Results 181 to 190 of about 113,736 (262)

Autonomous AI‐Driven Design for Skin Product Formulations

Advanced Intelligent Discovery, EarlyView.
This review presents a comprehensive closed‐loop framework for autonomous skin product formulation design. By integrating artificial intelligence‐driven experiment selection with automated multi‐tiered assays, the approach shifts development from trial‐and‐error to intelligent optimisation.
Yu Zhang, Yongrui Xiao, Chunlin Chen, Dimitrios Tsaoulidis, Kai Yang, Tao Chen   +5 more
wiley   +1 more source

Modeling chronic pain interconnections using Bayesian networks: insights from the Qatar Biobank study. [PDF]

Front Pain Res (Lausanne)
Al-Khinji AAMA, Malouche D.
europepmc   +1 more source

Assessing the Value of Seismic Amplitude Versus Offset (AVO) Attributes for CO2 Storage Project Using a Bayesian Network Model for Decision Support

Abhash Kumar, Jiaan Wang, William Harbert, Robert Dilmore, Brian Strazisar   +4 more
openalex   +1 more source

Multi‐Property Machine Learning Models to Accelerate the Transition Toward Bio‐Based Emulsion Polymers

Advanced Intelligent Discovery, EarlyView.
A machine learning framework simultaneously predicts four critical properties of monomers for emulsion polymerization: propagation rate constant, reactivity ratios, glass transition temperature, and water solubility. These tools can be used to systematically identify viable bio‐based monomer pairs as replacements for conventional formulations, with ...
Kiarash Farajzadehahary, Nicholas Ballard   +1 more
wiley   +1 more source

Data variability in neural network Bayesian inference

Lindner, Javed, Dahmen, David, Krämer, Michael, Helias, Moritz   +3 more
openalex   +1 more source

AI‐Driven Cancer Multi‐Omics: A Review From the Data Pipeline Perspective

Advanced Intelligent Discovery, EarlyView.
The exponential growth of cancer multi‐omics data brings opportunities and challenges for precision oncology. This review systematically examines AI's role in addressing these challenges, covering generative models, integration architectures, Explainable AI for clinical trust, clinical applications, and key directions for clinical translation.
Shilong Liu, Shunxiang Li, Kun Qian
wiley   +1 more source

Multicell migration tracking within angiogenic networks by deep learning-based segmentation and augmented Bayesian filtering

, 2018
Mengmeng Wang, Lee‐Ling Sharon Ong, Justin Dauwels, Haruhiko Asada   +3 more
openalex   +1 more source

Learning Analytics of Online Students Performance in Mathematics Using Bayesian Network

openalex   +2 more sources

Interpretable Machine Learning for Bandgap Prediction and Descriptor‐Guided Design Rules of Phosphates

Advanced Intelligent Discovery, EarlyView.
An explainable CatBoost model was trained to predict the bandgaps of 474 phosphate crystals based on composition and density descriptors. SHAP analysis identified two key variables—d‐electron‐count dispersion and atomic‐density dispersion—as the primary drivers of the model's predictions.
Wenhu Wang, Abudukadi Tudi, Ran An, Zhihua Yang   +3 more
wiley   +1 more source

Adaptive Non-Stationary Fuzzy Time Series Forecasting with Bayesian Networks. [PDF]

Sensors (Basel)
Wang B, Liu X.
europepmc   +1 more source