Results 91 to 100 of about 246,254 (290)

A Workflow to Accelerate Microstructure‐Sensitive Fatigue Life Predictions

Advanced Engineering Materials, EarlyView.
This study introduces a workflow to accelerate predictions of microstructure‐sensitive fatigue life. Results from frameworks with varying levels of simplification are benchmarked against published reference results. The analysis reveals a trade‐off between accuracy and model complexity, offering researchers a practical guide for selecting the optimal ...
Luca Loiodice, Krzysztof S. Stopka, Michael D. Sangid   +2 more
wiley   +1 more source

The Role of Artificial Intelligence in Predicting Acute Kidney Injury: A Systematic Review of Machine Learning and Deep Learning Approaches [PDF]

مجله دانشگاه علوم پزشکی خراسان شمالی
Introduction: Acute kidney injury (AKI) is a common and potentially reversible condition in hospital settings and is associated with adverse outcomes, such as increased mortality, prolonged hospitalization, and higher healthcare costs.
Masoumeh Kheirkhahzadeh, Amin Golabpour
doaj  

Multimodal Data‐Driven Microstructure Characterization

Advanced Engineering Materials, EarlyView.
A self‐consistent autonomous workflow for EBSP‐based microstructure segmentation by integrating PCA, GMM clustering, and cNMF with information‐theoretic parameter selection, requiring no user input. An optimal ROI size related to characteristic grain size is identified.
Qi Zhang, Santiago Benito, Özge Özgün, Sebastian Weber, Markus Stricker   +4 more
wiley   +1 more source

Can a Transparent Machine Learning Algorithm Predict Better than Its Black Box Counterparts? A Benchmarking Study Using 110 Data Sets

Entropy
We developed a novel machine learning (ML) algorithm with the goal of producing transparent models (i.e., understandable by humans) while also flexibly accounting for nonlinearity and interactions.
Ryan A. Peterson, Max McGrath, Joseph E. Cavanaugh   +2 more
doaj   +1 more source

Dice in the Black Box: User Experiences with an Inscrutable Algorithm

CoRR, 2018
We demonstrate that users may be prone to place an inordinate amount of trust in black box algorithms that are framed as intelligent. We deploy an algorithm that purportedly assesses the positivity and negativity of a users' writing emotional writing. In actuality, the algorithm responds in a random fashion.
Aaron Springer, Victoria Hollis, Steve Whittaker 0001   +2 more
openaire   +3 more sources

Symbolic Regression and Multi‐Objective Optimization of the Flory–Huggins Interaction Parameter for Hydrogels

Advanced Engineering Materials, EarlyView.
We develop a data‐driven method to derive the mathematical expressions of the Flory–Huggins interaction parameter χ for the swelling behavior of temperature–responsive hydrogels. Starting from initial assumptions of χ, our workflow combines Bayesian optimization, Flory–Rehner theory, and symbolic regression to generate candidate χ expressions.
Yawen Wang, Adrian Ehrenhofer, Thomas Wallmersperger   +2 more
wiley   +1 more source

Current Status and Challenges in Data Collection for Aerospace Coatings Deposited by Plasma Spraying

Advanced Engineering Materials, EarlyView.
An innovative approach has been integrated into the GRENAT project to optimize plasma spraying and coating performance. Raw materials are accelerated and melted in the plasma generated by torches, creating coatings. Monitoring sensors collect process data which are combined with ex situ characterization data.
Lila Randriamananjara, Vincent Rat, Quentin Dubreuil, Patrice Valorge, Simon Goutier, Alain Denoirjean, Olivier Messe, Céline Ruelle, Jean‐Philippe Poli   +8 more
wiley   +1 more source

Explaining the Behavior of Black-Box Prediction Algorithms with Causal Learning

CoRR, 2020
Causal approaches to post-hoc explainability for black-box prediction models (e.g., deep neural networks trained on image pixel data) have become increasingly popular. However, existing approaches have two important shortcomings: (i) the "explanatory units" are micro-level inputs into the relevant prediction model, e.g., image pixels, rather than ...
Numair Sani, Daniel Malinsky, Ilya Shpitser   +2 more
openaire   +3 more sources

Molecular Dynamics Studies of Shape Memory Polymers: From Bead–Spring Models to Atomistic Simulations

Advanced Engineering Materials, EarlyView.
Coarse‐grained (left) and atomistic (right) models of the shape memory polymer ESTANE ETE 75DT3 are shown schematically. The two representations bridge molecular detail and mesoscopic description. Both models capture shape memory behavior, linking segmental mobility and conformational relaxation of anisotropic chains to macroscopic recovery, and ...
Fathollah Varnik
wiley   +1 more source

Transferring Black-Box Decision Making to a White-Box Model

Electronics
In the rapidly evolving realm of artificial intelligence (AI), black-box algorithms have exhibited outstanding performance. However, their opaque nature poses challenges in fields like medicine, where the clarity of the decision-making processes is ...
Bojan Žlahtič, J. Završnik, Helena Blažun Vošner, Peter Kokol   +3 more
semanticscholar   +1 more source