Results 241 to 250 of about 493,705 (320)

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

Algebraic methods and computational strategies for pseudoinverse-based MR image reconstruction (Pinv-Recon). [PDF]

Sci Rep
Yeung K, Tobler C, Schulte RF, White B, McIntyre A, Serres S, Morris P, Auer D, Gleeson FV, Tyler DJ, Grist JT, Wiesinger F.   +11 more
europepmc   +1 more source

The Charter, Detention and Possible Regularization of Migrants in an Irregular Situation under the Returns Directive: Mahdi [PDF]

, 2015
Diego Acosta Arcarazo
openalex  

Robust Feedback Optimization with Model Uncertainty: A Regularization Approach [PDF]

W.L. Chan, Z. Y. He, Keith Moffat, Saverio Bolognani, Michael Muehlebach, Florian Dörfler   +5 more
openalex   +1 more source

A Solution for Exosome‐Based Analysis: Surface‐Enhanced Raman Spectroscopy and Artificial Intelligence

Advanced Intelligent Discovery, EarlyView.
Exosomes are emerging as powerful biomarkers for disease diagnosis and monitoring. This review highlights the integration of surface‐enhanced Raman spectroscopy with artificial intelligence to enhance molecular fingerprinting of exosomes. Machine learning and deep learning techniques improve spectral interpretation, enabling accurate classification of ...
Munevver Akdeniz, Zakarya Al‐Shaebi, Omer Aydin   +2 more
wiley   +1 more source

Resolution-enhanced transport of intensity phase imaging using contrast transfer function reformulation and SNR-guided deconvolution. [PDF]

Biomed Opt Express
Shanmugavel SC, Zhu Y.
europepmc   +1 more source

A Machine Learning Perspective on the Brønsted–Evans–Polanyi Relation in Water‐Gas Shift Catalysis on MXenes

Advanced Intelligent Discovery, EarlyView.
Machine learning predicts activation energies for key steps in the water‐gas shift reaction on 92 MXenes. Random Forest is identified as the most accurate model. Reaction energy and reactant LogP emerge as key descriptors. The approach provides a predictive framework for catalyst design, grounded in density functional theory data and validated through ...
Kais Iben Nassar, Tiago L. P. Galvão, José D. Gouveia, José R. B. Gomes   +3 more
wiley   +1 more source

A Generalized Fisher Discriminant Analysis with Adaptive Entropic Regularization for Cross-Model Vibration State Monitoring in Wind Tunnels. [PDF]

Sensors (Basel)
Li Z, Li Z, Chen X, Lin H.
europepmc   +1 more source

Application of Neural Networks for Advanced Ir Spectroscopy Characterization of Ceria Catalysts Surfaces

Advanced Intelligent Discovery, EarlyView.
A novel convolutional neural network architecture enables rapid, unsupervised analysis of IR spectroscopic data from DRIFTS and IRRAS. By combining synthetic data generation with parallel convolutional layers and advanced regularization, the model accurately resolves spectral features of adsorbed CO, offering real‐time insights into ceria surface ...
Mehrdad Jalali, Lachlan Caulfield, Eric Sauter, Alexei Nefedov, Chengwu Yang, Christof Wöll   +5 more
wiley   +1 more source

Brain-age models with lower age prediction accuracy have higher sensitivity for disease detection. [PDF]

PLoS Biol
Schulz MA, Siegel NT, Ritter K.
europepmc   +1 more source