Multimodal Learning with Rashomon Analysis for Battery Discharge Capacity Prediction
Multimodal fusion integrates composition, crystal‐structure, and radial‐distribution descriptors to predict battery discharge capacity. Rashomon analysis across near‐optimal models reveals that explanatory variation is structured rather than arbitrary, separating stable mechanistic signals from model‐contingent attributions and providing a more ...
Jue Gong +4 more
wiley +1 more source
Modeling insurance claims using Bayesian nonparametric regression. [PDF]
Shams M, Ghosh K.
europepmc +1 more source
Lower Bounds on the Mean-Squared Error of Low-Rank Matrix Reconstruction
Gongguo Tang, A. Nehorai
semanticscholar +1 more source
Data‐Efficient Cycle‐Level Capacity Prediction Using 1D Deep Convolutional Network
We introduce DeepBat, a deep learning framework featuring a 1D convolutional backbone designed to extract latent degradation patterns from a microstructurally diverse electrode dataset. By learning complex formulation–performance relationships, the model accurately predicts long‐term specific discharge capacity using limited early‐cycle data, providing
Tao Huang +16 more
wiley +1 more source
Estimation of hemoglobin concentration at the initiation of cardiopulmonary bypass using support vector regression. [PDF]
Hirano H +3 more
europepmc +1 more source
A Hybrid Transfer Learning Framework for Brain Tumor Diagnosis
A novel hybrid transfer learning approach for brain tumor classification achieves 99.47% accuracy using magnetic resonance imaging (MRI) images. By combining image preprocessing, ensemble deep learning, and explainable artificial intelligence (XAI) techniques like gradient‐weighted class activation mapping and SHapley Additive exPlanations (SHAP), the ...
Sadia Islam Tonni +11 more
wiley +1 more source
A feasibility study on a machine-learning-based quality assurance tool for spot-scanning proton therapy using delivery log files and treatment plans. [PDF]
Yoo SK +9 more
europepmc +1 more source
Predicting Performance of Hall Effect Ion Source Using Machine Learning
This study introduces HallNN, a machine learning tool for predicting Hall effect ion source performance using a neural network ensemble trained on data generated from numerical simulations. HallNN provides faster and more accurate predictions than numerical methods and traditional scaling laws, making it valuable for designing and optimizing Hall ...
Jaehong Park +8 more
wiley +1 more source
An ANN-based non-destructive model for age and maturity estimation of the data-deficient Goldblotch Grouper (<i>Epinephelus costae</i>): a practical tool for data-poor fisheries in conflict-affected Mediterranean coasts. [PDF]
Hamwi NI.
europepmc +1 more source
Collaborative Visual Localization for Modular Self‐Reconfigurable Robots
Relative localization in modular self‐reconfigurable robots is challenged by hardware limitations, constrained fields of view, and sensor faults. This paper, based on the SnailBot platform, presents a vision‐based collaborative localization method that combines ArUco markers with learning‐based algorithms to enable robust pose estimation from ...
Guanqi Liang +4 more
wiley +1 more source

