Results 121 to 130 of about 5,933,507 (244)

IMPLEMENTING PLS FOR DISTANCE-BASED REGRESSION: COMPUTATIONAL ISSUES [PDF]

Distance-based regression allows for a neat implementation of the Partial Least Squares recurrence. In this paper we address practical issues arising when dealing with moderately large datasets (n ~ 104) such as those typical of automobile insurance ...
Aurea Grane, Josep Fortiana, Eva Boj, M. Merce Claramunt   +3 more
core  

Machine Learning‐Supported Analysis for Predicting and Visualizing Nonlinear Relationships Between Material Properties in Electroplated Chromium Layers

Advanced Engineering Materials, EarlyView.
This study applies machine learning regression to predict chromium layer thickness in decorative trivalent chromium electroplating, using 441 experiments from laboratory‐scale (1L) and pilot‐scale (14L) setups. Tree‐based models, particularly CatBoost, outperformed linear regression by capturing nonlinear parameter interactions (R2$R^2$ up to 0.77 ...
Christoph Baumer, Christopher Mai, Luca Eisentraut, Andreas Bund, Ricardo Buettner   +4 more
wiley   +1 more source

Partial Least Squares Regression Method to Predict Docosahexaenoic and Eicosapentaenoic Acids in Fish Oil Supplements

Orbital: The Electronic Journal of Chemistry
Fish oil dietary supplements have been linked to various health benefits due to the high concentration of omega-3 polyunsaturated fatty acid (ω-3 PUFA). The potential use of attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy
Thiago Inácio Barros Lopes, Tainara Andrade do Nascimento, Elba de Souza Pereira, Samuel Leite de Oliveira, Diego Galvan, Glaucia Braz Alcantara   +5 more
doaj   +1 more source

Partial Least Squares Regression Analysis of Alzheimer's Disease Biomarkers, Modifiable Health Variables, and Cognitive Change in Older Adults with Mild Cognitive Impairment. [PDF]

J Alzheimers Dis, 2023
Stark J, Hiersche KJ, Yu JC, Hasselbach AN, Abdi H, Hayes SM, Alzheimer’s Disease Neuroimaging Initiative.   +6 more
europepmc   +1 more source

New AI‐Assisted Approach for Expanding the Solution Space: Application to Lattice Structure Design

Advanced Engineering Materials, EarlyView.
This work introduces an innovative framework for designing structured materials by ex panding the design space through reparameterization of qualitative variables into continuous structural descriptors. Combined with machine‐learning‐based prediction and multi‐objective optimization, the approach enables the discovery of novel lattice architectures ...
G. H. Gahimbare, Y. Beideler, D. Chune, R. De Guio, T. Roland, H. Chibane   +5 more
wiley   +1 more source

Predicting Activity Duration in Smart Sensing Environments Using Synthetic Data and Partial Least Squares Regression: The Case of Dementia Patients. [PDF]

Sensors (Basel), 2022
Ortiz-Barrios M, Järpe E, García-Constantino M, Cleland I, Nugent C, Nugent C, Arias-Fonseca S, Jaramillo-Rueda N.   +7 more
europepmc   +1 more source

Partial least-squares regression with unlabeled data

It is well known that the prediction errors from principal component regression (PCR) and partial least-squares regression (PLSR) can be reduced by using both labeled and unlabeled data for stabilizing the latent subspaces in the calibration step.
Bonvin, Dominique, Amrhein, Michael, Gujral, Paman, Wise, Barry   +3 more
core  

Microstructure Modification of Additively Manufactured Mo–9Si–8B by Annealing and Its Effects on High‐Temperature Mechanical Properties

Advanced Engineering Materials, EarlyView.
Postbuild annealing systematically modifies the phase fractions and morphology of EB‐PBF processed Mo–9Si–8B. Quantitative microstructure–property correlations reveal how controlled phase evolution enhances high‐temperature compressive strength and creep resistance.
Christopher Schmidt, Yong Chen, Georg Hasemann, Benjamin Wahlmann, Carolin Körner, Manja Krüger   +5 more
wiley   +1 more source

Correlation of Glucosinolates and Volatile Constituents of Six Brassicaceae Seeds with Their Antioxidant Activities Based on Partial Least Squares Regression. [PDF]

Plants (Basel), 2022
Khalil N, Gad HA, Al Musayeib NM, Bishr M, Ashour ML.   +4 more
europepmc   +1 more source

Leveraging Symbolic Artificial Intelligence and Fuzzy Logic for Materials Science: A Review of Methods, Challenges, and Applications to Scarce and Imperfect Experimental Data

Advanced Engineering Materials, EarlyView.
This article explores the transformative potential of symbolic artificial intelligence (AI) in the field of materials science, particularly in leveraging experimental data. The article presents several symbolic AI models and discusses their applications in materials science.
Ahmed Amrani, Mohamed‐Ali Belloum, Laurence Boudet, Valentin Fouillard, Edwin Friedmann, Guillaume Lacharme, Ali Mahmoud, Jean‐Philippe Poli   +7 more
wiley   +1 more source