2026-10
90/100 stars

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Article Title: High-throughput seed quality analysis in faba bean: leveraging Near-InfraRed spectroscopy (NIRS) data and statistical methods.
Article Snippet: Partial Least Squares (PLS), Elastic Net (EN), Memory-based Learning (MBL), and Bayes B (BB), were employed to estimate protein, oil, and oleic acid content in faba bean seeds.

Article Title: High-throughput seed quality analysis in faba bean: leveraging Near-InfraRed spectroscopy (NIRS) data and statistical methods.
Article Snippet: Memory-based learning (MBL) is a powerful local modelling technique widely adopted in soil spectroscopy (Ramirez-Lopez et al., 2014), yet its application in agriculture and plants remains largely unexplored.

Article Title: A panomics-driven framework for the improvement of major food legume crops: advances, challenges, and future prospects
Article Snippet: Given that models like support vector machines (SVMs) and neural networks, such as artificial neural networks (ANNs) and convolutional neural networks (CNNs), require extensive datasets for training, alternative models, including partial least squares (PLS), elastic net (EN), memory-based learning (MBL), and Bayes B (BB), were employed to predict seed quality in faba beans grown at various locations, with best prediction performance achieved for protein and oil content [ ].

Article Title: Prediction of soil organic carbon fractions in tropical cropland using a regional visible and near-infrared spectral library and machine learning
Article Snippet: Soil organic carbon (SOC) is not a single and uniform entity, therefore understanding SOC fractions, particularly particulate organic carbon (POC) and mineral-associated organic carbon (MAOC), offers valuable insights into SOC dynamics.. However, traditional laboratory measurements of SOC fractions are labor-intensive and costly.. Therefore, leveraging rapid and cost-effective soil spectroscopy holds significant promise for addressing this challenge.

Article Title: NIRS-based detection advances in agriculture: Data enhancement, characteristic wavelength selection and modelling techniques.
Article Snippet: • NIRS-based detection advances in agriculture are overviewed from multiple

Article Title: High-throughput seed quality analysis in faba bean: leveraging Near-InfraRed spectroscopy (NIRS) data and statistical methods.
Article Snippet: We combined chemical analysis of protein, oil content (and composition) with NIRS through chemometrics, employing Partial Least Squares (PLS), Elastic Net (EN), Memory-based Learning (MBL), and Bayes B (BB) as prediction models.

Article Title: Inversion of soil organic carbon content based on the two-point machine learning method.
Article Snippet: • Problem of different spectra for the same substance exists in SOC inversion.. • TPML can overcome this problem by modeling spatially heterogeneous

Spectroscopy:

Article Title: A novel quantitative detection method for soil organic matter content based on visible to near-infrared spectroscopy
Article Snippet: Continued mining operations have resulted in substantial soil degradation, necessitating the effective restoration of ecological functions in soils.. Accurate and rapid measurement of soil organic matter (SOM) is essential for boosting soil fertility, supporting ecological restoration, and facilitating effective environmental management.. Combining visible to near-infrared spectroscopy with machine learning algorithms is a promising technique for quantitative analysis of SOM.



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