Results 241 to 250 of about 248,790 (377)
Brain Connectivity and Information-Flow Breakdown Revealed by a Minimum Spanning Tree-Based Analysis of MRI Data in Behavioral Variant Frontotemporal Dementia. [PDF]
Saba V+9 more
europepmc +1 more source
Inadequacies of Minimum Spanning Trees in Molecular Epidemiology [PDF]
Stephen J. Salipante, Barry G. Hall
openalex +1 more source
Machine Learning for Organic Fluorescent Materials
Organic fluorescent materials (OFMs) have demonstrated significant potential in diverse applications. Conventional approaches for studying OFMs face significant limitations in fluorescence spectroscopy and computational methods. Machine learning (ML) has revolutionized materials chemistry, offering superior predictive accuracy and efficiency over ...
Jiamin Zhong+7 more
wiley +1 more source
Study of the minimum spanning hyper-tree routing algorithm in wireless sensor networks
Ting Yang+4 more
openalex +2 more sources
AI‐Enhanced Surface‐Enhanced Raman Scattering for Accurate and Sensitive Biomedical Sensing
AI‐SERS advances spectral interpretation with greater precision and speed, enhancing molecular detection, biomedical analysis, and imaging. This review explores its essential contributions to biofluid analysis, disease identification, therapeutic agent evaluation, and high‐resolution biomedical imaging, aiding diagnostic decision‐making.
Seungki Lee, Rowoon Park, Ho Sang Jung
wiley +1 more source
Integrating the Local Property and Topological Structure in the Minimum Spanning Tree Brain Functional Network for Classification of Early Mild Cognitive Impairment. [PDF]
Cui X+5 more
europepmc +1 more source
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
MISTICA: Minimum Spanning Tree-Based Coarse Image Alignment for Microscopy Image Sequences. [PDF]
Ray N, McArdle S, Ley K, Acton ST.
europepmc +1 more source
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+3 more
wiley +1 more source