Results 191 to 200 of about 6,072 (305)

Resampling methods with multiply imputed data. [PDF]

Biometrika
Robbins MW, Burgette L.
europepmc   +1 more source

Artificial Intelligence‐Driven Insights into Electrospinning: Machine Learning Models to Predict Cotton‐Wool‐Like Structure of Electrospun Fibers

Advanced Intelligent Discovery, EarlyView.
Electrospinning allows the fabrication of fibrous 3D cotton‐wool‐like scaffolds for tissue engineering. Optimizing this process traditionally relies on trial‐and‐error approaches, and artificial intelligence (AI)‐based tools can support it, with the prediction of fiber properties. This work uses machine learning to classify and predict the structure of
Paolo D’Elia, Giovanna Pappalardo, Aldo R. Boccaccini, Liliana Liverani   +3 more
wiley   +1 more source

Revisiting the interpretation of axon diameter mapping using higher-order signal representations. [PDF]

Imaging Neurosci (Camb)
Karat BG, Wren-Jarvis J, Raven EP, Khan AR, Jones DK, Palombo M, Veraart J.   +6 more
europepmc   +1 more source

Factorization Machine‐Based Active Learning for Functional Materials Design with Optimal Initial Data

Advanced Intelligent Discovery, EarlyView.
This work investigates the optimal initial data size for surrogate‐based active learning in functional material optimization. Using factorization machine (FM)‐based quadratic unconstrained binary optimization (QUBO) surrogates and averaged piecewise linear regression, we show that adequate initial data accelerates convergence, enhances efficiency, and ...
Seongmin Kim, In‐Saeng Suh
wiley   +1 more source

Confidence Interval Construction for Causally Generalized Estimates With Target Sample Summary Information. [PDF]

Stat Med
Chen Y, Chen G, Yu M.
europepmc   +1 more source

Sampling Strategy: An Overlooked Factor Affecting Artificial Intelligence Prediction Accuracy of Peptides’ Physicochemical Properties

Advanced Intelligent Discovery, EarlyView.
This study reveals that sampling strategy (i.e., sampling size and approach) is a foundational prerequisite for building accurate and generalizable AI models in peptide discovery. Reaching a threshold of 7.5% of the total tetrapeptide sequence space was essential to ensure reliable predictions.
Meiru Yan, Ankeer Abuduhebaier, Haojin Zhou, Jiaqi Wang   +3 more
wiley   +1 more source

Neural heterogeneity enables adaptive encoding of time sequences. [PDF]

Commun Phys
Lafond-Mercier R, Maler L, Wallach A, Longtin A.   +3 more
europepmc   +1 more source

Large Language Model‐Based Chatbots in Higher Education

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
The use of large language models (LLMs) in higher education can facilitate personalized learning experiences, advance asynchronized learning, and support instructors, students, and researchers across diverse fields. The development of regulations and guidelines that address ethical and legal issues is essential to ensure safe and responsible adaptation
Defne Yigci, Merve Eryilmaz, Ail K. Yetisen, Savas Tasoglu, Aydogan Ozcan   +4 more
wiley   +1 more source

A two-stage random-effects estimator for meta-analyses of the value per statistical life. [PDF]

PLoS One
Newbold SC, Dockins C, Simon N, Maguire K, Sakib AM.   +4 more
europepmc   +1 more source

Real‐Time, Label‐Free Classification of Cell Death Pathways via Holotomography‐Based Deep Learning Framework

Advanced Intelligent Systems, EarlyView.
A 3D holotomography system coupled with a deep learning model distinguishes how cells die—apoptosis, necroptosis or necrosis—without any fluorescent labels. Training on refractive index maps of HeLa cells yields 97% accuracy and flags necroptosis hours before chemical dyes.
Minwook Kim, Wei Sun Park, Geon Kim, Sanggeun Oh, Jaephil Do, Juyeon Park, Jihwan Yu, Won Do Heo, YongKeun Park   +8 more
wiley   +1 more source