Results 171 to 180 of about 38,544 (293)
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
Data driven pedagogy in physical education a new paradigm in teaching effectiveness. [PDF]
Wu Y.
europepmc +1 more source
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 +2 more
wiley +1 more source
Toward universal models for collective interactions in biomolecular condensates. [PDF]
Milanetti E +4 more
europepmc +1 more source
A sequential deep learning framework is developed to model surface roughness progression in multi‐stage microneedle fabrication. Using real‐world experimental data from 3D printing, molding, and casting stages, an long short‐term memory‐based recurrent neural network captures the cumulative influence of geometric parameters and intermediate outputs ...
Abdollah Ahmadpour +5 more
wiley +1 more source
An integrative model of pro- and anti-inflammatory signaling pathways in macrophage differentiation: the role of NF-<i>κ</i>B and CREB. [PDF]
Martínez-Méndez D +7 more
europepmc +1 more source
CrossMatAgent is a multi‐agent framework that combines large language models and diffusion‐based generative AI to automate metamaterial design. By coordinating task‐specific agents—such as describer, architect, and builder—it transforms user‐provided image prompts into high‐fidelity, printable lattice patterns.
Jie Tian +12 more
wiley +1 more source
Fuzzy energy management using a chaotic model to improve fuel consumption of fuel cell-battery hybrid fixed-wing UAVs operating under uncertainty control. [PDF]
Rostami M, Habibi P, Farajollahi A.
europepmc +1 more source
Deep Learning‐Assisted Coherent Raman Scattering Microscopy
The analytical capabilities of coherent Raman scattering microscopy are augmented through deep learning integration. This synergistic paradigm improves fundamental performance via denoising, deconvolution, and hyperspectral unmixing. Concurrently, it enhances downstream image analysis including subcellular localization, virtual staining, and clinical ...
Jianlin Liu +4 more
wiley +1 more source
Galaxy Evolution with Manifold Learning. [PDF]
Takeuchi TT, Cooray S, Kano RR.
europepmc +1 more source

