Results 191 to 200 of about 474,975 (277)

Predicting High‐Resolution Spatial and Spectral Features in Mass Spectrometry Imaging with Machine Learning and Multimodal Data Fusion

Advanced Intelligent Discovery, EarlyView.
A multimodal fusion pipeline predicts high‐resolution ion distributions in imaging mass spectrometry by integrating Fourier transform ion cyclotron resonance, time‐of‐flight matrix‐assisted laser desorption/ionization, and time‐of‐flight secondary ion mass spectrometry data.
Md Inzamam Ul Haque, Ramakrishnan Kannan, Jacob D. Hinkle, Sylwia A. Stopka, Anton V. Ievlev, Nathalie Y. R. Agar, Olga S. Ovchinnikova, Debangshu Mukherjee   +7 more
wiley   +1 more source

Hybrid net powered large scale audit of dataset licensing and attribution practices for enhanced transparency and compliance. [PDF]

Sci Rep
Ayyamperumal V, Aswath S, Vignesh S, Thamaraimanalan T.   +3 more
europepmc   +1 more source

Universally Accurate or Specifically Inadequate? Stress‐Testing General Purpose Machine Learning Interatomic Potentials

Advanced Intelligent Discovery, EarlyView.
We investigate MACE‐MP‐0 and M3GNet, two general‐purpose machine learning potentials, in materials discovery and find that both generally yield reliable predictions. At the same time, both potentials show a bias towards overstabilizing high energy metastable states. We deduce a metric to quantify when these potentials are safe to use.
Konstantin S. Jakob, Karsten Reuter, Johannes T. Margraf   +2 more
wiley   +1 more source

Perceived Determinants of Excellent Self-Rated Health Among HIV Virally Suppressed Adults in the Eastern Cape Province, South Africa. [PDF]

Int J Environ Res Public Health
Nomatshila ZB, Gonah L, Nomatshila SC, Apalata TR.   +3 more
europepmc   +1 more source

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

Large language model-driven time-series forecasting of financial network indicators. [PDF]

Front Artif Intell
Wang MH, Yeung Y.
europepmc   +1 more source

Harnessing Large Language Models to Advance Microbiome Research: From Sequence Analysis to Clinical Applications

Advanced Intelligent Discovery, EarlyView.
Large language models are transforming microbiome research by enabling advanced sequence profiling, functional prediction, and association mining across complex datasets. They automate microbial classification and disease‐state recognition, improving cross‐study integration and clinical diagnostics.
Jieqi Xing, He Wang, Yangyang Sun, Xiaoquan Su, Shunyao Wu   +4 more
wiley   +1 more source

Automated Radiological Report Generation from Breast Ultrasound Images Using Vision and Language Transformers. [PDF]

J Imaging
Khatoon S, Mahmood A.
europepmc   +1 more source

opXRD: Open Experimental Powder X‐Ray Diffraction Database

Advanced Intelligent Discovery, EarlyView.
We introduce the Open Experimental Powder X‐ray Diffraction Database, the largest openly accessible collection of experimental powder diffractograms, comprising over 92,000 patterns collected across diverse material classes and experimental setups. Our ongoing effort aims to guide machine learning research toward fully automated analysis of pXRD data ...
Daniel Hollarek, Henrik Schopmans, Jona Östreicher, Jonas Teufel, Bin Cao, Adie Alwen, Simon Schweidler, Mriganka Singh, Tim Kodalle, Hanlin Hu, Gregoire Heymans, Maged Abdelsamie, Arthur Hardiagon, Alexander Wieczorek, Siarhei Zhuk, Ruth Schwaiger, Sebastian Siol, François‐Xavier Coudert, Moritz Wolf, Carolin M. Sutter‐Fella, Ben Breitung, Andrea M. Hodge, Tong‐yi Zhang, Pascal Friederich   +23 more
wiley   +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